previously used keras version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import cv2\n",
    "import shutil\n",
    "import bcolz\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# os.chdir('/home/f/dogbreed')\n",
    "os.chdir('D:\\Machine Learning\\Kaggle\\Dog Breed Identification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "augment_size = 1\n",
    "learning_rate = 0.00003\n",
    "batch_size_top = 4096\n",
    "batch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('labels.csv')\n",
    "df_test = pd.read_csv('sample_submission.csv',index_col=0)\n",
    "\n",
    "targets_series = pd.Series(df_train['breed'])\n",
    "one_hot = pd.get_dummies(targets_series, sparse = True)\n",
    "one_hot_labels = np.asarray(one_hot)\n",
    "num_classes = one_hot_labels.shape[1]\n",
    "num_total_examples = one_hot_labels.shape[0]\n",
    "dummy_labels = np.argmax(one_hot_labels,axis=1)\n",
    "dummy_targets = np.asarray(targets_series)\n",
    "\n",
    "targets_to_idx_dict = {}\n",
    "idx_to_targets_dict = {}\n",
    "\n",
    "for n,i in zip(dummy_targets,dummy_labels):\n",
    "    targets_to_idx_dict[n] = i\n",
    "    idx_to_targets_dict[i] = n\n",
    "\n",
    "# for key in targets_to_idx_dict.keys():\n",
    "#     os.mkdir('train/{}'.format(key))\n",
    "    \n",
    "# os.mkdir('val')\n",
    "# for key in targets_to_idx_dict.keys():\n",
    "#     os.mkdir('val/{}'.format(key))\n",
    "\n",
    "# train_example_list = list(np.random.choice(range(num_total_examples),size=int(0.85*num_total_examples),replace=False))\n",
    "\n",
    "# val_example_list = list(set(range(num_total_examples)) - set(train_example_list))\n",
    "\n",
    "# for i in train_example_list:\n",
    "#     shutil.move('train/' + df_train.iloc[i]['id'] + '.jpg', 'train/{}'.format(df_train.iloc[i]['breed']))\n",
    "\n",
    "# for i in val_example_list:\n",
    "#     shutil.move('train/' + df_train.iloc[i]['id'] + '.jpg', 'val/{}'.format(df_train.iloc[i]['breed']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout, Flatten, BatchNormalization\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import Input\n",
    "from keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_crop(image):\n",
    "    \"\"\"Crops a random region of width x height from image.\n",
    "    Returns an image\"\"\"\n",
    "    ratio = 0.8\n",
    "    width = int(image.shape[1] * ratio)\n",
    "    height = int(image.shape[0] * ratio)\n",
    "    crop_origin_x = np.random.randint(0, image.shape[0] - width)\n",
    "    crop_origin_y = np.random.randint(0, image.shape[1] - height)\n",
    "    cropped_image = image[crop_origin_y:crop_origin_y+height,crop_origin_x:crop_origin_x+width,:]\n",
    "    cropped_image = cv2.resize(cropped_image,(image.shape[0],image.shape[1]))\n",
    "    return cropped_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8688 images belonging to 120 classes.\n",
      "Found 1534 images belonging to 120 classes.\n",
      "Found 8688 images belonging to 120 classes.\n"
     ]
    }
   ],
   "source": [
    "batch_size = 512\n",
    "datagen = ImageDataGenbatch_size = 512\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "datagen_aug = ImageDataGenerator(rescale=1./255,\n",
    "                                   preprocessing_function=random_crop,\n",
    "                                   rotation_range=45,\n",
    "                                   width_shift_range=0.1,\n",
    "                                   height_shift_range=0.1,\n",
    "                                   shear_range=0.1,\n",
    "                                   zoom_range=0.1,\n",
    "                                   horizontal_flip=True,\n",
    "                                   fill_mode='nearest')\n",
    "aug_generator = datagen_aug.flow_from_directory('train/',target_size=(360,360),batch_size=batch_size)\n",
    "val_generator = datagen.flow_from_directory('val/',target_size=(360,360),batch_size=batch_size)\n",
    "train_generator = datagen.flow_from_directory('train/',target_size=(360,360),batch_size=batch_size)\n",
    "# steps_per_epoch = len(train_generator)erator(rescale=1./255)\n",
    "# steps_per_epoch = len(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_img = Input(shape=(360,360,3))\n",
    "# model_Xception = keras.applications.Xception(include_top=True,weights='imagenet',input_shape=(360,360,3),input_tensor=input_img)\n",
    "model_Inception = keras.applications.InceptionV3(include_top=True,weights='imagenet',input_shape=(360,360,3),input_tensor=input_img)\n",
    "# extracted_features = model_Xception.layers[-1].input\n",
    "extracted_features = model_Inception.layers[-1].input\n",
    "extraction_model = Model(inputs=input_img,outputs=extracted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def features_extraction(gen,model,augment_size=1):\n",
    "    x_bcolz,y_bcolz = None,None\n",
    "    i = 0\n",
    "    for x,y in gen:\n",
    "        bottelneck_features = model.predict(x)\n",
    "        if not x_bcolz:\n",
    "            x_bcolz = bcolz.carray(bottelneck_features,bcolz.cparams(clevel=5),mode='w')\n",
    "            y_bcolz = bcolz.carray(y,bcolz.cparams(clevel=5),mode='w')\n",
    "        x_bcolz.append(bottelneck_features)\n",
    "        y_bcolz.append(y)\n",
    "        print('{} pieces generated'.format((i+1)*batch_size))\n",
    "        if x_bcolz.shape[0] >= len(gen)*augment_size*batch_size:\n",
    "            break\n",
    "        i += 1\n",
    "    return x_bcolz[:],y_bcolz[:]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "512 pieces generated\n",
      "1024 pieces generated\n",
      "1536 pieces generated\n",
      "2048 pieces generated\n",
      "2560 pieces generated\n",
      "3072 pieces generated\n",
      "3584 pieces generated\n",
      "4096 pieces generated\n",
      "4608 pieces generated\n",
      "5120 pieces generated\n",
      "5632 pieces generated\n",
      "6144 pieces generated\n",
      "6656 pieces generated\n",
      "7168 pieces generated\n",
      "7680 pieces generated\n",
      "8192 pieces generated\n"
     ]
    }
   ],
   "source": [
    "x_train,y_train = features_extraction(train_generator,extraction_model,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "512 pieces generated\n",
      "1024 pieces generated\n"
     ]
    }
   ],
   "source": [
    "x_val,y_val = features_extraction(val_generator,extraction_model,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_total = np.concatenate((x_train,x_val),axis=0)\n",
    "y_total = np.concatenate((y_train,y_val),axis=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('x_total_incep',x_total)\n",
    "np.save('y_total_incep',y_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_total = np.load('bottleneck_data\\\\x_total_incep.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_total = np.load('bottleneck_data\\\\y_total_incep.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_top_only_model(learning_rate):\n",
    "    input_features = Input(shape=(2048,))\n",
    "    out = Dense(units=120,activation='softmax',use_bias=True)(input_features)\n",
    "    model_top = Model(inputs=input_features,outputs=out)\n",
    "    model_top.compile(optimizer=keras.optimizers.Adam(lr=learning_rate),\n",
    "                  loss=keras.losses.categorical_crossentropy,\n",
    "                  metrics=[keras.metrics.categorical_accuracy])\n",
    "    return model_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing fold # 0\n",
      "processing fold # 1\n",
      "processing fold # 2\n",
      "processing fold # 3\n",
      "processing fold # 4\n"
     ]
    }
   ],
   "source": [
    "num_epochs_k_folds = 6000\n",
    "all_acc_histories = []\n",
    "all_loss_histories = []\n",
    "k = 5\n",
    "\n",
    "num_val_samples = len(x_total) // k\n",
    "all_scores = []\n",
    "for i in range(k):\n",
    "    print('processing fold #', i)\n",
    "    # Prepare the validation data: data from partition # k\n",
    "    val_data = x_total[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = y_total[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "\n",
    "    # Prepare the training data: data from all other partitions\n",
    "    partial_x_total = np.concatenate(\n",
    "        [x_total[:i * num_val_samples],\n",
    "         x_total[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    partial_y_total = np.concatenate(\n",
    "        [y_total[:i * num_val_samples],\n",
    "         y_total[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "\n",
    "    # Build the Keras model (already compiled)\n",
    "    model = build_top_only_model(learning_rate)\n",
    "    # Train the model (in silent mode, verbose=0)\n",
    "    his = model.fit(partial_x_total, partial_y_total,epochs=num_epochs_k_folds,validation_data=(val_data, val_targets), batch_size=batch_size_top, verbose=0)\n",
    "    val_acc_history = his.history['val_categorical_accuracy']\n",
    "    all_acc_histories.append(val_acc_history)\n",
    "    val_loss_history = his.history['val_loss']\n",
    "    all_loss_histories.append(val_loss_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_acc_history = [np.mean([x[i] for x in all_acc_histories]) for i in range(num_epochs_k_folds)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "average_loss_history = [np.mean([x[i] for x in all_loss_histories]) for i in range(num_epochs_k_folds)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jgVs6UwuIiA9GxKqIWEvy/f+niHgTc6weAJLmS1pYWyb5XtzFHPx+RcSjwEOS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda26d850f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(average_acc_history) + 1), average_acc_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation ACC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cvt8C7GFutOUC4N1mthW4laBb5wYqsy24e2/43AfcTvBlXGm/Yy8DL7v7Y+Hr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\nlVDgi4hUCQW+iEiVUOCLiFSJ/w/wYEcWn6phtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda27186b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(average_loss_history) + 1), average_loss_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation LOSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def smooth_curve(points, factor=0.9):\n",
    "  smoothed_points = []\n",
    "  for point in points:\n",
    "    if smoothed_points:\n",
    "      previous = smoothed_points[-1]\n",
    "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
    "    else:\n",
    "      smoothed_points.append(point)\n",
    "  return smoothed_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wPatnLbAOuKUzrYCI+HBErIqINSTf/x9FxJuYY+2okjRf0sLqMsl3427m2Hcs\nInYCj0g6NS16KXBPV9vRjQmibvyQzNrfRzIe/JFuxzNNjF8HHgUmSDL/xSTjuDcC96e/l6V1BVyZ\ntucuYH3ddv4M2Jb+vL0L7XgBSdf1TuD29OeVc7QtpwO3pW25G7gsLT+ZZGe4DfgG0J+WD6Tr29LX\nT67b1kfSNm4FXtHF79mLmDz7aE62I437jvRnS/X/9Bz9jj0H2JR+x75NcvZQ19rhK5rNzKwmL8NH\nZmbWBicFMzOrcVIwM7MaJwUzM6txUjAzsxonBbOUpHJ6x83qz1G7m66kNaq7+63ZbNUzcxWz3BiJ\n5HYWZrnlnoLZDNL79l+h5LkKt0h6Rlp+kqQb0/va3yjpxLR8paRvKXkGwx2S/ijdVFHS55Q8l+EH\n6RXSSHqvpHvS7VzbpWaaAU4KZvUGG4aP/rTutQMRcRbwaZJ7BpEufzkiTgeuAT6Vln8K+ElEPJvk\nPjZb0vJ1wJUR8a+AfcDr0vJLgTPS7fyHrBpn1g5f0WyWknQwIhY0KX8IeElEPJDe6G9nRBwj6TGS\ne95PpOWPRsRySbuBVRExVreNNcAPI2Jduv6fgd6I+BtJ3wcOktzi4NsRcTDjpppNyz0Fs/bENMvT\n1WlmrG65zOSc3qtI7mfzXGBz3V1LzTrOScGsPX9a9/sX6fLPSe44CvAm4Kfp8o3Au6D2gJ5F021U\nUgFYHRE3kTwAZwlwSG/FrFN8RGI2aTB9wlrV9yOielpqv6SbSQ6kLkrL3gt8QdKHSJ6e9fa0/H3A\n1ZIuJukRvIvk7rfNFIGvSlpMcgfMT0Ty3AazrvCcgtkM0jmF9RHxWLdjMcuah4/MzKzGPQUzM6tx\nT8HMzGqcFMzMrMZJwczMapwUzMysxknBzMxqnBTMzKzm/wPX+CVQMoCjMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda27273d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smooth_acc_history = smooth_curve(average_acc_history[10:])\n",
    "\n",
    "plt.plot(range(1, len(smooth_acc_history) + 1), smooth_acc_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation ACC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JVDVH2v4d8GLgcXc/keDqV7+PtKoaesGi4KcIDeuISNJVE/jj7r4byJhZxt1vIQHn0ikp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\n8EVEUkKBLyKSEgp8EZGU+P8gWe/YYzo7lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda27284be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smooth_loss_history = smooth_curve(average_loss_history[10:])\n",
    "\n",
    "plt.plot(range(1, len(smooth_loss_history) + 1), smooth_loss_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation LOSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3444"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(np.array(average_loss_history))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_top = build_top_only_model(learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.8606 - categorical_accuracy: 0.00 - ETA: 0s - loss: 4.8628 - categorical_accuracy: 0.00 - 1s 70us/step - loss: 4.8615 - categorical_accuracy: 0.0088\n",
      "Epoch 2/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.8407 - categorical_accuracy: 0.00 - ETA: 0s - loss: 4.8429 - categorical_accuracy: 0.00 - 0s 48us/step - loss: 4.8407 - categorical_accuracy: 0.0092\n",
      "Epoch 3/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.8236 - categorical_accuracy: 0.00 - ETA: 0s - loss: 4.8211 - categorical_accuracy: 0.00 - 0s 48us/step - loss: 4.8203 - categorical_accuracy: 0.0099\n",
      "Epoch 4/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.8050 - categorical_accuracy: 0.01 - ETA: 0s - loss: 4.8035 - categorical_accuracy: 0.01 - 1s 50us/step - loss: 4.8002 - categorical_accuracy: 0.0109\n",
      "Epoch 5/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.7933 - categorical_accuracy: 0.01 - ETA: 0s - loss: 4.7826 - categorical_accuracy: 0.01 - 1s 49us/step - loss: 4.7804 - categorical_accuracy: 0.0120\n",
      "Epoch 6/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.7633 - categorical_accuracy: 0.01 - ETA: 0s - loss: 4.7604 - categorical_accuracy: 0.01 - 0s 49us/step - loss: 4.7607 - categorical_accuracy: 0.0130\n",
      "Epoch 7/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.7538 - categorical_accuracy: 0.01 - ETA: 0s - loss: 4.7471 - categorical_accuracy: 0.01 - 1s 49us/step - loss: 4.7412 - categorical_accuracy: 0.0148\n",
      "Epoch 8/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.7225 - categorical_accuracy: 0.01 - ETA: 0s - loss: 4.7218 - categorical_accuracy: 0.01 - 1s 55us/step - loss: 4.7218 - categorical_accuracy: 0.0170\n",
      "Epoch 9/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.7129 - categorical_accuracy: 0.01 - ETA: 0s - loss: 4.7061 - categorical_accuracy: 0.01 - 1s 50us/step - loss: 4.7026 - categorical_accuracy: 0.0187\n",
      "Epoch 10/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.6823 - categorical_accuracy: 0.02 - ETA: 0s - loss: 4.6814 - categorical_accuracy: 0.02 - 1s 49us/step - loss: 4.6835 - categorical_accuracy: 0.0223\n",
      "Epoch 11/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.6712 - categorical_accuracy: 0.02 - ETA: 0s - loss: 4.6678 - categorical_accuracy: 0.02 - 1s 50us/step - loss: 4.6646 - categorical_accuracy: 0.0263\n",
      "Epoch 12/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.6500 - categorical_accuracy: 0.03 - ETA: 0s - loss: 4.6497 - categorical_accuracy: 0.02 - 1s 55us/step - loss: 4.6458 - categorical_accuracy: 0.0309\n",
      "Epoch 13/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.6293 - categorical_accuracy: 0.03 - ETA: 0s - loss: 4.6284 - categorical_accuracy: 0.03 - 1s 49us/step - loss: 4.6271 - categorical_accuracy: 0.0350\n",
      "Epoch 14/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.6116 - categorical_accuracy: 0.03 - ETA: 0s - loss: 4.6082 - categorical_accuracy: 0.03 - 1s 50us/step - loss: 4.6085 - categorical_accuracy: 0.0401\n",
      "Epoch 15/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.5937 - categorical_accuracy: 0.04 - ETA: 0s - loss: 4.5921 - categorical_accuracy: 0.04 - 1s 50us/step - loss: 4.5901 - categorical_accuracy: 0.0447\n",
      "Epoch 16/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.5766 - categorical_accuracy: 0.04 - ETA: 0s - loss: 4.5745 - categorical_accuracy: 0.04 - 1s 51us/step - loss: 4.5717 - categorical_accuracy: 0.0504\n",
      "Epoch 17/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.5624 - categorical_accuracy: 0.05 - ETA: 0s - loss: 4.5562 - categorical_accuracy: 0.05 - 1s 50us/step - loss: 4.5534 - categorical_accuracy: 0.0558\n",
      "Epoch 18/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.5505 - categorical_accuracy: 0.05 - ETA: 0s - loss: 4.5377 - categorical_accuracy: 0.06 - 1s 53us/step - loss: 4.5352 - categorical_accuracy: 0.0614\n",
      "Epoch 19/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.5183 - categorical_accuracy: 0.07 - ETA: 0s - loss: 4.5190 - categorical_accuracy: 0.06 - 1s 51us/step - loss: 4.5171 - categorical_accuracy: 0.0685\n",
      "Epoch 20/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.5163 - categorical_accuracy: 0.07 - ETA: 0s - loss: 4.5044 - categorical_accuracy: 0.07 - 1s 51us/step - loss: 4.4990 - categorical_accuracy: 0.0768\n",
      "Epoch 21/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.4889 - categorical_accuracy: 0.07 - ETA: 0s - loss: 4.4826 - categorical_accuracy: 0.08 - 1s 52us/step - loss: 4.4810 - categorical_accuracy: 0.0837\n",
      "Epoch 22/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.4695 - categorical_accuracy: 0.08 - ETA: 0s - loss: 4.4629 - categorical_accuracy: 0.09 - 1s 52us/step - loss: 4.4630 - categorical_accuracy: 0.0924\n",
      "Epoch 23/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.4543 - categorical_accuracy: 0.09 - ETA: 0s - loss: 4.4470 - categorical_accuracy: 0.09 - 1s 50us/step - loss: 4.4451 - categorical_accuracy: 0.1020\n",
      "Epoch 24/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.4289 - categorical_accuracy: 0.11 - ETA: 0s - loss: 4.4318 - categorical_accuracy: 0.11 - 1s 51us/step - loss: 4.4273 - categorical_accuracy: 0.1111\n",
      "Epoch 25/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.4140 - categorical_accuracy: 0.11 - ETA: 0s - loss: 4.4121 - categorical_accuracy: 0.11 - 1s 54us/step - loss: 4.4095 - categorical_accuracy: 0.1203\n",
      "Epoch 26/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.3991 - categorical_accuracy: 0.12 - ETA: 0s - loss: 4.3925 - categorical_accuracy: 0.13 - 1s 50us/step - loss: 4.3917 - categorical_accuracy: 0.1311\n",
      "Epoch 27/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.3839 - categorical_accuracy: 0.13 - ETA: 0s - loss: 4.3758 - categorical_accuracy: 0.13 - 1s 54us/step - loss: 4.3740 - categorical_accuracy: 0.1421\n",
      "Epoch 28/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.3631 - categorical_accuracy: 0.15 - ETA: 0s - loss: 4.3565 - categorical_accuracy: 0.15 - 1s 50us/step - loss: 4.3563 - categorical_accuracy: 0.1544\n",
      "Epoch 29/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.3401 - categorical_accuracy: 0.16 - ETA: 0s - loss: 4.3414 - categorical_accuracy: 0.16 - 1s 51us/step - loss: 4.3387 - categorical_accuracy: 0.1666\n",
      "Epoch 30/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.3311 - categorical_accuracy: 0.17 - ETA: 0s - loss: 4.3238 - categorical_accuracy: 0.17 - 1s 49us/step - loss: 4.3211 - categorical_accuracy: 0.1782\n",
      "Epoch 31/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.3061 - categorical_accuracy: 0.18 - ETA: 0s - loss: 4.3068 - categorical_accuracy: 0.19 - 1s 50us/step - loss: 4.3035 - categorical_accuracy: 0.1896\n",
      "Epoch 32/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.2907 - categorical_accuracy: 0.19 - ETA: 0s - loss: 4.2899 - categorical_accuracy: 0.19 - 1s 54us/step - loss: 4.2860 - categorical_accuracy: 0.2018\n",
      "Epoch 33/2000\n",
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      "Epoch 34/2000\n",
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      "Epoch 35/2000\n",
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      "Epoch 36/2000\n",
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     ]
    },
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      "Epoch 37/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.1998 - categorical_accuracy: 0.25 - ETA: 0s - loss: 4.1982 - categorical_accuracy: 0.25 - 1s 53us/step - loss: 4.1989 - categorical_accuracy: 0.2594\n",
      "Epoch 38/2000\n",
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      "Epoch 39/2000\n",
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      "Epoch 40/2000\n",
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      "Epoch 41/2000\n",
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      "Epoch 42/2000\n",
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      "Epoch 43/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 4.0928 - categorical_accuracy: 0.34 - ETA: 0s - loss: 4.0932 - categorical_accuracy: 0.34 - 0s 47us/step - loss: 4.0953 - categorical_accuracy: 0.3376\n",
      "Epoch 44/2000\n",
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      "Epoch 45/2000\n",
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      "Epoch 46/2000\n",
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      "Epoch 47/2000\n",
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      "Epoch 48/2000\n",
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      "Epoch 49/2000\n",
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      "Epoch 50/2000\n",
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      "Epoch 51/2000\n",
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      "Epoch 52/2000\n",
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      "Epoch 53/2000\n",
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      "Epoch 54/2000\n",
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      "Epoch 55/2000\n",
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      "Epoch 56/2000\n",
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      "Epoch 64/2000\n",
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      "Epoch 65/2000\n",
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      "Epoch 66/2000\n",
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      "Epoch 67/2000\n",
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      "Epoch 72/2000\n",
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     ]
    },
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      "Epoch 73/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 3.5936 - categorical_accuracy: 0.63 - ETA: 0s - loss: 3.5916 - categorical_accuracy: 0.64 - 1s 49us/step - loss: 3.5940 - categorical_accuracy: 0.6425\n",
      "Epoch 74/2000\n",
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      "Epoch 75/2000\n",
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      "Epoch 76/2000\n",
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      "Epoch 77/2000\n",
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      "Epoch 79/2000\n",
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      "Epoch 80/2000\n",
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      "Epoch 81/2000\n",
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      "Epoch 82/2000\n",
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      "Epoch 84/2000\n",
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      "Epoch 86/2000\n",
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      "Epoch 87/2000\n",
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      "Epoch 88/2000\n",
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      "Epoch 89/2000\n",
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      "Epoch 91/2000\n",
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      "Epoch 92/2000\n",
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      "Epoch 94/2000\n",
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      "Epoch 100/2000\n",
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      "Epoch 102/2000\n",
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     ]
    },
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      "Epoch 109/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 3.0391 - categorical_accuracy: 0.78 - ETA: 0s - loss: 3.0403 - categorical_accuracy: 0.78 - 0s 47us/step - loss: 3.0332 - categorical_accuracy: 0.7854\n",
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      "Epoch 111/2000\n",
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      "Epoch 115/2000\n",
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      "Epoch 116/2000\n",
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      "Epoch 139/2000\n",
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      "Epoch 144/2000\n",
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     ]
    },
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     "text": [
      "Epoch 145/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 2.5355 - categorical_accuracy: 0.83 - ETA: 0s - loss: 2.5322 - categorical_accuracy: 0.84 - 0s 48us/step - loss: 2.5274 - categorical_accuracy: 0.8448\n",
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     ]
    },
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      "Epoch 181/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 2.0930 - categorical_accuracy: 0.87 - ETA: 0s - loss: 2.0916 - categorical_accuracy: 0.87 - 0s 48us/step - loss: 2.0880 - categorical_accuracy: 0.8751\n",
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      "Epoch 202/2000\n",
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      "Epoch 203/2000\n",
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      "Epoch 204/2000\n",
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      "Epoch 205/2000\n",
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      "Epoch 206/2000\n",
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      "Epoch 207/2000\n",
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      "Epoch 208/2000\n",
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      "Epoch 209/2000\n",
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      "Epoch 210/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.7997 - categorical_accuracy: 0.88 - ETA: 0s - loss: 1.7922 - categorical_accuracy: 0.88 - 1s 50us/step - loss: 1.7871 - categorical_accuracy: 0.8897\n",
      "Epoch 211/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.7728 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.7792 - categorical_accuracy: 0.89 - 1s 50us/step - loss: 1.7776 - categorical_accuracy: 0.8901\n",
      "Epoch 212/2000\n",
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      "Epoch 213/2000\n",
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      "Epoch 214/2000\n",
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      "Epoch 215/2000\n",
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      "Epoch 216/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.7426 - categorical_accuracy: 0.88 - ETA: 0s - loss: 1.7392 - categorical_accuracy: 0.88 - 1s 52us/step - loss: 1.7308 - categorical_accuracy: 0.8924\n"
     ]
    },
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     "text": [
      "Epoch 217/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.7239 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.7232 - categorical_accuracy: 0.89 - 0s 49us/step - loss: 1.7216 - categorical_accuracy: 0.8927\n",
      "Epoch 218/2000\n",
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      "Epoch 219/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6916 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.7040 - categorical_accuracy: 0.89 - 0s 48us/step - loss: 1.7034 - categorical_accuracy: 0.8935\n",
      "Epoch 220/2000\n",
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      "Epoch 221/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6861 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.6861 - categorical_accuracy: 0.89 - 0s 48us/step - loss: 1.6854 - categorical_accuracy: 0.8938\n",
      "Epoch 222/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6721 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.6778 - categorical_accuracy: 0.89 - 0s 49us/step - loss: 1.6765 - categorical_accuracy: 0.8946\n",
      "Epoch 223/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6718 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.6731 - categorical_accuracy: 0.89 - 0s 48us/step - loss: 1.6677 - categorical_accuracy: 0.8946\n",
      "Epoch 224/2000\n",
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      "Epoch 225/2000\n",
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      "Epoch 226/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6430 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.6468 - categorical_accuracy: 0.89 - 0s 48us/step - loss: 1.6414 - categorical_accuracy: 0.8958\n",
      "Epoch 227/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6421 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.6320 - categorical_accuracy: 0.89 - 0s 48us/step - loss: 1.6328 - categorical_accuracy: 0.8961\n",
      "Epoch 228/2000\n",
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      "Epoch 229/2000\n",
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      "Epoch 230/2000\n",
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      "Epoch 231/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.6019 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.5991 - categorical_accuracy: 0.89 - 0s 49us/step - loss: 1.5988 - categorical_accuracy: 0.8972\n",
      "Epoch 232/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5902 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.5866 - categorical_accuracy: 0.89 - 1s 49us/step - loss: 1.5905 - categorical_accuracy: 0.8973\n",
      "Epoch 233/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5911 - categorical_accuracy: 0.88 - ETA: 0s - loss: 1.5874 - categorical_accuracy: 0.89 - 0s 49us/step - loss: 1.5821 - categorical_accuracy: 0.8978\n",
      "Epoch 234/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5766 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.5794 - categorical_accuracy: 0.89 - 0s 48us/step - loss: 1.5739 - categorical_accuracy: 0.8985\n",
      "Epoch 235/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5653 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.5660 - categorical_accuracy: 0.89 - 0s 49us/step - loss: 1.5657 - categorical_accuracy: 0.8989\n",
      "Epoch 236/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5574 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.5572 - categorical_accuracy: 0.89 - 1s 49us/step - loss: 1.5575 - categorical_accuracy: 0.8990\n",
      "Epoch 237/2000\n",
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      "Epoch 238/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5378 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.5426 - categorical_accuracy: 0.89 - 1s 49us/step - loss: 1.5414 - categorical_accuracy: 0.9000\n",
      "Epoch 239/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.5299 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.5342 - categorical_accuracy: 0.90 - 1s 50us/step - loss: 1.5334 - categorical_accuracy: 0.9002\n",
      "Epoch 240/2000\n",
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      "Epoch 241/2000\n",
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      "Epoch 242/2000\n",
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      "Epoch 243/2000\n",
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      "Epoch 244/2000\n",
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      "Epoch 245/2000\n",
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      "Epoch 246/2000\n",
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      "Epoch 247/2000\n",
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      "Epoch 248/2000\n",
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      "Epoch 249/2000\n",
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      "Epoch 250/2000\n",
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      "Epoch 252/2000\n",
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     ]
    },
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     "text": [
      "Epoch 253/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.4332 - categorical_accuracy: 0.89 - ETA: 0s - loss: 1.4313 - categorical_accuracy: 0.90 - 0s 49us/step - loss: 1.4269 - categorical_accuracy: 0.9051\n",
      "Epoch 254/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.4178 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.4153 - categorical_accuracy: 0.90 - 0s 48us/step - loss: 1.4197 - categorical_accuracy: 0.9052\n",
      "Epoch 255/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.4115 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.4086 - categorical_accuracy: 0.90 - 0s 47us/step - loss: 1.4125 - categorical_accuracy: 0.9058\n",
      "Epoch 256/2000\n",
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      "Epoch 257/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.3973 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.3999 - categorical_accuracy: 0.90 - 0s 47us/step - loss: 1.3982 - categorical_accuracy: 0.9058\n",
      "Epoch 258/2000\n",
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      "Epoch 259/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.3877 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.3808 - categorical_accuracy: 0.90 - 1s 49us/step - loss: 1.3842 - categorical_accuracy: 0.9064\n",
      "Epoch 260/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.3841 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.3802 - categorical_accuracy: 0.90 - 0s 48us/step - loss: 1.3773 - categorical_accuracy: 0.9068\n",
      "Epoch 261/2000\n",
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      "Epoch 262/2000\n",
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      "Epoch 263/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.3568 - categorical_accuracy: 0.90 - ETA: 0s - loss: 1.3599 - categorical_accuracy: 0.90 - 0s 48us/step - loss: 1.3568 - categorical_accuracy: 0.9075\n",
      "Epoch 264/2000\n",
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      "Epoch 265/2000\n",
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      "Epoch 266/2000\n",
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      "Epoch 267/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.3296 - categorical_accuracy: 0.91 - ETA: 0s - loss: 1.3320 - categorical_accuracy: 0.90 - 1s 49us/step - loss: 1.3300 - categorical_accuracy: 0.9085\n",
      "Epoch 268/2000\n",
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      "Epoch 269/2000\n",
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      "Epoch 270/2000\n",
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      "Epoch 271/2000\n",
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      "Epoch 272/2000\n",
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      "Epoch 274/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 1.2337 - categorical_accuracy: 0.91 - ETA: 0s - loss: 1.2259 - categorical_accuracy: 0.91 - 1s 51us/step - loss: 1.2245 - categorical_accuracy: 0.9128\n",
      "Epoch 285/2000\n",
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      "Epoch 286/2000\n",
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      "Epoch 287/2000\n",
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      "Epoch 288/2000\n",
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     ]
    },
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      "Epoch 289/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.1912 - categorical_accuracy: 0.91 - ETA: 0s - loss: 1.1995 - categorical_accuracy: 0.91 - 0s 49us/step - loss: 1.1957 - categorical_accuracy: 0.9137\n",
      "Epoch 290/2000\n",
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      "Epoch 291/2000\n",
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      "Epoch 292/2000\n",
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      "Epoch 293/2000\n",
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      "Epoch 294/2000\n",
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      "Epoch 295/2000\n",
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      "Epoch 296/2000\n",
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      "Epoch 297/2000\n",
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      "Epoch 298/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.1514 - categorical_accuracy: 0.91 - ETA: 0s - loss: 1.1515 - categorical_accuracy: 0.91 - 0s 48us/step - loss: 1.1466 - categorical_accuracy: 0.9148\n",
      "Epoch 299/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.1515 - categorical_accuracy: 0.91 - ETA: 0s - loss: 1.1459 - categorical_accuracy: 0.91 - 0s 48us/step - loss: 1.1413 - categorical_accuracy: 0.9149\n",
      "Epoch 300/2000\n",
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      "Epoch 302/2000\n",
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      "Epoch 303/2000\n",
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      "Epoch 304/2000\n",
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      "Epoch 305/2000\n",
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      "Epoch 307/2000\n",
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      "Epoch 319/2000\n",
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     ]
    },
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      "Epoch 325/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.0185 - categorical_accuracy: 0.91 - ETA: 0s - loss: 1.0161 - categorical_accuracy: 0.91 - 0s 49us/step - loss: 1.0166 - categorical_accuracy: 0.9178\n",
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      "Epoch 327/2000\n",
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      "Epoch 328/2000\n",
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      "Epoch 329/2000\n",
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      "Epoch 331/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 1.0006 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.9895 - categorical_accuracy: 0.91 - 1s 49us/step - loss: 0.9909 - categorical_accuracy: 0.9183\n",
      "Epoch 332/2000\n",
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      "Epoch 338/2000\n",
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      "Epoch 339/2000\n",
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      "Epoch 340/2000\n",
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      "Epoch 341/2000\n",
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      "Epoch 346/2000\n",
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      "Epoch 360/2000\n",
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     ]
    },
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      "Epoch 361/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8807 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8775 - categorical_accuracy: 0.92 - 0s 49us/step - loss: 0.8777 - categorical_accuracy: 0.9215\n",
      "Epoch 362/2000\n",
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      "Epoch 363/2000\n",
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      "Epoch 365/2000\n",
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      "Epoch 366/2000\n",
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      "Epoch 367/2000\n",
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      "Epoch 368/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8548 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8531 - categorical_accuracy: 0.92 - 0s 47us/step - loss: 0.8545 - categorical_accuracy: 0.9219\n",
      "Epoch 369/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8547 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.8500 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.8513 - categorical_accuracy: 0.9219\n",
      "Epoch 370/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8586 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.8486 - categorical_accuracy: 0.92 - 1s 52us/step - loss: 0.8481 - categorical_accuracy: 0.9220\n",
      "Epoch 371/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8471 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.8438 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.8449 - categorical_accuracy: 0.9221\n",
      "Epoch 372/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8341 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8357 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.8417 - categorical_accuracy: 0.9219\n",
      "Epoch 373/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8356 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8424 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.8386 - categorical_accuracy: 0.9219\n",
      "Epoch 374/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8435 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.8330 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.8354 - categorical_accuracy: 0.9220\n",
      "Epoch 375/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8337 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8332 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.8323 - categorical_accuracy: 0.9221\n",
      "Epoch 376/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8274 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8286 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.8293 - categorical_accuracy: 0.9221\n",
      "Epoch 377/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8265 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8262 - categorical_accuracy: 0.92 - 1s 51us/step - loss: 0.8262 - categorical_accuracy: 0.9222\n",
      "Epoch 378/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8169 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8241 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.8232 - categorical_accuracy: 0.9222\n",
      "Epoch 379/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8288 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8225 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.8202 - categorical_accuracy: 0.9224\n",
      "Epoch 380/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8128 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8143 - categorical_accuracy: 0.92 - 0s 49us/step - loss: 0.8172 - categorical_accuracy: 0.9227\n",
      "Epoch 381/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8159 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8096 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.8142 - categorical_accuracy: 0.9226\n",
      "Epoch 382/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8152 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.8122 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.8112 - categorical_accuracy: 0.9226\n",
      "Epoch 383/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8153 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.8083 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.8083 - categorical_accuracy: 0.9227\n",
      "Epoch 384/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8000 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8042 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.8054 - categorical_accuracy: 0.9229\n",
      "Epoch 385/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7952 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8035 - categorical_accuracy: 0.92 - 1s 51us/step - loss: 0.8025 - categorical_accuracy: 0.9229\n",
      "Epoch 386/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7963 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.8009 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.7996 - categorical_accuracy: 0.9231\n",
      "Epoch 387/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8012 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7998 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.7967 - categorical_accuracy: 0.9230\n",
      "Epoch 388/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.8016 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7950 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.7939 - categorical_accuracy: 0.9231\n",
      "Epoch 389/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7963 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7917 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.7911 - categorical_accuracy: 0.9232\n",
      "Epoch 390/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7894 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7881 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.7883 - categorical_accuracy: 0.9231\n",
      "Epoch 391/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7952 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.7891 - categorical_accuracy: 0.92 - 1s 51us/step - loss: 0.7855 - categorical_accuracy: 0.9231\n",
      "Epoch 392/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7742 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7864 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.7827 - categorical_accuracy: 0.9233\n",
      "Epoch 393/2000\n",
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      "Epoch 394/2000\n",
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      "Epoch 395/2000\n",
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      "Epoch 396/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7734 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7738 - categorical_accuracy: 0.92 - 1s 51us/step - loss: 0.7718 - categorical_accuracy: 0.9235\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 397/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7727 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7673 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7691 - categorical_accuracy: 0.9235\n",
      "Epoch 398/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7810 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.7728 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7665 - categorical_accuracy: 0.9234\n",
      "Epoch 399/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7623 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7658 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7638 - categorical_accuracy: 0.9233\n",
      "Epoch 400/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7585 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7606 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7612 - categorical_accuracy: 0.9232\n",
      "Epoch 401/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7648 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.7575 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7586 - categorical_accuracy: 0.9232\n",
      "Epoch 402/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7676 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.7581 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7560 - categorical_accuracy: 0.9233\n",
      "Epoch 403/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7486 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7519 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7534 - categorical_accuracy: 0.9234\n",
      "Epoch 404/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7458 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7491 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7509 - categorical_accuracy: 0.9234\n",
      "Epoch 405/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7537 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7513 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7483 - categorical_accuracy: 0.9236\n",
      "Epoch 406/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7375 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7440 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7458 - categorical_accuracy: 0.9237\n",
      "Epoch 407/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7527 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7483 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7433 - categorical_accuracy: 0.9240\n",
      "Epoch 408/2000\n",
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      "Epoch 409/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7289 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7335 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.7383 - categorical_accuracy: 0.9240\n",
      "Epoch 410/2000\n",
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      "Epoch 411/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7441 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.7360 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.7334 - categorical_accuracy: 0.9243\n",
      "Epoch 412/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7361 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7370 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.7309 - categorical_accuracy: 0.9243\n",
      "Epoch 413/2000\n",
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      "Epoch 414/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7316 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7257 - categorical_accuracy: 0.92 - 0s 49us/step - loss: 0.7261 - categorical_accuracy: 0.9243\n",
      "Epoch 415/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7220 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.7251 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.7237 - categorical_accuracy: 0.9243\n",
      "Epoch 416/2000\n",
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      "Epoch 417/2000\n",
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      "Epoch 418/2000\n",
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      "Epoch 419/2000\n",
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      "Epoch 420/2000\n",
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      "Epoch 421/2000\n",
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      "Epoch 422/2000\n",
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      "Epoch 423/2000\n",
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      "Epoch 424/2000\n",
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      "Epoch 425/2000\n",
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      "Epoch 426/2000\n",
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      "Epoch 427/2000\n",
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      "Epoch 428/2000\n",
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      "Epoch 429/2000\n",
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      "Epoch 430/2000\n",
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      "Epoch 432/2000\n",
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     ]
    },
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     "text": [
      "Epoch 433/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.7018 - categorical_accuracy: 0.91 - ETA: 0s - loss: 0.6871 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.6832 - categorical_accuracy: 0.9264\n",
      "Epoch 434/2000\n",
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      "Epoch 435/2000\n",
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      "Epoch 436/2000\n",
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      "Epoch 437/2000\n",
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      "Epoch 438/2000\n",
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      "Epoch 439/2000\n",
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      "Epoch 440/2000\n",
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      "Epoch 441/2000\n",
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      "Epoch 444/2000\n",
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      "Epoch 446/2000\n",
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      "Epoch 448/2000\n",
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      "Epoch 452/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6427 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6426 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6449 - categorical_accuracy: 0.9279\n",
      "Epoch 453/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6423 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6445 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6430 - categorical_accuracy: 0.9282\n",
      "Epoch 454/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6394 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6433 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.6411 - categorical_accuracy: 0.9284\n",
      "Epoch 455/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6378 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6384 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6392 - categorical_accuracy: 0.9284\n",
      "Epoch 456/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6402 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6396 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6374 - categorical_accuracy: 0.9285\n",
      "Epoch 457/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6366 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6355 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.6355 - categorical_accuracy: 0.9285\n",
      "Epoch 458/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6299 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6306 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6337 - categorical_accuracy: 0.9284\n",
      "Epoch 459/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6276 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6313 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.6318 - categorical_accuracy: 0.9285\n",
      "Epoch 460/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6434 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6308 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6300 - categorical_accuracy: 0.9286\n",
      "Epoch 461/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6289 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6259 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.6282 - categorical_accuracy: 0.9286\n",
      "Epoch 462/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6259 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6240 - categorical_accuracy: 0.92 - 1s 51us/step - loss: 0.6264 - categorical_accuracy: 0.9288\n",
      "Epoch 463/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6172 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6205 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.6246 - categorical_accuracy: 0.9286\n",
      "Epoch 464/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6198 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6261 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.6228 - categorical_accuracy: 0.9290\n",
      "Epoch 465/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6178 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6207 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.6211 - categorical_accuracy: 0.9290\n",
      "Epoch 466/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6258 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6162 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6193 - categorical_accuracy: 0.9293\n",
      "Epoch 467/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6296 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6163 - categorical_accuracy: 0.93 - 1s 53us/step - loss: 0.6175 - categorical_accuracy: 0.9293\n",
      "Epoch 468/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6152 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6148 - categorical_accuracy: 0.92 - 1s 56us/step - loss: 0.6158 - categorical_accuracy: 0.9298\n"
     ]
    },
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     "text": [
      "Epoch 469/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6203 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6191 - categorical_accuracy: 0.92 - 1s 54us/step - loss: 0.6141 - categorical_accuracy: 0.9298\n",
      "Epoch 470/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6019 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6087 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.6123 - categorical_accuracy: 0.9300\n",
      "Epoch 471/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6173 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6124 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.6106 - categorical_accuracy: 0.9300\n",
      "Epoch 472/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6137 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6112 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.6089 - categorical_accuracy: 0.9301\n",
      "Epoch 473/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6053 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6088 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.6072 - categorical_accuracy: 0.9301\n",
      "Epoch 474/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6074 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6101 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6055 - categorical_accuracy: 0.9301\n",
      "Epoch 475/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5886 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.6000 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.6039 - categorical_accuracy: 0.9300\n",
      "Epoch 476/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6073 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.6024 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.6022 - categorical_accuracy: 0.9301\n",
      "Epoch 477/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5997 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5992 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.6005 - categorical_accuracy: 0.9303\n",
      "Epoch 478/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.6066 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5997 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.5989 - categorical_accuracy: 0.9304\n",
      "Epoch 479/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5928 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5937 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.5972 - categorical_accuracy: 0.9304\n",
      "Epoch 480/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5916 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5944 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5956 - categorical_accuracy: 0.9303\n",
      "Epoch 481/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5866 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5924 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.5940 - categorical_accuracy: 0.9303\n",
      "Epoch 482/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5936 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5961 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.5924 - categorical_accuracy: 0.9302\n",
      "Epoch 483/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5946 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5894 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.5908 - categorical_accuracy: 0.9303\n",
      "Epoch 484/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5924 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5892 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.5892 - categorical_accuracy: 0.9303\n",
      "Epoch 485/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5840 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5882 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5876 - categorical_accuracy: 0.9303\n",
      "Epoch 486/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5880 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5862 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5860 - categorical_accuracy: 0.9304\n",
      "Epoch 487/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5930 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5842 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.5845 - categorical_accuracy: 0.9306\n",
      "Epoch 488/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5753 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5802 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5829 - categorical_accuracy: 0.9306\n",
      "Epoch 489/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5739 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5807 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5813 - categorical_accuracy: 0.9307\n",
      "Epoch 490/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5804 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5810 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.5798 - categorical_accuracy: 0.9308\n",
      "Epoch 491/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5876 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5808 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.5783 - categorical_accuracy: 0.9310\n",
      "Epoch 492/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5817 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5781 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.5767 - categorical_accuracy: 0.9311\n",
      "Epoch 493/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5750 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5754 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5752 - categorical_accuracy: 0.9311\n",
      "Epoch 494/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5686 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5756 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5737 - categorical_accuracy: 0.9310\n",
      "Epoch 495/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5665 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5695 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5722 - categorical_accuracy: 0.9310\n",
      "Epoch 496/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5617 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5686 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.5707 - categorical_accuracy: 0.9311\n",
      "Epoch 497/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5659 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5690 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5692 - categorical_accuracy: 0.9312\n",
      "Epoch 498/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5739 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5738 - categorical_accuracy: 0.92 - 1s 50us/step - loss: 0.5677 - categorical_accuracy: 0.9312\n",
      "Epoch 499/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5548 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5643 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.5663 - categorical_accuracy: 0.9311\n",
      "Epoch 500/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5645 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5631 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.5648 - categorical_accuracy: 0.9310\n",
      "Epoch 501/2000\n",
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      "Epoch 502/2000\n",
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      "Epoch 503/2000\n",
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      "Epoch 504/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5611 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5593 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5590 - categorical_accuracy: 0.9312\n"
     ]
    },
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     "text": [
      "Epoch 505/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5527 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5595 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.5576 - categorical_accuracy: 0.9313\n",
      "Epoch 506/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5530 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5499 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5562 - categorical_accuracy: 0.9313\n",
      "Epoch 507/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5552 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5536 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5548 - categorical_accuracy: 0.9314\n",
      "Epoch 508/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5460 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5580 - categorical_accuracy: 0.92 - 0s 48us/step - loss: 0.5534 - categorical_accuracy: 0.9317\n",
      "Epoch 509/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5421 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5467 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5520 - categorical_accuracy: 0.9319\n",
      "Epoch 510/2000\n",
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      "Epoch 511/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5614 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5514 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5492 - categorical_accuracy: 0.9321\n",
      "Epoch 512/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5528 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5470 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5478 - categorical_accuracy: 0.9321\n",
      "Epoch 513/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5449 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5460 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5464 - categorical_accuracy: 0.9322\n",
      "Epoch 514/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5539 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5445 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5451 - categorical_accuracy: 0.9322\n",
      "Epoch 515/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5531 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5420 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5437 - categorical_accuracy: 0.9321\n",
      "Epoch 516/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5446 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5410 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5424 - categorical_accuracy: 0.9323\n",
      "Epoch 517/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5429 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5436 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5411 - categorical_accuracy: 0.9323\n",
      "Epoch 518/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5404 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5388 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5397 - categorical_accuracy: 0.9324\n",
      "Epoch 519/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5438 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5430 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.5384 - categorical_accuracy: 0.9324\n",
      "Epoch 520/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5295 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5335 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5371 - categorical_accuracy: 0.9324\n",
      "Epoch 521/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5328 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5338 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5358 - categorical_accuracy: 0.9324\n",
      "Epoch 522/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5278 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5311 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.5345 - categorical_accuracy: 0.9325\n",
      "Epoch 523/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5324 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5384 - categorical_accuracy: 0.92 - 1s 49us/step - loss: 0.5332 - categorical_accuracy: 0.9326\n",
      "Epoch 524/2000\n",
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      "Epoch 525/2000\n",
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      "Epoch 526/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5236 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5289 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.5293 - categorical_accuracy: 0.9327\n",
      "Epoch 527/2000\n",
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      "Epoch 529/2000\n",
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      "Epoch 530/2000\n",
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      "Epoch 532/2000\n",
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      "Epoch 534/2000\n",
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      "Epoch 535/2000\n",
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      "Epoch 536/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5108 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5192 - categorical_accuracy: 0.93 - 1s 52us/step - loss: 0.5168 - categorical_accuracy: 0.9332\n",
      "Epoch 537/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5094 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5116 - categorical_accuracy: 0.93 - 1s 52us/step - loss: 0.5156 - categorical_accuracy: 0.9331\n",
      "Epoch 538/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5108 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5135 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.5144 - categorical_accuracy: 0.9332\n",
      "Epoch 539/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5070 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5171 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5132 - categorical_accuracy: 0.9332\n",
      "Epoch 540/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5084 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5100 - categorical_accuracy: 0.93 - 1s 52us/step - loss: 0.5120 - categorical_accuracy: 0.9332\n"
     ]
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      "Epoch 541/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5053 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5120 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.5108 - categorical_accuracy: 0.9332\n",
      "Epoch 542/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5157 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5077 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5096 - categorical_accuracy: 0.9333\n",
      "Epoch 543/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5050 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5079 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5084 - categorical_accuracy: 0.9333\n",
      "Epoch 544/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5089 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5031 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5073 - categorical_accuracy: 0.9334\n",
      "Epoch 545/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5133 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5052 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5061 - categorical_accuracy: 0.9334\n",
      "Epoch 546/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5041 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5048 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5049 - categorical_accuracy: 0.9334\n",
      "Epoch 547/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5022 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5029 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5038 - categorical_accuracy: 0.9335\n",
      "Epoch 548/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4943 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5039 - categorical_accuracy: 0.93 - 0s 47us/step - loss: 0.5026 - categorical_accuracy: 0.9335\n",
      "Epoch 549/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5070 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5007 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.5015 - categorical_accuracy: 0.9335\n",
      "Epoch 550/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4971 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4966 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.5003 - categorical_accuracy: 0.9335\n",
      "Epoch 551/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5082 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.5003 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4992 - categorical_accuracy: 0.9339\n",
      "Epoch 552/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.5039 - categorical_accuracy: 0.92 - ETA: 0s - loss: 0.5007 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4981 - categorical_accuracy: 0.9340\n",
      "Epoch 553/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4843 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4919 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4969 - categorical_accuracy: 0.9341\n",
      "Epoch 554/2000\n",
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      "Epoch 555/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4808 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4956 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.4947 - categorical_accuracy: 0.9341\n",
      "Epoch 556/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4912 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4946 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4936 - categorical_accuracy: 0.9343\n",
      "Epoch 557/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4952 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4895 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.4925 - categorical_accuracy: 0.9345\n",
      "Epoch 558/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4959 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4907 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4914 - categorical_accuracy: 0.9347\n",
      "Epoch 559/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4865 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4866 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4903 - categorical_accuracy: 0.9348\n",
      "Epoch 560/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4934 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4875 - categorical_accuracy: 0.93 - 1s 56us/step - loss: 0.4892 - categorical_accuracy: 0.9348\n",
      "Epoch 561/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4886 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4903 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4881 - categorical_accuracy: 0.9348\n",
      "Epoch 562/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4756 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4869 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4870 - categorical_accuracy: 0.9348\n",
      "Epoch 563/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4900 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4841 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4859 - categorical_accuracy: 0.9347\n",
      "Epoch 564/2000\n",
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      "Epoch 565/2000\n",
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      "Epoch 566/2000\n",
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      "Epoch 567/2000\n",
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      "Epoch 568/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4743 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4790 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4806 - categorical_accuracy: 0.9349\n",
      "Epoch 569/2000\n",
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      "Epoch 570/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4825 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4771 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4785 - categorical_accuracy: 0.9352\n",
      "Epoch 571/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4792 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4773 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4775 - categorical_accuracy: 0.9352\n",
      "Epoch 572/2000\n",
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      "Epoch 573/2000\n",
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      "Epoch 574/2000\n",
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      "Epoch 576/2000\n",
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     ]
    },
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      "Epoch 577/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4692 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4748 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4714 - categorical_accuracy: 0.9353\n",
      "Epoch 578/2000\n",
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      "Epoch 579/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4710 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4710 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.4694 - categorical_accuracy: 0.9353\n",
      "Epoch 580/2000\n",
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      "Epoch 581/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4716 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4650 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4674 - categorical_accuracy: 0.9354\n",
      "Epoch 582/2000\n",
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      "Epoch 583/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4558 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4678 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4654 - categorical_accuracy: 0.9354\n",
      "Epoch 584/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4689 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4641 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4644 - categorical_accuracy: 0.9354\n",
      "Epoch 585/2000\n",
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      "Epoch 586/2000\n",
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      "Epoch 587/2000\n",
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      "Epoch 588/2000\n",
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      "Epoch 589/2000\n",
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      "Epoch 590/2000\n",
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      "Epoch 591/2000\n",
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      "Epoch 592/2000\n",
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      "Epoch 593/2000\n",
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      "Epoch 594/2000\n",
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      "Epoch 595/2000\n",
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      "Epoch 596/2000\n",
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      "Epoch 597/2000\n",
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      "Epoch 598/2000\n",
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      "Epoch 599/2000\n",
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      "Epoch 600/2000\n",
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      "Epoch 601/2000\n",
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      "Epoch 603/2000\n",
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     ]
    },
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      "Epoch 613/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4416 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4404 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4378 - categorical_accuracy: 0.9369\n",
      "Epoch 614/2000\n",
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      "Epoch 615/2000\n",
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      "Epoch 617/2000\n",
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      "Epoch 618/2000\n",
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      "Epoch 619/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4314 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4312 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4327 - categorical_accuracy: 0.9365\n",
      "Epoch 620/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4225 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4278 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4318 - categorical_accuracy: 0.9365\n",
      "Epoch 621/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4349 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4356 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4310 - categorical_accuracy: 0.9369\n",
      "Epoch 622/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4357 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4323 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4302 - categorical_accuracy: 0.9370\n",
      "Epoch 623/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4392 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4295 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4293 - categorical_accuracy: 0.9371\n",
      "Epoch 624/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4289 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4302 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4285 - categorical_accuracy: 0.9372\n",
      "Epoch 625/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4196 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4218 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4277 - categorical_accuracy: 0.9372\n",
      "Epoch 626/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4277 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4276 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4268 - categorical_accuracy: 0.9374\n",
      "Epoch 627/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4283 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4254 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4260 - categorical_accuracy: 0.9371\n",
      "Epoch 628/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4203 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4246 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4252 - categorical_accuracy: 0.9369\n",
      "Epoch 629/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4259 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4243 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.4244 - categorical_accuracy: 0.9368\n",
      "Epoch 630/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4249 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4258 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4236 - categorical_accuracy: 0.9370\n",
      "Epoch 631/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4260 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4214 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4228 - categorical_accuracy: 0.9370\n",
      "Epoch 632/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4209 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4187 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4220 - categorical_accuracy: 0.9370\n",
      "Epoch 633/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4302 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4240 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4212 - categorical_accuracy: 0.9372\n",
      "Epoch 634/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4117 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4164 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4204 - categorical_accuracy: 0.9373\n",
      "Epoch 635/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4164 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4185 - categorical_accuracy: 0.93 - 1s 53us/step - loss: 0.4196 - categorical_accuracy: 0.9376\n",
      "Epoch 636/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4328 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4188 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4188 - categorical_accuracy: 0.9377\n",
      "Epoch 637/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4181 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4177 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4180 - categorical_accuracy: 0.9377\n",
      "Epoch 638/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4124 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4150 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4172 - categorical_accuracy: 0.9376\n",
      "Epoch 639/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4131 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4128 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4164 - categorical_accuracy: 0.9377\n",
      "Epoch 640/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4111 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4121 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4157 - categorical_accuracy: 0.9378\n",
      "Epoch 641/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4168 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4122 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4149 - categorical_accuracy: 0.9378\n",
      "Epoch 642/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4137 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4140 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4141 - categorical_accuracy: 0.9378\n",
      "Epoch 643/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4148 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4125 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4134 - categorical_accuracy: 0.9378\n",
      "Epoch 644/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4113 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4137 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4126 - categorical_accuracy: 0.9380\n",
      "Epoch 645/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4120 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4131 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4118 - categorical_accuracy: 0.9381\n",
      "Epoch 646/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4053 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4104 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4111 - categorical_accuracy: 0.9381\n",
      "Epoch 647/2000\n",
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      "Epoch 648/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4096 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4042 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4095 - categorical_accuracy: 0.9383\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 649/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4125 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4044 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4088 - categorical_accuracy: 0.9383\n",
      "Epoch 650/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4079 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4071 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4080 - categorical_accuracy: 0.9384\n",
      "Epoch 651/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4021 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4064 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.4073 - categorical_accuracy: 0.9385\n",
      "Epoch 652/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3973 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4027 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4066 - categorical_accuracy: 0.9385\n",
      "Epoch 653/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4071 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4057 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4058 - categorical_accuracy: 0.9384\n",
      "Epoch 654/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4017 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4028 - categorical_accuracy: 0.93 - 0s 47us/step - loss: 0.4051 - categorical_accuracy: 0.9383\n",
      "Epoch 655/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4108 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4063 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4043 - categorical_accuracy: 0.9383\n",
      "Epoch 656/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3993 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4043 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.4036 - categorical_accuracy: 0.9385\n",
      "Epoch 657/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3965 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4034 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4029 - categorical_accuracy: 0.9389\n",
      "Epoch 658/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3964 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.4033 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4022 - categorical_accuracy: 0.9390\n",
      "Epoch 659/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3985 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.4035 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.4014 - categorical_accuracy: 0.9392\n",
      "Epoch 660/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4032 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3998 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.4007 - categorical_accuracy: 0.9393\n",
      "Epoch 661/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3911 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3996 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.4000 - categorical_accuracy: 0.9393\n",
      "Epoch 662/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3990 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3992 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.3993 - categorical_accuracy: 0.9394\n",
      "Epoch 663/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3953 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3927 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3986 - categorical_accuracy: 0.9393\n",
      "Epoch 664/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3960 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3987 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3978 - categorical_accuracy: 0.9393\n",
      "Epoch 665/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3995 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3992 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.3971 - categorical_accuracy: 0.9393\n",
      "Epoch 666/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3983 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3974 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.3964 - categorical_accuracy: 0.9395\n",
      "Epoch 667/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.4050 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3972 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.3957 - categorical_accuracy: 0.9395\n",
      "Epoch 668/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3947 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3997 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.3950 - categorical_accuracy: 0.9395\n",
      "Epoch 669/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3907 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3926 - categorical_accuracy: 0.94 - 1s 54us/step - loss: 0.3943 - categorical_accuracy: 0.9396\n",
      "Epoch 670/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3937 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3946 - categorical_accuracy: 0.93 - 1s 57us/step - loss: 0.3936 - categorical_accuracy: 0.9396\n",
      "Epoch 671/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3930 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3941 - categorical_accuracy: 0.93 - 1s 57us/step - loss: 0.3929 - categorical_accuracy: 0.9396\n",
      "Epoch 672/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3894 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3901 - categorical_accuracy: 0.94 - 1s 57us/step - loss: 0.3922 - categorical_accuracy: 0.9396\n",
      "Epoch 673/2000\n",
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      "Epoch 674/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3903 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3871 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3909 - categorical_accuracy: 0.9396\n",
      "Epoch 675/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3917 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3902 - categorical_accuracy: 0.93 - 1s 53us/step - loss: 0.3902 - categorical_accuracy: 0.9396\n",
      "Epoch 676/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3925 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3875 - categorical_accuracy: 0.93 - 1s 50us/step - loss: 0.3895 - categorical_accuracy: 0.9396\n",
      "Epoch 677/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3944 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3888 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3888 - categorical_accuracy: 0.9397\n",
      "Epoch 678/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3887 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3856 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3882 - categorical_accuracy: 0.9397\n",
      "Epoch 679/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3822 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3895 - categorical_accuracy: 0.93 - 1s 51us/step - loss: 0.3875 - categorical_accuracy: 0.9397\n",
      "Epoch 680/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3944 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3846 - categorical_accuracy: 0.94 - 1s 53us/step - loss: 0.3868 - categorical_accuracy: 0.9397\n",
      "Epoch 681/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3843 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3873 - categorical_accuracy: 0.93 - 1s 52us/step - loss: 0.3861 - categorical_accuracy: 0.9398\n",
      "Epoch 682/2000\n",
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      "Epoch 683/2000\n",
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      "Epoch 684/2000\n",
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     ]
    },
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     "text": [
      "Epoch 685/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3813 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3820 - categorical_accuracy: 0.93 - 0s 48us/step - loss: 0.3835 - categorical_accuracy: 0.9401\n",
      "Epoch 686/2000\n",
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      "Epoch 687/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3853 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3803 - categorical_accuracy: 0.93 - 0s 47us/step - loss: 0.3822 - categorical_accuracy: 0.9403\n",
      "Epoch 688/2000\n",
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      "Epoch 689/2000\n",
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      "Epoch 690/2000\n",
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      "Epoch 691/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3877 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3779 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3796 - categorical_accuracy: 0.9406\n",
      "Epoch 692/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3860 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3784 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3789 - categorical_accuracy: 0.9406\n",
      "Epoch 693/2000\n",
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      "Epoch 694/2000\n",
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      "Epoch 695/2000\n",
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      "Epoch 696/2000\n",
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      "Epoch 697/2000\n",
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      "Epoch 698/2000\n",
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      "Epoch 699/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3733 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3766 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3745 - categorical_accuracy: 0.9408\n",
      "Epoch 700/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3662 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3718 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3739 - categorical_accuracy: 0.9408\n",
      "Epoch 701/2000\n",
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      "Epoch 702/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3726 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3716 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3726 - categorical_accuracy: 0.9409\n",
      "Epoch 703/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3696 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3745 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.3720 - categorical_accuracy: 0.9411\n",
      "Epoch 704/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3824 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3720 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3714 - categorical_accuracy: 0.9411\n",
      "Epoch 705/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3602 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3718 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3708 - categorical_accuracy: 0.9411\n",
      "Epoch 706/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3730 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3682 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3701 - categorical_accuracy: 0.9412\n",
      "Epoch 707/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3777 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3716 - categorical_accuracy: 0.93 - 0s 49us/step - loss: 0.3695 - categorical_accuracy: 0.9413\n",
      "Epoch 708/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3788 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3721 - categorical_accuracy: 0.94 - 1s 52us/step - loss: 0.3689 - categorical_accuracy: 0.9414\n",
      "Epoch 709/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3715 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3692 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3683 - categorical_accuracy: 0.9415\n",
      "Epoch 710/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3676 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3666 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3677 - categorical_accuracy: 0.9415\n",
      "Epoch 711/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3694 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3705 - categorical_accuracy: 0.93 - 1s 49us/step - loss: 0.3671 - categorical_accuracy: 0.9417\n",
      "Epoch 712/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3663 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3671 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3665 - categorical_accuracy: 0.9417\n",
      "Epoch 713/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3679 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3647 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3659 - categorical_accuracy: 0.9418\n",
      "Epoch 714/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3659 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3657 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3653 - categorical_accuracy: 0.9420\n",
      "Epoch 715/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3660 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3618 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3647 - categorical_accuracy: 0.9421\n",
      "Epoch 716/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3607 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3632 - categorical_accuracy: 0.94 - 1s 52us/step - loss: 0.3641 - categorical_accuracy: 0.9423\n",
      "Epoch 717/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3571 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3622 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3635 - categorical_accuracy: 0.9423\n",
      "Epoch 718/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3569 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3584 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3629 - categorical_accuracy: 0.9423\n",
      "Epoch 719/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3569 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3643 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3623 - categorical_accuracy: 0.9423\n",
      "Epoch 720/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3648 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3625 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3618 - categorical_accuracy: 0.9422\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 721/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3506 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3604 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3612 - categorical_accuracy: 0.9424\n",
      "Epoch 722/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3607 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3591 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3606 - categorical_accuracy: 0.9424\n",
      "Epoch 723/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3536 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3589 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3600 - categorical_accuracy: 0.9424\n",
      "Epoch 724/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3616 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3587 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3594 - categorical_accuracy: 0.9425\n",
      "Epoch 725/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3544 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3581 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3589 - categorical_accuracy: 0.9426\n",
      "Epoch 726/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3598 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3582 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3583 - categorical_accuracy: 0.9426\n",
      "Epoch 727/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3519 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3565 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3577 - categorical_accuracy: 0.9425\n",
      "Epoch 728/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3634 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3540 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3571 - categorical_accuracy: 0.9425\n",
      "Epoch 729/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3505 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3564 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3566 - categorical_accuracy: 0.9425\n",
      "Epoch 730/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3465 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3539 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3560 - categorical_accuracy: 0.9427\n",
      "Epoch 731/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3563 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3557 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3554 - categorical_accuracy: 0.9427\n",
      "Epoch 732/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3532 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3547 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3549 - categorical_accuracy: 0.9427\n",
      "Epoch 733/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3527 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3520 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3543 - categorical_accuracy: 0.9427\n",
      "Epoch 734/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3571 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3546 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3537 - categorical_accuracy: 0.9426\n",
      "Epoch 735/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3539 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3513 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3532 - categorical_accuracy: 0.9426\n",
      "Epoch 736/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3513 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3487 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3526 - categorical_accuracy: 0.9429\n",
      "Epoch 737/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3556 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3515 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3521 - categorical_accuracy: 0.9429\n",
      "Epoch 738/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3549 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3515 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3515 - categorical_accuracy: 0.9429\n",
      "Epoch 739/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3474 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3524 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3510 - categorical_accuracy: 0.9430\n",
      "Epoch 740/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3402 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3437 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3504 - categorical_accuracy: 0.9430\n",
      "Epoch 741/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3414 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3465 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3499 - categorical_accuracy: 0.9429\n",
      "Epoch 742/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3514 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3490 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3493 - categorical_accuracy: 0.9429\n",
      "Epoch 743/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3476 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3478 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3488 - categorical_accuracy: 0.9432\n",
      "Epoch 744/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3508 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3482 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3482 - categorical_accuracy: 0.9434\n",
      "Epoch 745/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3465 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3484 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3477 - categorical_accuracy: 0.9435\n",
      "Epoch 746/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3540 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3484 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3471 - categorical_accuracy: 0.9437\n",
      "Epoch 747/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3456 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3451 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3466 - categorical_accuracy: 0.9438\n",
      "Epoch 748/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3450 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3452 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3461 - categorical_accuracy: 0.9439\n",
      "Epoch 749/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3503 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3494 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3455 - categorical_accuracy: 0.9438\n",
      "Epoch 750/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3514 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3447 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3450 - categorical_accuracy: 0.9438\n",
      "Epoch 751/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3394 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3453 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3445 - categorical_accuracy: 0.9438\n",
      "Epoch 752/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3534 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3451 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3440 - categorical_accuracy: 0.9440\n",
      "Epoch 753/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3422 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3416 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3434 - categorical_accuracy: 0.9440\n",
      "Epoch 754/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3529 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3431 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3429 - categorical_accuracy: 0.9440\n",
      "Epoch 755/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3407 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3436 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3424 - categorical_accuracy: 0.9440\n",
      "Epoch 756/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3477 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3443 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3418 - categorical_accuracy: 0.9440\n"
     ]
    },
    {
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     "text": [
      "Epoch 757/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3440 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3404 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3413 - categorical_accuracy: 0.9440\n",
      "Epoch 758/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3386 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3378 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3408 - categorical_accuracy: 0.9440\n",
      "Epoch 759/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3389 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3370 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3403 - categorical_accuracy: 0.9441\n",
      "Epoch 760/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3390 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3416 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3398 - categorical_accuracy: 0.9440\n",
      "Epoch 761/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3371 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3396 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3392 - categorical_accuracy: 0.9438\n",
      "Epoch 762/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3282 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3374 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3387 - categorical_accuracy: 0.9441\n",
      "Epoch 763/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3424 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3415 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3382 - categorical_accuracy: 0.9442\n",
      "Epoch 764/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3415 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3385 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3377 - categorical_accuracy: 0.9443\n",
      "Epoch 765/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3372 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3358 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3372 - categorical_accuracy: 0.9443\n",
      "Epoch 766/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3348 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3384 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3367 - categorical_accuracy: 0.9444\n",
      "Epoch 767/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3367 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3330 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3362 - categorical_accuracy: 0.9443\n",
      "Epoch 768/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3348 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3349 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3357 - categorical_accuracy: 0.9444\n",
      "Epoch 769/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3309 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3357 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3352 - categorical_accuracy: 0.9445\n",
      "Epoch 770/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3202 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3332 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3347 - categorical_accuracy: 0.9445\n",
      "Epoch 771/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3332 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3343 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3342 - categorical_accuracy: 0.9445\n",
      "Epoch 772/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3366 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3337 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3337 - categorical_accuracy: 0.9445\n",
      "Epoch 773/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3375 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3328 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3332 - categorical_accuracy: 0.9445\n",
      "Epoch 774/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3355 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3336 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3327 - categorical_accuracy: 0.9446\n",
      "Epoch 775/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3365 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3355 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3322 - categorical_accuracy: 0.9444\n",
      "Epoch 776/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3288 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3328 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3317 - categorical_accuracy: 0.9444\n",
      "Epoch 777/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3343 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3294 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3312 - categorical_accuracy: 0.9447\n",
      "Epoch 778/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3318 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3322 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3307 - categorical_accuracy: 0.9448\n",
      "Epoch 779/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3255 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3309 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3302 - categorical_accuracy: 0.9448\n",
      "Epoch 780/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3356 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3325 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3297 - categorical_accuracy: 0.9449\n",
      "Epoch 781/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3324 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3297 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3292 - categorical_accuracy: 0.9449\n",
      "Epoch 782/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3254 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3294 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3288 - categorical_accuracy: 0.9450\n",
      "Epoch 783/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3292 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3277 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3283 - categorical_accuracy: 0.9451\n",
      "Epoch 784/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3122 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3258 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3278 - categorical_accuracy: 0.9452\n",
      "Epoch 785/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3370 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3251 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3273 - categorical_accuracy: 0.9453\n",
      "Epoch 786/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3431 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3291 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3268 - categorical_accuracy: 0.9455\n",
      "Epoch 787/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3213 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3244 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3264 - categorical_accuracy: 0.9456\n",
      "Epoch 788/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3312 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3268 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3259 - categorical_accuracy: 0.9458\n",
      "Epoch 789/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3262 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3308 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3254 - categorical_accuracy: 0.9458\n",
      "Epoch 790/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3255 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3250 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.3249 - categorical_accuracy: 0.9458\n",
      "Epoch 791/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3173 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3212 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3245 - categorical_accuracy: 0.9458\n",
      "Epoch 792/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3274 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3246 - categorical_accuracy: 0.94 - 1s 55us/step - loss: 0.3240 - categorical_accuracy: 0.9458\n"
     ]
    },
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     "text": [
      "Epoch 793/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3312 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3227 - categorical_accuracy: 0.94 - 1s 52us/step - loss: 0.3235 - categorical_accuracy: 0.9459\n",
      "Epoch 794/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3251 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3249 - categorical_accuracy: 0.94 - 0s 47us/step - loss: 0.3230 - categorical_accuracy: 0.9459\n",
      "Epoch 795/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3260 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3238 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3226 - categorical_accuracy: 0.9460\n",
      "Epoch 796/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3316 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3254 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3221 - categorical_accuracy: 0.9461\n",
      "Epoch 797/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3184 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3213 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3216 - categorical_accuracy: 0.9460\n",
      "Epoch 798/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3225 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3215 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3212 - categorical_accuracy: 0.9461\n",
      "Epoch 799/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3207 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3182 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3207 - categorical_accuracy: 0.9461\n",
      "Epoch 800/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3152 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3176 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3203 - categorical_accuracy: 0.9462\n",
      "Epoch 801/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3164 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3154 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3198 - categorical_accuracy: 0.9460\n",
      "Epoch 802/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3236 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3208 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3193 - categorical_accuracy: 0.9460\n",
      "Epoch 803/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3110 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3178 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3189 - categorical_accuracy: 0.9459\n",
      "Epoch 804/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3366 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3249 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3184 - categorical_accuracy: 0.9461\n",
      "Epoch 805/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3321 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3232 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3180 - categorical_accuracy: 0.9462\n",
      "Epoch 806/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3203 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3117 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3175 - categorical_accuracy: 0.9463\n",
      "Epoch 807/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3253 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3226 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3171 - categorical_accuracy: 0.9463\n",
      "Epoch 808/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3053 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3168 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3166 - categorical_accuracy: 0.9463\n",
      "Epoch 809/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3044 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3146 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3162 - categorical_accuracy: 0.9461\n",
      "Epoch 810/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3127 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3164 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3157 - categorical_accuracy: 0.9463\n",
      "Epoch 811/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3320 - categorical_accuracy: 0.93 - ETA: 0s - loss: 0.3164 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3153 - categorical_accuracy: 0.9464\n",
      "Epoch 812/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3195 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3155 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3148 - categorical_accuracy: 0.9465\n",
      "Epoch 813/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3167 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3166 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3144 - categorical_accuracy: 0.9466\n",
      "Epoch 814/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3175 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3129 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3139 - categorical_accuracy: 0.9466\n",
      "Epoch 815/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3131 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3110 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3135 - categorical_accuracy: 0.9468\n",
      "Epoch 816/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3142 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3108 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3131 - categorical_accuracy: 0.9470\n",
      "Epoch 817/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3132 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3128 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3126 - categorical_accuracy: 0.9469\n",
      "Epoch 818/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3051 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3132 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3122 - categorical_accuracy: 0.9470\n",
      "Epoch 819/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3154 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3137 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3117 - categorical_accuracy: 0.9471\n",
      "Epoch 820/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3095 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3086 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.3113 - categorical_accuracy: 0.9472\n",
      "Epoch 821/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3251 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3124 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3109 - categorical_accuracy: 0.9472\n",
      "Epoch 822/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3076 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3094 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3104 - categorical_accuracy: 0.9472\n",
      "Epoch 823/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3091 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3084 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3100 - categorical_accuracy: 0.9475\n",
      "Epoch 824/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3162 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3097 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3096 - categorical_accuracy: 0.9476\n",
      "Epoch 825/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3085 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3091 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3091 - categorical_accuracy: 0.9476\n",
      "Epoch 826/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3057 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3066 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3087 - categorical_accuracy: 0.9476\n",
      "Epoch 827/2000\n",
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      "Epoch 828/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3003 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3062 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.3078 - categorical_accuracy: 0.9475\n"
     ]
    },
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     "text": [
      "Epoch 829/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3015 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3039 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3074 - categorical_accuracy: 0.9475\n",
      "Epoch 830/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3117 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3093 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3070 - categorical_accuracy: 0.9475\n",
      "Epoch 831/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3114 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3088 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3066 - categorical_accuracy: 0.9476\n",
      "Epoch 832/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3149 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3058 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3061 - categorical_accuracy: 0.9476\n",
      "Epoch 833/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3020 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3029 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3057 - categorical_accuracy: 0.9477\n",
      "Epoch 834/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3064 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3087 - categorical_accuracy: 0.94 - 0s 47us/step - loss: 0.3053 - categorical_accuracy: 0.9477\n",
      "Epoch 835/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3038 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3026 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3049 - categorical_accuracy: 0.9477\n",
      "Epoch 836/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3071 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3073 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3045 - categorical_accuracy: 0.9477\n",
      "Epoch 837/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3067 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3016 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3040 - categorical_accuracy: 0.9477\n",
      "Epoch 838/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3049 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3051 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3036 - categorical_accuracy: 0.9474\n",
      "Epoch 839/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3119 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3051 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3032 - categorical_accuracy: 0.9475\n",
      "Epoch 840/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3085 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3023 - categorical_accuracy: 0.94 - 1s 52us/step - loss: 0.3028 - categorical_accuracy: 0.9476\n",
      "Epoch 841/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3081 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3029 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3024 - categorical_accuracy: 0.9478\n",
      "Epoch 842/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3006 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3020 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3020 - categorical_accuracy: 0.9479\n",
      "Epoch 843/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2987 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2980 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3016 - categorical_accuracy: 0.9479\n",
      "Epoch 844/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3009 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3015 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.3011 - categorical_accuracy: 0.9479\n",
      "Epoch 845/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2963 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2979 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.3007 - categorical_accuracy: 0.9479\n",
      "Epoch 846/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2928 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2958 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.3003 - categorical_accuracy: 0.9479\n",
      "Epoch 847/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2992 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.3027 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.2999 - categorical_accuracy: 0.9479\n",
      "Epoch 848/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2985 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2987 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2995 - categorical_accuracy: 0.9479\n",
      "Epoch 849/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2928 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2968 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.2991 - categorical_accuracy: 0.9480\n",
      "Epoch 850/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2981 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2984 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2987 - categorical_accuracy: 0.9479\n",
      "Epoch 851/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2920 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2954 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2983 - categorical_accuracy: 0.9479\n",
      "Epoch 852/2000\n",
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      "Epoch 853/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3016 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2981 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2975 - categorical_accuracy: 0.9479\n",
      "Epoch 854/2000\n",
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      "Epoch 855/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3048 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3026 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2967 - categorical_accuracy: 0.9479\n",
      "Epoch 856/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.3063 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.3001 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2963 - categorical_accuracy: 0.9480\n",
      "Epoch 857/2000\n",
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      "Epoch 858/2000\n",
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      "Epoch 859/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2939 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2932 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2951 - categorical_accuracy: 0.9480\n",
      "Epoch 860/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2903 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2935 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2947 - categorical_accuracy: 0.9480\n",
      "Epoch 861/2000\n",
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      "Epoch 862/2000\n",
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      "Epoch 863/2000\n",
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      "Epoch 864/2000\n",
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     ]
    },
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     "text": [
      "Epoch 865/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2929 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2938 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2928 - categorical_accuracy: 0.9482\n",
      "Epoch 866/2000\n",
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      "Epoch 867/2000\n",
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      "Epoch 868/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2914 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2915 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2916 - categorical_accuracy: 0.9484\n",
      "Epoch 869/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2902 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2899 - categorical_accuracy: 0.94 - 0s 47us/step - loss: 0.2912 - categorical_accuracy: 0.9484\n",
      "Epoch 870/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2929 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2895 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2908 - categorical_accuracy: 0.9485\n",
      "Epoch 871/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2838 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2925 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2905 - categorical_accuracy: 0.9486\n",
      "Epoch 872/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2905 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2918 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2901 - categorical_accuracy: 0.9488\n",
      "Epoch 873/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2909 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2903 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2897 - categorical_accuracy: 0.9488\n",
      "Epoch 874/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2958 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2906 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2893 - categorical_accuracy: 0.9491\n",
      "Epoch 875/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2894 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2910 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2889 - categorical_accuracy: 0.9492\n",
      "Epoch 876/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2951 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2919 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2886 - categorical_accuracy: 0.9493\n",
      "Epoch 877/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2795 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2885 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2882 - categorical_accuracy: 0.9493\n",
      "Epoch 878/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2875 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2887 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2878 - categorical_accuracy: 0.9493\n",
      "Epoch 879/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2894 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2885 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2874 - categorical_accuracy: 0.9492\n",
      "Epoch 880/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2869 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2887 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2871 - categorical_accuracy: 0.9493\n",
      "Epoch 881/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2853 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2876 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.2867 - categorical_accuracy: 0.9494\n",
      "Epoch 882/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2805 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2872 - categorical_accuracy: 0.94 - 0s 49us/step - loss: 0.2863 - categorical_accuracy: 0.9493\n",
      "Epoch 883/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2897 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2853 - categorical_accuracy: 0.94 - 1s 55us/step - loss: 0.2859 - categorical_accuracy: 0.9492\n",
      "Epoch 884/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2819 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2851 - categorical_accuracy: 0.95 - 1s 56us/step - loss: 0.2856 - categorical_accuracy: 0.9492\n",
      "Epoch 885/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2864 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2867 - categorical_accuracy: 0.95 - 1s 60us/step - loss: 0.2852 - categorical_accuracy: 0.9494\n",
      "Epoch 886/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2779 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2839 - categorical_accuracy: 0.95 - 1s 61us/step - loss: 0.2848 - categorical_accuracy: 0.9494\n",
      "Epoch 887/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2778 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2804 - categorical_accuracy: 0.95 - 1s 60us/step - loss: 0.2844 - categorical_accuracy: 0.9493\n",
      "Epoch 888/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2870 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2893 - categorical_accuracy: 0.94 - 1s 58us/step - loss: 0.2841 - categorical_accuracy: 0.9494\n",
      "Epoch 889/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2908 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2849 - categorical_accuracy: 0.94 - 1s 57us/step - loss: 0.2837 - categorical_accuracy: 0.9494\n",
      "Epoch 890/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2798 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2859 - categorical_accuracy: 0.94 - 1s 56us/step - loss: 0.2833 - categorical_accuracy: 0.9495\n",
      "Epoch 891/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2873 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2861 - categorical_accuracy: 0.94 - 1s 54us/step - loss: 0.2830 - categorical_accuracy: 0.9496\n",
      "Epoch 892/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2719 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2799 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2826 - categorical_accuracy: 0.9497\n",
      "Epoch 893/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2846 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2829 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2823 - categorical_accuracy: 0.9497\n",
      "Epoch 894/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2782 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2853 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2819 - categorical_accuracy: 0.9497\n",
      "Epoch 895/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2862 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2810 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2815 - categorical_accuracy: 0.9497\n",
      "Epoch 896/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2781 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2820 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2812 - categorical_accuracy: 0.9498\n",
      "Epoch 897/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2790 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2818 - categorical_accuracy: 0.94 - 1s 51us/step - loss: 0.2808 - categorical_accuracy: 0.9499\n",
      "Epoch 898/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2841 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2838 - categorical_accuracy: 0.94 - 1s 50us/step - loss: 0.2804 - categorical_accuracy: 0.9500\n",
      "Epoch 899/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2743 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2769 - categorical_accuracy: 0.95 - 1s 53us/step - loss: 0.2801 - categorical_accuracy: 0.9500\n",
      "Epoch 900/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2784 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2801 - categorical_accuracy: 0.94 - 1s 55us/step - loss: 0.2797 - categorical_accuracy: 0.9500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 901/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2747 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2811 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2794 - categorical_accuracy: 0.9500\n",
      "Epoch 902/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2741 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2775 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2790 - categorical_accuracy: 0.9501\n",
      "Epoch 903/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2828 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2801 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2787 - categorical_accuracy: 0.9500\n",
      "Epoch 904/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2756 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2776 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2783 - categorical_accuracy: 0.9501\n",
      "Epoch 905/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2701 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2759 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2780 - categorical_accuracy: 0.9501\n",
      "Epoch 906/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2743 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2789 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2776 - categorical_accuracy: 0.9501\n",
      "Epoch 907/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2714 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2759 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2773 - categorical_accuracy: 0.9501\n",
      "Epoch 908/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2732 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2796 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2769 - categorical_accuracy: 0.9501\n",
      "Epoch 909/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2811 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2804 - categorical_accuracy: 0.94 - 0s 48us/step - loss: 0.2765 - categorical_accuracy: 0.9501\n",
      "Epoch 910/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2826 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2755 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2762 - categorical_accuracy: 0.9502\n",
      "Epoch 911/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2722 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2771 - categorical_accuracy: 0.94 - 1s 53us/step - loss: 0.2759 - categorical_accuracy: 0.9503\n",
      "Epoch 912/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2731 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2758 - categorical_accuracy: 0.95 - 1s 52us/step - loss: 0.2755 - categorical_accuracy: 0.9504\n",
      "Epoch 913/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2757 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2737 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2752 - categorical_accuracy: 0.9506\n",
      "Epoch 914/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2831 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2769 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2748 - categorical_accuracy: 0.9506\n",
      "Epoch 915/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2765 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2754 - categorical_accuracy: 0.94 - 1s 49us/step - loss: 0.2745 - categorical_accuracy: 0.9506\n",
      "Epoch 916/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2711 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2704 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2741 - categorical_accuracy: 0.9508\n",
      "Epoch 917/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2685 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2759 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2738 - categorical_accuracy: 0.9508\n",
      "Epoch 918/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2673 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2721 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2734 - categorical_accuracy: 0.9508\n",
      "Epoch 919/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2871 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2744 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2731 - categorical_accuracy: 0.9511\n",
      "Epoch 920/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2729 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2748 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2727 - categorical_accuracy: 0.9512\n",
      "Epoch 921/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2740 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2729 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2724 - categorical_accuracy: 0.9513\n",
      "Epoch 922/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2731 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2707 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2721 - categorical_accuracy: 0.9514\n",
      "Epoch 923/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2708 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2736 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2717 - categorical_accuracy: 0.9514\n",
      "Epoch 924/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2717 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2698 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2714 - categorical_accuracy: 0.9515\n",
      "Epoch 925/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2635 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2676 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2710 - categorical_accuracy: 0.9514\n",
      "Epoch 926/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2681 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2690 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2707 - categorical_accuracy: 0.9514\n",
      "Epoch 927/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2737 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2700 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2704 - categorical_accuracy: 0.9514\n",
      "Epoch 928/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2747 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2717 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2700 - categorical_accuracy: 0.9516\n",
      "Epoch 929/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2681 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2701 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2697 - categorical_accuracy: 0.9516\n",
      "Epoch 930/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2677 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2661 - categorical_accuracy: 0.95 - 1s 52us/step - loss: 0.2694 - categorical_accuracy: 0.9517\n",
      "Epoch 931/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2694 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2685 - categorical_accuracy: 0.95 - 1s 52us/step - loss: 0.2690 - categorical_accuracy: 0.9517\n",
      "Epoch 932/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2681 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2687 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2687 - categorical_accuracy: 0.9518\n",
      "Epoch 933/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2620 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2667 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2684 - categorical_accuracy: 0.9518\n",
      "Epoch 934/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2697 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2692 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2680 - categorical_accuracy: 0.9519\n",
      "Epoch 935/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2705 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2686 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2677 - categorical_accuracy: 0.9519\n",
      "Epoch 936/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2683 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2698 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2674 - categorical_accuracy: 0.9519\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 937/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2613 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2675 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2670 - categorical_accuracy: 0.9519\n",
      "Epoch 938/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2643 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2642 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2667 - categorical_accuracy: 0.9519\n",
      "Epoch 939/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2695 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2653 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2664 - categorical_accuracy: 0.9520\n",
      "Epoch 940/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2660 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2657 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2661 - categorical_accuracy: 0.9520\n",
      "Epoch 941/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2681 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2651 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2657 - categorical_accuracy: 0.9520\n",
      "Epoch 942/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2661 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2658 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2654 - categorical_accuracy: 0.9520\n",
      "Epoch 943/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2614 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2608 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2651 - categorical_accuracy: 0.9521\n",
      "Epoch 944/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2611 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2621 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2648 - categorical_accuracy: 0.9521\n",
      "Epoch 945/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2566 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2625 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2644 - categorical_accuracy: 0.9521\n",
      "Epoch 946/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2658 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2638 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2641 - categorical_accuracy: 0.9523\n",
      "Epoch 947/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2569 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2633 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2638 - categorical_accuracy: 0.9523\n",
      "Epoch 948/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2669 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2628 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2635 - categorical_accuracy: 0.9523\n",
      "Epoch 949/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2650 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2651 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2631 - categorical_accuracy: 0.9523\n",
      "Epoch 950/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2585 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2651 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2628 - categorical_accuracy: 0.9525\n",
      "Epoch 951/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2603 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2628 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2625 - categorical_accuracy: 0.9525\n",
      "Epoch 952/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2543 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2605 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2622 - categorical_accuracy: 0.9525\n",
      "Epoch 953/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2722 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2662 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2619 - categorical_accuracy: 0.9525\n",
      "Epoch 954/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2533 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2642 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2615 - categorical_accuracy: 0.9527\n",
      "Epoch 955/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2588 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2627 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2612 - categorical_accuracy: 0.9527\n",
      "Epoch 956/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2705 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2640 - categorical_accuracy: 0.95 - 1s 53us/step - loss: 0.2609 - categorical_accuracy: 0.9525\n",
      "Epoch 957/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2566 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2582 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2606 - categorical_accuracy: 0.9526\n",
      "Epoch 958/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2625 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2611 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2603 - categorical_accuracy: 0.9527\n",
      "Epoch 959/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2623 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2607 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2600 - categorical_accuracy: 0.9527\n",
      "Epoch 960/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2737 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2639 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2597 - categorical_accuracy: 0.9527\n",
      "Epoch 961/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2533 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2566 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2593 - categorical_accuracy: 0.9529\n",
      "Epoch 962/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2661 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2553 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2590 - categorical_accuracy: 0.9530\n",
      "Epoch 963/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2596 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2581 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2587 - categorical_accuracy: 0.9530\n",
      "Epoch 964/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2563 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2563 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2584 - categorical_accuracy: 0.9530\n",
      "Epoch 965/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2642 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2573 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2581 - categorical_accuracy: 0.9532\n",
      "Epoch 966/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2545 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2585 - categorical_accuracy: 0.95 - 1s 52us/step - loss: 0.2578 - categorical_accuracy: 0.9530\n",
      "Epoch 967/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2510 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2544 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2575 - categorical_accuracy: 0.9530\n",
      "Epoch 968/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2536 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2599 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2572 - categorical_accuracy: 0.9530\n",
      "Epoch 969/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2529 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2583 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2569 - categorical_accuracy: 0.9530\n",
      "Epoch 970/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2508 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2524 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2565 - categorical_accuracy: 0.9535\n",
      "Epoch 971/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2548 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2544 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2562 - categorical_accuracy: 0.9534\n",
      "Epoch 972/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2614 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2542 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2559 - categorical_accuracy: 0.9534\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 973/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2621 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2579 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2556 - categorical_accuracy: 0.9534\n",
      "Epoch 974/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2560 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2542 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2553 - categorical_accuracy: 0.9533\n",
      "Epoch 975/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2465 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2513 - categorical_accuracy: 0.95 - 0s 47us/step - loss: 0.2550 - categorical_accuracy: 0.9535\n",
      "Epoch 976/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2688 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2538 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2547 - categorical_accuracy: 0.9536\n",
      "Epoch 977/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2526 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2557 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2544 - categorical_accuracy: 0.9538\n",
      "Epoch 978/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2663 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2560 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2541 - categorical_accuracy: 0.9540\n",
      "Epoch 979/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2615 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2547 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2538 - categorical_accuracy: 0.9541\n",
      "Epoch 980/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2574 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2542 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2535 - categorical_accuracy: 0.9542\n",
      "Epoch 981/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2625 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2542 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2532 - categorical_accuracy: 0.9541\n",
      "Epoch 982/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2503 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2540 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2529 - categorical_accuracy: 0.9544\n",
      "Epoch 983/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2526 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2548 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2526 - categorical_accuracy: 0.9543\n",
      "Epoch 984/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2472 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2534 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2523 - categorical_accuracy: 0.9543\n",
      "Epoch 985/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2435 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2503 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2520 - categorical_accuracy: 0.9545\n",
      "Epoch 986/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2594 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2587 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2517 - categorical_accuracy: 0.9546\n",
      "Epoch 987/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2451 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2521 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2514 - categorical_accuracy: 0.9547\n",
      "Epoch 988/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2528 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2532 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2511 - categorical_accuracy: 0.9546\n",
      "Epoch 989/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2562 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2508 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2508 - categorical_accuracy: 0.9546\n",
      "Epoch 990/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2507 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2493 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2505 - categorical_accuracy: 0.9546\n",
      "Epoch 991/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2532 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2521 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2502 - categorical_accuracy: 0.9546\n",
      "Epoch 992/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2481 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2495 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2499 - categorical_accuracy: 0.9546\n",
      "Epoch 993/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2423 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2468 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2496 - categorical_accuracy: 0.9546\n",
      "Epoch 994/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2508 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2517 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2494 - categorical_accuracy: 0.9547\n",
      "Epoch 995/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2502 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2470 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2491 - categorical_accuracy: 0.9547\n",
      "Epoch 996/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2518 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2491 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2488 - categorical_accuracy: 0.9548\n",
      "Epoch 997/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2542 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2498 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2485 - categorical_accuracy: 0.9550\n",
      "Epoch 998/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2412 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2455 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2482 - categorical_accuracy: 0.9550\n",
      "Epoch 999/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2488 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2485 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2479 - categorical_accuracy: 0.9550\n",
      "Epoch 1000/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2442 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2484 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2476 - categorical_accuracy: 0.9549\n",
      "Epoch 1001/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2494 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2500 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2473 - categorical_accuracy: 0.9549\n",
      "Epoch 1002/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2508 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2483 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2470 - categorical_accuracy: 0.9549\n",
      "Epoch 1003/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2606 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2517 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2467 - categorical_accuracy: 0.9550\n",
      "Epoch 1004/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2445 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2474 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2465 - categorical_accuracy: 0.9552\n",
      "Epoch 1005/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2376 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2442 - categorical_accuracy: 0.95 - 1s 52us/step - loss: 0.2462 - categorical_accuracy: 0.9555\n",
      "Epoch 1006/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2399 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2416 - categorical_accuracy: 0.95 - 1s 52us/step - loss: 0.2459 - categorical_accuracy: 0.9556\n",
      "Epoch 1007/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2460 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2446 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2456 - categorical_accuracy: 0.9556\n",
      "Epoch 1008/2000\n"
     ]
    },
    {
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2347 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2438 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2453 - categorical_accuracy: 0.9556\n",
      "Epoch 1009/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2471 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2450 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2450 - categorical_accuracy: 0.9556\n",
      "Epoch 1010/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2460 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2444 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2447 - categorical_accuracy: 0.9556\n",
      "Epoch 1011/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2385 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2456 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2445 - categorical_accuracy: 0.9558\n",
      "Epoch 1012/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2498 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2443 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2442 - categorical_accuracy: 0.9558\n",
      "Epoch 1013/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2397 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2400 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2439 - categorical_accuracy: 0.9558\n",
      "Epoch 1014/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2475 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2461 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2436 - categorical_accuracy: 0.9558\n",
      "Epoch 1015/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2507 - categorical_accuracy: 0.94 - ETA: 0s - loss: 0.2455 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2433 - categorical_accuracy: 0.9558\n",
      "Epoch 1016/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2439 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2466 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2431 - categorical_accuracy: 0.9558\n",
      "Epoch 1017/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2352 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2397 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2428 - categorical_accuracy: 0.9558\n",
      "Epoch 1018/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2400 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2406 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2425 - categorical_accuracy: 0.9558\n",
      "Epoch 1019/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2359 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2392 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2422 - categorical_accuracy: 0.9558\n",
      "Epoch 1020/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2420 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2413 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2419 - categorical_accuracy: 0.9558\n",
      "Epoch 1021/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2457 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2475 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2417 - categorical_accuracy: 0.9559\n",
      "Epoch 1022/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2423 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2372 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2414 - categorical_accuracy: 0.9559\n",
      "Epoch 1023/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2365 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2417 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2411 - categorical_accuracy: 0.9560\n",
      "Epoch 1024/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2323 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2395 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2408 - categorical_accuracy: 0.9559\n",
      "Epoch 1025/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2357 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2404 - categorical_accuracy: 0.95 - 1s 53us/step - loss: 0.2405 - categorical_accuracy: 0.9558\n",
      "Epoch 1026/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2452 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2445 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2403 - categorical_accuracy: 0.9558\n",
      "Epoch 1027/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2452 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2420 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2400 - categorical_accuracy: 0.9558\n",
      "Epoch 1028/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2495 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2440 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2397 - categorical_accuracy: 0.9559\n",
      "Epoch 1029/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2424 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2375 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2395 - categorical_accuracy: 0.9560\n",
      "Epoch 1030/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2444 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2403 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2392 - categorical_accuracy: 0.9560\n",
      "Epoch 1031/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2431 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2370 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2389 - categorical_accuracy: 0.9561\n",
      "Epoch 1032/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2445 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2385 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2386 - categorical_accuracy: 0.9562\n",
      "Epoch 1033/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2345 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2398 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2384 - categorical_accuracy: 0.9562\n",
      "Epoch 1034/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2410 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2384 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2381 - categorical_accuracy: 0.9562\n",
      "Epoch 1035/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2318 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2371 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2378 - categorical_accuracy: 0.9562\n",
      "Epoch 1036/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2454 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2401 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2376 - categorical_accuracy: 0.9562\n",
      "Epoch 1037/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2271 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2345 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2373 - categorical_accuracy: 0.9562\n",
      "Epoch 1038/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2366 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2376 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2370 - categorical_accuracy: 0.9563\n",
      "Epoch 1039/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2424 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2397 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2367 - categorical_accuracy: 0.9563\n",
      "Epoch 1040/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2386 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2370 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2365 - categorical_accuracy: 0.9562\n",
      "Epoch 1041/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2483 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2386 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2362 - categorical_accuracy: 0.9562\n",
      "Epoch 1042/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2362 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2347 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2359 - categorical_accuracy: 0.9562\n",
      "Epoch 1043/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2316 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2374 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2357 - categorical_accuracy: 0.9563\n",
      "Epoch 1044/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2421 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2355 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2354 - categorical_accuracy: 0.9563\n",
      "Epoch 1045/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2256 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2359 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2351 - categorical_accuracy: 0.9564\n",
      "Epoch 1046/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2382 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2377 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2349 - categorical_accuracy: 0.9565\n",
      "Epoch 1047/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2393 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2357 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2346 - categorical_accuracy: 0.9566\n",
      "Epoch 1048/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2338 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2366 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2343 - categorical_accuracy: 0.9566\n",
      "Epoch 1049/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2329 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2316 - categorical_accuracy: 0.95 - 0s 47us/step - loss: 0.2341 - categorical_accuracy: 0.9566\n",
      "Epoch 1050/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2304 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2346 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2338 - categorical_accuracy: 0.9566\n",
      "Epoch 1051/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2405 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2346 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2336 - categorical_accuracy: 0.9567\n",
      "Epoch 1052/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2331 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2315 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2333 - categorical_accuracy: 0.9567\n",
      "Epoch 1053/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2281 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2271 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2330 - categorical_accuracy: 0.9567\n",
      "Epoch 1054/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2313 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2340 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2328 - categorical_accuracy: 0.9567\n",
      "Epoch 1055/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2381 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2321 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2325 - categorical_accuracy: 0.9567\n",
      "Epoch 1056/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2294 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2302 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2322 - categorical_accuracy: 0.9568\n",
      "Epoch 1057/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2344 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2328 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2320 - categorical_accuracy: 0.9567\n",
      "Epoch 1058/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2346 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2329 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2317 - categorical_accuracy: 0.9567\n",
      "Epoch 1059/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2319 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2341 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2315 - categorical_accuracy: 0.9567\n",
      "Epoch 1060/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2298 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2305 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2312 - categorical_accuracy: 0.9567\n",
      "Epoch 1061/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2352 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2323 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2310 - categorical_accuracy: 0.9566\n",
      "Epoch 1062/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2336 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2344 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2307 - categorical_accuracy: 0.9567\n",
      "Epoch 1063/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2281 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2330 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2304 - categorical_accuracy: 0.9568\n",
      "Epoch 1064/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2302 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2312 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2302 - categorical_accuracy: 0.9570\n",
      "Epoch 1065/2000\n",
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      "Epoch 1066/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2233 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2277 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2297 - categorical_accuracy: 0.9571\n",
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      "Epoch 1068/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2246 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2297 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2289 - categorical_accuracy: 0.9574\n",
      "Epoch 1070/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2316 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2265 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2287 - categorical_accuracy: 0.9575\n",
      "Epoch 1071/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2241 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2264 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2284 - categorical_accuracy: 0.9574\n",
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      "Epoch 1074/2000\n",
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      "Epoch 1075/2000\n",
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      "Epoch 1077/2000\n",
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      "Epoch 1078/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2312 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2261 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2266 - categorical_accuracy: 0.9574\n",
      "Epoch 1079/2000\n",
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      "Epoch 1080/2000\n",
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      "Epoch 1081/2000\n",
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      "Epoch 1082/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2223 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2242 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2256 - categorical_accuracy: 0.9577\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2197 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2237 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2254 - categorical_accuracy: 0.9579\n",
      "Epoch 1084/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2345 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2261 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2251 - categorical_accuracy: 0.9579\n",
      "Epoch 1085/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2342 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2221 - categorical_accuracy: 0.95 - 0s 47us/step - loss: 0.2249 - categorical_accuracy: 0.9578\n",
      "Epoch 1086/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2279 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2246 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2246 - categorical_accuracy: 0.9578\n",
      "Epoch 1087/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2259 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2231 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2244 - categorical_accuracy: 0.9579\n",
      "Epoch 1088/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2237 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2241 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2242 - categorical_accuracy: 0.9579\n",
      "Epoch 1089/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2208 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2242 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2239 - categorical_accuracy: 0.9583\n",
      "Epoch 1090/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2253 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2228 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2237 - categorical_accuracy: 0.9584\n",
      "Epoch 1091/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2192 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2230 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2234 - categorical_accuracy: 0.9585\n",
      "Epoch 1092/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2224 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2245 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2232 - categorical_accuracy: 0.9587\n",
      "Epoch 1093/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2179 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2237 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2229 - categorical_accuracy: 0.9589\n",
      "Epoch 1094/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2192 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2219 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2227 - categorical_accuracy: 0.9589\n",
      "Epoch 1095/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2273 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2259 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2224 - categorical_accuracy: 0.9589\n",
      "Epoch 1096/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2206 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2216 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2222 - categorical_accuracy: 0.9588\n",
      "Epoch 1097/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2211 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2224 - categorical_accuracy: 0.95 - 1s 49us/step - loss: 0.2220 - categorical_accuracy: 0.9588\n",
      "Epoch 1098/2000\n",
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      "Epoch 1099/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2231 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2194 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2215 - categorical_accuracy: 0.9588\n",
      "Epoch 1100/2000\n",
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      "Epoch 1101/2000\n",
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      "Epoch 1102/2000\n",
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      "Epoch 1103/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2282 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2222 - categorical_accuracy: 0.95 - 1s 51us/step - loss: 0.2205 - categorical_accuracy: 0.9587\n",
      "Epoch 1104/2000\n",
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      "Epoch 1105/2000\n",
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      "Epoch 1106/2000\n",
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      "Epoch 1107/2000\n",
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      "Epoch 1108/2000\n",
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      "Epoch 1109/2000\n",
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      "Epoch 1110/2000\n",
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      "Epoch 1111/2000\n",
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      "Epoch 1112/2000\n",
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      "Epoch 1113/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2252 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2230 - categorical_accuracy: 0.95 - 1s 50us/step - loss: 0.2181 - categorical_accuracy: 0.9592\n",
      "Epoch 1114/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2125 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2177 - categorical_accuracy: 0.95 - 0s 49us/step - loss: 0.2179 - categorical_accuracy: 0.9591\n",
      "Epoch 1115/2000\n",
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      "Epoch 1116/2000\n",
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      "Epoch 1117/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2206 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2183 - categorical_accuracy: 0.95 - 0s 47us/step - loss: 0.2172 - categorical_accuracy: 0.9594\n",
      "Epoch 1118/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2135 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2139 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.2170 - categorical_accuracy: 0.9593\n",
      "Epoch 1119/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2236 - categorical_accuracy: 0.95 - ETA: 0s - loss: 0.2188 - categorical_accuracy: 0.95 - 0s 48us/step - loss: 0.2167 - categorical_accuracy: 0.9593\n",
      "Epoch 1120/2000\n",
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      "Epoch 1121/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2174 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2146 - categorical_accuracy: 0.96 - 0s 47us/step - loss: 0.2163 - categorical_accuracy: 0.9596\n",
      "Epoch 1122/2000\n",
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      "Epoch 1123/2000\n",
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      "Epoch 1128/2000\n",
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      "Epoch 1129/2000\n",
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      "Epoch 1130/2000\n",
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      "Epoch 1131/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2153 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2133 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.2139 - categorical_accuracy: 0.9599\n",
      "Epoch 1132/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2113 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2128 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.2137 - categorical_accuracy: 0.9600\n",
      "Epoch 1133/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2098 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2138 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.2135 - categorical_accuracy: 0.9602\n",
      "Epoch 1134/2000\n",
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      "Epoch 1135/2000\n",
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      "Epoch 1138/2000\n",
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      "Epoch 1139/2000\n",
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      "Epoch 1140/2000\n",
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      "Epoch 1141/2000\n",
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      "Epoch 1143/2000\n",
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      "Epoch 1145/2000\n",
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      "Epoch 1147/2000\n",
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      "Epoch 1148/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2042 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2101 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.2101 - categorical_accuracy: 0.9607\n",
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      "Epoch 1183/2000\n"
     ]
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2044 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2060 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.2025 - categorical_accuracy: 0.9622\n",
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      "Epoch 1200/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.2028 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2001 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1988 - categorical_accuracy: 0.9628\n",
      "Epoch 1202/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2002 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1979 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1986 - categorical_accuracy: 0.9628\n",
      "Epoch 1203/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1988 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1998 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1984 - categorical_accuracy: 0.9628\n",
      "Epoch 1204/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2010 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1994 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1982 - categorical_accuracy: 0.9628\n",
      "Epoch 1205/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1946 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1934 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1980 - categorical_accuracy: 0.9633\n",
      "Epoch 1206/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1976 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.2005 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1978 - categorical_accuracy: 0.9633\n",
      "Epoch 1207/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1970 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1952 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1975 - categorical_accuracy: 0.9634\n",
      "Epoch 1208/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2044 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1973 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1973 - categorical_accuracy: 0.9635\n",
      "Epoch 1209/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1904 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1945 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1971 - categorical_accuracy: 0.9636\n",
      "Epoch 1210/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1951 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1959 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1969 - categorical_accuracy: 0.9636\n",
      "Epoch 1211/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1992 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1993 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1967 - categorical_accuracy: 0.9635\n",
      "Epoch 1212/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.2043 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1988 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1965 - categorical_accuracy: 0.9634\n",
      "Epoch 1213/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1950 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1966 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1963 - categorical_accuracy: 0.9635\n",
      "Epoch 1214/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1887 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1959 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1961 - categorical_accuracy: 0.9635\n",
      "Epoch 1215/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1916 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1928 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1959 - categorical_accuracy: 0.9635\n",
      "Epoch 1216/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1965 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1982 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1957 - categorical_accuracy: 0.9635\n",
      "Epoch 1217/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1978 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1982 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1955 - categorical_accuracy: 0.9637\n",
      "Epoch 1218/2000\n"
     ]
    },
    {
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1912 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1919 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1953 - categorical_accuracy: 0.9638\n",
      "Epoch 1219/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1975 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1939 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1951 - categorical_accuracy: 0.9636\n",
      "Epoch 1220/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1981 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1943 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1949 - categorical_accuracy: 0.9638\n",
      "Epoch 1221/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1940 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1969 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1947 - categorical_accuracy: 0.9638\n",
      "Epoch 1222/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1927 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1959 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1945 - categorical_accuracy: 0.9637\n",
      "Epoch 1223/2000\n",
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      "Epoch 1224/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1964 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1926 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1941 - categorical_accuracy: 0.9639\n",
      "Epoch 1225/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1805 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1936 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1939 - categorical_accuracy: 0.9642\n",
      "Epoch 1226/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1947 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1935 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1937 - categorical_accuracy: 0.9644\n",
      "Epoch 1227/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1865 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1924 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1935 - categorical_accuracy: 0.9644\n",
      "Epoch 1228/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1888 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1917 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1933 - categorical_accuracy: 0.9644\n",
      "Epoch 1229/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1903 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1926 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1931 - categorical_accuracy: 0.9643\n",
      "Epoch 1230/2000\n",
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      "Epoch 1231/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1959 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1934 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1928 - categorical_accuracy: 0.9645\n",
      "Epoch 1232/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1994 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1921 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1926 - categorical_accuracy: 0.9646\n",
      "Epoch 1233/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1969 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1920 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1924 - categorical_accuracy: 0.9645\n",
      "Epoch 1234/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1992 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1956 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1922 - categorical_accuracy: 0.9646\n",
      "Epoch 1235/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1952 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1930 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1920 - categorical_accuracy: 0.9646\n",
      "Epoch 1236/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1945 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1927 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1918 - categorical_accuracy: 0.9646\n",
      "Epoch 1237/2000\n",
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      "Epoch 1238/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1906 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1895 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1914 - categorical_accuracy: 0.9646\n",
      "Epoch 1239/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1884 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1885 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1912 - categorical_accuracy: 0.9646\n",
      "Epoch 1240/2000\n",
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      "Epoch 1243/2000\n",
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      "Epoch 1246/2000\n",
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      "Epoch 1253/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1924 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1904 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1885 - categorical_accuracy: 0.9651\n",
      "Epoch 1254/2000\n",
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      "Epoch 1255/2000\n",
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      "Epoch 1260/2000\n",
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      "Epoch 1261/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1833 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1855 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1868 - categorical_accuracy: 0.9653\n",
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      "Epoch 1265/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1819 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1880 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1862 - categorical_accuracy: 0.9655\n",
      "Epoch 1266/2000\n",
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      "Epoch 1269/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1890 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1861 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1825 - categorical_accuracy: 0.9665\n",
      "Epoch 1286/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1767 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1823 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1824 - categorical_accuracy: 0.9665\n",
      "Epoch 1287/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1803 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1822 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1822 - categorical_accuracy: 0.9666\n",
      "Epoch 1288/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1857 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1840 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1820 - categorical_accuracy: 0.9667\n",
      "Epoch 1289/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1807 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1817 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1818 - categorical_accuracy: 0.9667\n",
      "Epoch 1290/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1787 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1828 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1816 - categorical_accuracy: 0.9668\n",
      "Epoch 1291/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1856 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1825 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1815 - categorical_accuracy: 0.9668\n",
      "Epoch 1292/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1830 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1817 - categorical_accuracy: 0.96 - 0s 47us/step - loss: 0.1813 - categorical_accuracy: 0.9667\n",
      "Epoch 1293/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1787 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1806 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1811 - categorical_accuracy: 0.9668\n",
      "Epoch 1294/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1767 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1807 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1809 - categorical_accuracy: 0.9670\n",
      "Epoch 1295/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1790 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1774 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1807 - categorical_accuracy: 0.9670\n",
      "Epoch 1296/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1771 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1789 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1806 - categorical_accuracy: 0.9669\n",
      "Epoch 1297/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1849 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1786 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1804 - categorical_accuracy: 0.9669\n",
      "Epoch 1298/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1823 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1811 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1802 - categorical_accuracy: 0.9669\n",
      "Epoch 1299/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1821 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1813 - categorical_accuracy: 0.96 - 1s 52us/step - loss: 0.1800 - categorical_accuracy: 0.9669\n",
      "Epoch 1300/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1820 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1806 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1799 - categorical_accuracy: 0.9670\n",
      "Epoch 1301/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1794 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1790 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1797 - categorical_accuracy: 0.9671\n",
      "Epoch 1302/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1837 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1798 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1795 - categorical_accuracy: 0.9672\n",
      "Epoch 1303/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1783 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1800 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1793 - categorical_accuracy: 0.9671\n",
      "Epoch 1304/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1751 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1809 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1791 - categorical_accuracy: 0.9671\n",
      "Epoch 1305/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1813 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1795 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1790 - categorical_accuracy: 0.9671\n",
      "Epoch 1306/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1749 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1785 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1788 - categorical_accuracy: 0.9671\n",
      "Epoch 1307/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1741 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1778 - categorical_accuracy: 0.96 - 1s 54us/step - loss: 0.1786 - categorical_accuracy: 0.9672\n",
      "Epoch 1308/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1785 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1771 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1784 - categorical_accuracy: 0.9675\n",
      "Epoch 1309/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1720 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1775 - categorical_accuracy: 0.96 - 1s 59us/step - loss: 0.1783 - categorical_accuracy: 0.9674\n",
      "Epoch 1310/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1824 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1815 - categorical_accuracy: 0.96 - 1s 60us/step - loss: 0.1781 - categorical_accuracy: 0.9674\n",
      "Epoch 1311/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1705 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1763 - categorical_accuracy: 0.96 - 1s 56us/step - loss: 0.1779 - categorical_accuracy: 0.9675\n",
      "Epoch 1312/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1780 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1767 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1777 - categorical_accuracy: 0.9676\n",
      "Epoch 1313/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1750 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1757 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1776 - categorical_accuracy: 0.9678\n",
      "Epoch 1314/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1738 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1764 - categorical_accuracy: 0.96 - 1s 56us/step - loss: 0.1774 - categorical_accuracy: 0.9678\n",
      "Epoch 1315/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1791 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1788 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1772 - categorical_accuracy: 0.9679\n",
      "Epoch 1316/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1820 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1786 - categorical_accuracy: 0.96 - 1s 54us/step - loss: 0.1770 - categorical_accuracy: 0.9679\n",
      "Epoch 1317/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1866 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1757 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1769 - categorical_accuracy: 0.9679\n",
      "Epoch 1318/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1803 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1758 - categorical_accuracy: 0.96 - 1s 63us/step - loss: 0.1767 - categorical_accuracy: 0.9679\n",
      "Epoch 1319/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1788 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1761 - categorical_accuracy: 0.96 - 1s 63us/step - loss: 0.1765 - categorical_accuracy: 0.9680\n",
      "Epoch 1320/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1817 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1749 - categorical_accuracy: 0.96 - 1s 63us/step - loss: 0.1763 - categorical_accuracy: 0.9679\n",
      "Epoch 1321/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1715 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1752 - categorical_accuracy: 0.96 - 1s 64us/step - loss: 0.1762 - categorical_accuracy: 0.9679\n",
      "Epoch 1322/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1662 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1754 - categorical_accuracy: 0.96 - 1s 63us/step - loss: 0.1760 - categorical_accuracy: 0.9679\n",
      "Epoch 1323/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1781 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1726 - categorical_accuracy: 0.96 - 1s 61us/step - loss: 0.1758 - categorical_accuracy: 0.9680\n",
      "Epoch 1324/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1744 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1754 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1756 - categorical_accuracy: 0.9680\n",
      "Epoch 1325/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1775 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1743 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1755 - categorical_accuracy: 0.9680\n",
      "Epoch 1326/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1657 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1727 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1753 - categorical_accuracy: 0.9682\n",
      "Epoch 1327/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1741 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1748 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1751 - categorical_accuracy: 0.9684\n",
      "Epoch 1328/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1837 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1757 - categorical_accuracy: 0.96 - 1s 51us/step - loss: 0.1750 - categorical_accuracy: 0.9684\n",
      "Epoch 1329/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1774 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1726 - categorical_accuracy: 0.96 - 1s 56us/step - loss: 0.1748 - categorical_accuracy: 0.9684\n",
      "Epoch 1330/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1754 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1736 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1746 - categorical_accuracy: 0.9684\n",
      "Epoch 1331/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1755 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1746 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1744 - categorical_accuracy: 0.9686\n",
      "Epoch 1332/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1719 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1732 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1743 - categorical_accuracy: 0.9686\n",
      "Epoch 1333/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1829 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1776 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1741 - categorical_accuracy: 0.9686\n",
      "Epoch 1334/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1728 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1737 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1739 - categorical_accuracy: 0.9686\n",
      "Epoch 1335/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1727 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1733 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1738 - categorical_accuracy: 0.9686\n",
      "Epoch 1336/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1787 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1761 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1736 - categorical_accuracy: 0.9688\n",
      "Epoch 1337/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1705 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1739 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1734 - categorical_accuracy: 0.9688\n",
      "Epoch 1338/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1833 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1774 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1732 - categorical_accuracy: 0.9688\n",
      "Epoch 1339/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1739 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1739 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1731 - categorical_accuracy: 0.9689\n",
      "Epoch 1340/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1746 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1724 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1729 - categorical_accuracy: 0.9690\n",
      "Epoch 1341/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1728 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1731 - categorical_accuracy: 0.96 - 1s 52us/step - loss: 0.1727 - categorical_accuracy: 0.9690\n",
      "Epoch 1342/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1797 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1741 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1726 - categorical_accuracy: 0.9690\n",
      "Epoch 1343/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1778 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1722 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1724 - categorical_accuracy: 0.9690\n",
      "Epoch 1344/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1641 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1704 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1722 - categorical_accuracy: 0.9692\n",
      "Epoch 1345/2000\n",
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      "Epoch 1346/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1755 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1735 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1719 - categorical_accuracy: 0.9693\n",
      "Epoch 1347/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1723 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1707 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1717 - categorical_accuracy: 0.9693\n",
      "Epoch 1348/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1683 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1686 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1716 - categorical_accuracy: 0.9693\n",
      "Epoch 1349/2000\n",
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      "Epoch 1350/2000\n",
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      "Epoch 1351/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1683 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1717 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1711 - categorical_accuracy: 0.9692\n",
      "Epoch 1352/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1715 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1729 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1709 - categorical_accuracy: 0.9695\n",
      "Epoch 1353/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1726 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1732 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1707 - categorical_accuracy: 0.9695\n",
      "Epoch 1354/2000\n",
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      "Epoch 1355/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1714 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1753 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1704 - categorical_accuracy: 0.9694\n",
      "Epoch 1356/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1606 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1682 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1702 - categorical_accuracy: 0.9694\n",
      "Epoch 1357/2000\n",
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      "Epoch 1358/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1673 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1688 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1699 - categorical_accuracy: 0.9694\n",
      "Epoch 1359/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1657 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1684 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1697 - categorical_accuracy: 0.9695\n",
      "Epoch 1360/2000\n",
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      "Epoch 1361/2000\n",
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      "Epoch 1362/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1674 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1677 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1693 - categorical_accuracy: 0.9696\n",
      "Epoch 1363/2000\n",
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      "Epoch 1364/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1742 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1697 - categorical_accuracy: 0.96 - 0s 47us/step - loss: 0.1689 - categorical_accuracy: 0.9697\n",
      "Epoch 1365/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1640 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1688 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1688 - categorical_accuracy: 0.9697\n",
      "Epoch 1366/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1663 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1715 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1686 - categorical_accuracy: 0.9698\n",
      "Epoch 1367/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1685 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1701 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1684 - categorical_accuracy: 0.9698\n",
      "Epoch 1368/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1684 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1676 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1683 - categorical_accuracy: 0.9698\n",
      "Epoch 1369/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1753 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1709 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1681 - categorical_accuracy: 0.9698\n",
      "Epoch 1370/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1674 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1684 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1680 - categorical_accuracy: 0.9697\n",
      "Epoch 1371/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1769 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1695 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1678 - categorical_accuracy: 0.9699\n",
      "Epoch 1372/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1695 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1706 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1676 - categorical_accuracy: 0.9698\n",
      "Epoch 1373/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1713 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1671 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1675 - categorical_accuracy: 0.9698\n",
      "Epoch 1374/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1675 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1649 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1673 - categorical_accuracy: 0.9699\n",
      "Epoch 1375/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1658 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1675 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1671 - categorical_accuracy: 0.9700\n",
      "Epoch 1376/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1728 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1683 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1670 - categorical_accuracy: 0.9700\n",
      "Epoch 1377/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1668 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1677 - categorical_accuracy: 0.96 - 0s 49us/step - loss: 0.1668 - categorical_accuracy: 0.9701\n",
      "Epoch 1378/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1714 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1654 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1667 - categorical_accuracy: 0.9699\n",
      "Epoch 1379/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1621 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1643 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1665 - categorical_accuracy: 0.9700\n",
      "Epoch 1380/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1735 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1687 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1663 - categorical_accuracy: 0.9701\n",
      "Epoch 1381/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1657 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1676 - categorical_accuracy: 0.97 - 1s 63us/step - loss: 0.1662 - categorical_accuracy: 0.9701\n",
      "Epoch 1382/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1750 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1685 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1660 - categorical_accuracy: 0.9701\n",
      "Epoch 1383/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1693 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1665 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1659 - categorical_accuracy: 0.9701\n",
      "Epoch 1384/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1622 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1655 - categorical_accuracy: 0.96 - 1s 57us/step - loss: 0.1657 - categorical_accuracy: 0.9700\n",
      "Epoch 1385/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1584 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1620 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1655 - categorical_accuracy: 0.9701\n",
      "Epoch 1386/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1672 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1644 - categorical_accuracy: 0.96 - 1s 56us/step - loss: 0.1654 - categorical_accuracy: 0.9702\n",
      "Epoch 1387/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1612 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1658 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1652 - categorical_accuracy: 0.9702\n",
      "Epoch 1388/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1719 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1658 - categorical_accuracy: 0.97 - 1s 56us/step - loss: 0.1651 - categorical_accuracy: 0.9703\n",
      "Epoch 1389/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1682 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1654 - categorical_accuracy: 0.96 - 1s 56us/step - loss: 0.1649 - categorical_accuracy: 0.9704\n",
      "Epoch 1390/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1685 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1628 - categorical_accuracy: 0.97 - 1s 58us/step - loss: 0.1647 - categorical_accuracy: 0.9702\n",
      "Epoch 1391/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1716 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1669 - categorical_accuracy: 0.96 - 1s 57us/step - loss: 0.1646 - categorical_accuracy: 0.9702\n",
      "Epoch 1392/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1683 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1670 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1644 - categorical_accuracy: 0.9703\n",
      "Epoch 1393/2000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1615 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1659 - categorical_accuracy: 0.96 - 1s 55us/step - loss: 0.1643 - categorical_accuracy: 0.9703\n",
      "Epoch 1394/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1639 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1659 - categorical_accuracy: 0.96 - 1s 54us/step - loss: 0.1641 - categorical_accuracy: 0.9704\n",
      "Epoch 1395/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1658 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1655 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1640 - categorical_accuracy: 0.9704\n",
      "Epoch 1396/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1546 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1615 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1638 - categorical_accuracy: 0.9704\n",
      "Epoch 1397/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1668 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1654 - categorical_accuracy: 0.96 - 1s 53us/step - loss: 0.1636 - categorical_accuracy: 0.9704\n",
      "Epoch 1398/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1676 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1635 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1635 - categorical_accuracy: 0.9704\n",
      "Epoch 1399/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1610 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1637 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1633 - categorical_accuracy: 0.9704\n",
      "Epoch 1400/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1573 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1618 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1632 - categorical_accuracy: 0.9705\n",
      "Epoch 1401/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1565 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1617 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1630 - categorical_accuracy: 0.9705\n",
      "Epoch 1402/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1582 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1630 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1629 - categorical_accuracy: 0.9704\n",
      "Epoch 1403/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1637 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1632 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1627 - categorical_accuracy: 0.9704\n",
      "Epoch 1404/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1649 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1632 - categorical_accuracy: 0.96 - 1s 49us/step - loss: 0.1625 - categorical_accuracy: 0.9703\n",
      "Epoch 1405/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1638 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1623 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1624 - categorical_accuracy: 0.9703\n",
      "Epoch 1406/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1607 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1638 - categorical_accuracy: 0.96 - 1s 50us/step - loss: 0.1622 - categorical_accuracy: 0.9704\n",
      "Epoch 1407/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1641 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1609 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1621 - categorical_accuracy: 0.9706\n",
      "Epoch 1408/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1653 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1615 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1619 - categorical_accuracy: 0.9707\n",
      "Epoch 1409/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1565 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1630 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1618 - categorical_accuracy: 0.9708\n",
      "Epoch 1410/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1612 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1607 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1616 - categorical_accuracy: 0.9708\n",
      "Epoch 1411/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1600 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1600 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1615 - categorical_accuracy: 0.9708\n",
      "Epoch 1412/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1633 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1619 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1613 - categorical_accuracy: 0.9709\n",
      "Epoch 1413/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1590 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1597 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1612 - categorical_accuracy: 0.9709\n",
      "Epoch 1414/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1661 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1618 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1610 - categorical_accuracy: 0.9708\n",
      "Epoch 1415/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1620 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1596 - categorical_accuracy: 0.96 - 0s 48us/step - loss: 0.1608 - categorical_accuracy: 0.9708\n",
      "Epoch 1416/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1648 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1618 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1607 - categorical_accuracy: 0.9709\n",
      "Epoch 1417/2000\n",
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      "Epoch 1418/2000\n",
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      "Epoch 1419/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1654 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1608 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1602 - categorical_accuracy: 0.9708\n",
      "Epoch 1420/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1548 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1597 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1601 - categorical_accuracy: 0.9709\n",
      "Epoch 1421/2000\n",
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      "Epoch 1422/2000\n",
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      "Epoch 1423/2000\n",
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      "Epoch 1424/2000\n",
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      "Epoch 1425/2000\n",
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      "Epoch 1426/2000\n",
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      "Epoch 1427/2000\n",
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      "Epoch 1428/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1622 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1596 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1589 - categorical_accuracy: 0.9715\n",
      "Epoch 1429/2000\n",
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      "Epoch 1430/2000\n",
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      "Epoch 1431/2000\n",
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      "Epoch 1432/2000\n",
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      "Epoch 1433/2000\n",
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      "Epoch 1434/2000\n",
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      "Epoch 1435/2000\n",
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      "Epoch 1436/2000\n",
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      "Epoch 1437/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1534 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1575 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1575 - categorical_accuracy: 0.9716\n",
      "Epoch 1438/2000\n",
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      "Epoch 1439/2000\n",
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      "Epoch 1440/2000\n",
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      "Epoch 1441/2000\n",
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      "Epoch 1442/2000\n",
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      "Epoch 1443/2000\n",
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      "Epoch 1444/2000\n",
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      "Epoch 1445/2000\n",
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      "Epoch 1446/2000\n",
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      "Epoch 1448/2000\n",
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      "Epoch 1449/2000\n",
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      "Epoch 1450/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1588 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1526 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1554 - categorical_accuracy: 0.9721\n",
      "Epoch 1452/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1567 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1562 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1553 - categorical_accuracy: 0.9722\n",
      "Epoch 1453/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1517 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1565 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1551 - categorical_accuracy: 0.9720\n",
      "Epoch 1454/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1601 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1549 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1550 - categorical_accuracy: 0.9721\n",
      "Epoch 1455/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1574 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1535 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1549 - categorical_accuracy: 0.9721\n",
      "Epoch 1456/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1501 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1523 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1547 - categorical_accuracy: 0.9720\n",
      "Epoch 1457/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1492 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1526 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1546 - categorical_accuracy: 0.9720\n",
      "Epoch 1458/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1568 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1559 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1544 - categorical_accuracy: 0.9720\n",
      "Epoch 1459/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1577 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1545 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1543 - categorical_accuracy: 0.9720\n",
      "Epoch 1460/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1550 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1549 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1541 - categorical_accuracy: 0.9720\n",
      "Epoch 1461/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1559 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1529 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1540 - categorical_accuracy: 0.9724\n",
      "Epoch 1462/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1545 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1512 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1538 - categorical_accuracy: 0.9722\n",
      "Epoch 1463/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1620 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1549 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1537 - categorical_accuracy: 0.9724\n",
      "Epoch 1464/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1566 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1564 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1535 - categorical_accuracy: 0.9725\n",
      "Epoch 1465/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1528 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1525 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1534 - categorical_accuracy: 0.9725\n",
      "Epoch 1466/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1611 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1578 - categorical_accuracy: 0.97 - 0s 47us/step - loss: 0.1533 - categorical_accuracy: 0.9725\n",
      "Epoch 1467/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1589 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1532 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1531 - categorical_accuracy: 0.9724\n",
      "Epoch 1468/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1505 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1512 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1530 - categorical_accuracy: 0.9724\n",
      "Epoch 1469/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1516 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1511 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1528 - categorical_accuracy: 0.9724\n",
      "Epoch 1470/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1551 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1542 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1527 - categorical_accuracy: 0.9724\n",
      "Epoch 1471/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1495 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1516 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1525 - categorical_accuracy: 0.9725\n",
      "Epoch 1472/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1499 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1517 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1524 - categorical_accuracy: 0.9725\n",
      "Epoch 1473/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1522 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1504 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1522 - categorical_accuracy: 0.9726\n",
      "Epoch 1474/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1486 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1541 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1521 - categorical_accuracy: 0.9726\n",
      "Epoch 1475/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1582 - categorical_accuracy: 0.96 - ETA: 0s - loss: 0.1535 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1520 - categorical_accuracy: 0.9727\n",
      "Epoch 1476/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1523 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1532 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1518 - categorical_accuracy: 0.9727\n",
      "Epoch 1477/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1524 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1546 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1517 - categorical_accuracy: 0.9727\n",
      "Epoch 1478/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1512 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1509 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1515 - categorical_accuracy: 0.9727\n",
      "Epoch 1479/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1539 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1516 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1514 - categorical_accuracy: 0.9728\n",
      "Epoch 1480/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1560 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1512 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1512 - categorical_accuracy: 0.9730\n",
      "Epoch 1481/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1544 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1486 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1511 - categorical_accuracy: 0.9731\n",
      "Epoch 1482/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1475 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1495 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1510 - categorical_accuracy: 0.9731\n",
      "Epoch 1483/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1503 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1505 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1508 - categorical_accuracy: 0.9730\n",
      "Epoch 1484/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1526 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1533 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1507 - categorical_accuracy: 0.9732\n",
      "Epoch 1485/2000\n",
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      "Epoch 1486/2000\n",
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      "Epoch 1487/2000\n",
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      "Epoch 1488/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1500 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1486 - categorical_accuracy: 0.97 - 1s 58us/step - loss: 0.1501 - categorical_accuracy: 0.9735\n",
      "Epoch 1489/2000\n",
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      "Epoch 1490/2000\n",
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      "Epoch 1491/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1478 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1492 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1497 - categorical_accuracy: 0.9739\n",
      "Epoch 1492/2000\n",
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      "Epoch 1493/2000\n",
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      "Epoch 1495/2000\n",
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      "Epoch 1497/2000\n",
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      "Epoch 1498/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1476 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1482 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1487 - categorical_accuracy: 0.9737\n",
      "Epoch 1499/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1500 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1464 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1486 - categorical_accuracy: 0.9739\n",
      "Epoch 1500/2000\n",
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      "Epoch 1501/2000\n",
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      "Epoch 1502/2000\n",
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      "Epoch 1504/2000\n",
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      "Epoch 1505/2000\n",
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      "Epoch 1506/2000\n",
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      "Epoch 1508/2000\n",
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      "Epoch 1510/2000\n",
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      "Epoch 1512/2000\n",
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      "Epoch 1514/2000\n",
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      "Epoch 1515/2000\n",
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      "Epoch 1516/2000\n",
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      "Epoch 1532/2000\n",
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      "Epoch 1533/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1371 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1394 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1439 - categorical_accuracy: 0.9745\n",
      "Epoch 1534/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1379 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1439 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1438 - categorical_accuracy: 0.9745\n",
      "Epoch 1535/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1402 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1427 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1437 - categorical_accuracy: 0.9746\n",
      "Epoch 1536/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1424 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1443 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1435 - categorical_accuracy: 0.9748\n",
      "Epoch 1537/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1430 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1427 - categorical_accuracy: 0.97 - 1s 57us/step - loss: 0.1434 - categorical_accuracy: 0.9748\n",
      "Epoch 1538/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1395 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1399 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1433 - categorical_accuracy: 0.9747\n",
      "Epoch 1539/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1458 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1463 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1431 - categorical_accuracy: 0.9747\n",
      "Epoch 1540/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1444 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1431 - categorical_accuracy: 0.97 - 0s 47us/step - loss: 0.1430 - categorical_accuracy: 0.9745\n",
      "Epoch 1541/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1436 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1411 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1429 - categorical_accuracy: 0.9745\n",
      "Epoch 1542/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1427 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1452 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1427 - categorical_accuracy: 0.9745\n",
      "Epoch 1543/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1446 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1419 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1426 - categorical_accuracy: 0.9747\n",
      "Epoch 1544/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1426 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1414 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1425 - categorical_accuracy: 0.9748\n",
      "Epoch 1545/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1490 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1430 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1423 - categorical_accuracy: 0.9748\n",
      "Epoch 1546/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1408 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1416 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1422 - categorical_accuracy: 0.9748\n",
      "Epoch 1547/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1415 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1429 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1421 - categorical_accuracy: 0.9748\n",
      "Epoch 1548/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1406 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1421 - categorical_accuracy: 0.97 - 1s 56us/step - loss: 0.1419 - categorical_accuracy: 0.9749\n",
      "Epoch 1549/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1364 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1416 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1418 - categorical_accuracy: 0.9749\n",
      "Epoch 1550/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1394 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1407 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1417 - categorical_accuracy: 0.9750\n",
      "Epoch 1551/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1402 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1424 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1416 - categorical_accuracy: 0.9750\n",
      "Epoch 1552/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1477 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1446 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1414 - categorical_accuracy: 0.9750\n",
      "Epoch 1553/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1373 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1422 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1413 - categorical_accuracy: 0.9750\n",
      "Epoch 1554/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1411 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1401 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1412 - categorical_accuracy: 0.9750\n",
      "Epoch 1555/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1454 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1412 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1410 - categorical_accuracy: 0.9752\n",
      "Epoch 1556/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1369 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1396 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1409 - categorical_accuracy: 0.9751\n",
      "Epoch 1557/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1325 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1379 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1408 - categorical_accuracy: 0.9751\n",
      "Epoch 1558/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1435 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1404 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1406 - categorical_accuracy: 0.9752\n",
      "Epoch 1559/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1402 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1414 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1405 - categorical_accuracy: 0.9752\n",
      "Epoch 1560/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1427 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1395 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1404 - categorical_accuracy: 0.9752\n",
      "Epoch 1561/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1414 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1407 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1403 - categorical_accuracy: 0.9753\n",
      "Epoch 1562/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1398 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1378 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1401 - categorical_accuracy: 0.9753\n",
      "Epoch 1563/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1403 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1380 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1400 - categorical_accuracy: 0.9753\n",
      "Epoch 1564/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1436 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1410 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1399 - categorical_accuracy: 0.9753\n",
      "Epoch 1565/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1368 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1393 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1397 - categorical_accuracy: 0.9753\n",
      "Epoch 1566/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1390 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1400 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1396 - categorical_accuracy: 0.9754\n",
      "Epoch 1567/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1371 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1405 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1395 - categorical_accuracy: 0.9756\n",
      "Epoch 1568/2000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1376 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1388 - categorical_accuracy: 0.97 - 1s 56us/step - loss: 0.1393 - categorical_accuracy: 0.9755\n",
      "Epoch 1569/2000\n",
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      "Epoch 1570/2000\n",
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      "Epoch 1571/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1437 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1383 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1390 - categorical_accuracy: 0.9755\n",
      "Epoch 1572/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1444 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1388 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1388 - categorical_accuracy: 0.9755\n",
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      "Epoch 1574/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1365 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1399 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1386 - categorical_accuracy: 0.9757\n",
      "Epoch 1575/2000\n",
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      "Epoch 1576/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1379 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1385 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1383 - categorical_accuracy: 0.9756\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1356 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1386 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1382 - categorical_accuracy: 0.9756\n",
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      "Epoch 1579/2000\n",
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      "Epoch 1580/2000\n",
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      "Epoch 1581/2000\n",
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      "Epoch 1582/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1403 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1377 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1375 - categorical_accuracy: 0.9760\n",
      "Epoch 1583/2000\n",
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      "Epoch 1585/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1339 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1354 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1372 - categorical_accuracy: 0.9761\n",
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      "Epoch 1587/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1363 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1371 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1367 - categorical_accuracy: 0.9759\n",
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      "Epoch 1600/2000\n",
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      "Epoch 1602/2000\n",
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      "Epoch 1603/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1337 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1316 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1349 - categorical_accuracy: 0.9764\n",
      "Epoch 1604/2000\n",
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      "Epoch 1616/2000\n",
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1402 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1348 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1332 - categorical_accuracy: 0.9769\n",
      "Epoch 1618/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1340 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1353 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1331 - categorical_accuracy: 0.9770\n",
      "Epoch 1619/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1293 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1328 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1330 - categorical_accuracy: 0.9772\n",
      "Epoch 1620/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1299 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1340 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1328 - categorical_accuracy: 0.9773\n",
      "Epoch 1621/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1361 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1331 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1327 - categorical_accuracy: 0.9773\n",
      "Epoch 1622/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1334 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1317 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1326 - categorical_accuracy: 0.9773\n",
      "Epoch 1623/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1323 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1334 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1325 - categorical_accuracy: 0.9773\n",
      "Epoch 1624/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1300 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1305 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1324 - categorical_accuracy: 0.9774\n",
      "Epoch 1625/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1316 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1307 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1322 - categorical_accuracy: 0.9774\n",
      "Epoch 1626/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1302 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1312 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1321 - categorical_accuracy: 0.9775\n",
      "Epoch 1627/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1298 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1297 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1320 - categorical_accuracy: 0.9775\n",
      "Epoch 1628/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1343 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1312 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1319 - categorical_accuracy: 0.9775\n",
      "Epoch 1629/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1367 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1323 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1317 - categorical_accuracy: 0.9776\n",
      "Epoch 1630/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1348 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1328 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1316 - categorical_accuracy: 0.9776\n",
      "Epoch 1631/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1319 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1319 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1315 - categorical_accuracy: 0.9776\n",
      "Epoch 1632/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1254 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1290 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1314 - categorical_accuracy: 0.9776\n",
      "Epoch 1633/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1305 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1312 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1313 - categorical_accuracy: 0.9776\n",
      "Epoch 1634/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1297 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1337 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1311 - categorical_accuracy: 0.9776\n",
      "Epoch 1635/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1346 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1314 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1310 - categorical_accuracy: 0.9778\n",
      "Epoch 1636/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1290 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1307 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1309 - categorical_accuracy: 0.9778\n",
      "Epoch 1637/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1307 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1333 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1308 - categorical_accuracy: 0.9777\n",
      "Epoch 1638/2000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1323 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1318 - categorical_accuracy: 0.97 - 1s 56us/step - loss: 0.1307 - categorical_accuracy: 0.9779\n",
      "Epoch 1639/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1315 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1299 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1305 - categorical_accuracy: 0.9779\n",
      "Epoch 1640/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1296 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1303 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1304 - categorical_accuracy: 0.9780\n",
      "Epoch 1641/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1299 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1301 - categorical_accuracy: 0.97 - 0s 47us/step - loss: 0.1303 - categorical_accuracy: 0.9780\n",
      "Epoch 1642/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1362 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1312 - categorical_accuracy: 0.97 - 0s 47us/step - loss: 0.1302 - categorical_accuracy: 0.9780\n",
      "Epoch 1643/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1341 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1298 - categorical_accuracy: 0.97 - 0s 47us/step - loss: 0.1301 - categorical_accuracy: 0.9780\n",
      "Epoch 1644/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1360 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1306 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1300 - categorical_accuracy: 0.9781\n",
      "Epoch 1645/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1227 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1317 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1298 - categorical_accuracy: 0.9782\n",
      "Epoch 1646/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1310 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1300 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1297 - categorical_accuracy: 0.9782\n",
      "Epoch 1647/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1303 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1297 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1296 - categorical_accuracy: 0.9782\n",
      "Epoch 1648/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1251 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1276 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1295 - categorical_accuracy: 0.9782\n",
      "Epoch 1649/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1288 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1300 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1294 - categorical_accuracy: 0.9782\n",
      "Epoch 1650/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1308 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1289 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1292 - categorical_accuracy: 0.9782\n",
      "Epoch 1651/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1310 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1263 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1291 - categorical_accuracy: 0.9783\n",
      "Epoch 1652/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1282 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1284 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1290 - categorical_accuracy: 0.9783\n",
      "Epoch 1653/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1266 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1303 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1289 - categorical_accuracy: 0.9785\n",
      "Epoch 1654/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1300 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1290 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1288 - categorical_accuracy: 0.9786\n",
      "Epoch 1655/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1293 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1292 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1287 - categorical_accuracy: 0.9786\n",
      "Epoch 1656/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1269 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1265 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1285 - categorical_accuracy: 0.9786\n",
      "Epoch 1657/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1265 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1303 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1284 - categorical_accuracy: 0.9786\n",
      "Epoch 1658/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1338 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1277 - categorical_accuracy: 0.97 - 0s 49us/step - loss: 0.1283 - categorical_accuracy: 0.9786\n",
      "Epoch 1659/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1276 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1284 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1282 - categorical_accuracy: 0.9787\n",
      "Epoch 1660/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1316 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1282 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1281 - categorical_accuracy: 0.9787\n",
      "Epoch 1661/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1326 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1289 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1280 - categorical_accuracy: 0.9788\n",
      "Epoch 1662/2000\n",
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      "Epoch 1663/2000\n",
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      "Epoch 1664/2000\n",
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      "Epoch 1665/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1260 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1245 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1275 - categorical_accuracy: 0.9790\n",
      "Epoch 1666/2000\n",
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      "Epoch 1667/2000\n",
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      "Epoch 1668/2000\n",
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      "Epoch 1669/2000\n",
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      "Epoch 1670/2000\n",
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      "Epoch 1671/2000\n",
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      "Epoch 1672/2000\n",
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      "Epoch 1673/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1293 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1280 - categorical_accuracy: 0.97 - 1s 49us/step - loss: 0.1266 - categorical_accuracy: 0.9792\n",
      "Epoch 1674/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1282 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1259 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1264 - categorical_accuracy: 0.9793\n",
      "Epoch 1675/2000\n",
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      "Epoch 1676/2000\n",
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      "Epoch 1678/2000\n",
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      "Epoch 1679/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1247 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1254 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1259 - categorical_accuracy: 0.9793\n",
      "Epoch 1680/2000\n",
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      "Epoch 1681/2000\n",
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      "Epoch 1682/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1314 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1240 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.1255 - categorical_accuracy: 0.9794\n",
      "Epoch 1683/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1199 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1248 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1254 - categorical_accuracy: 0.9794\n",
      "Epoch 1684/2000\n",
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      "Epoch 1685/2000\n",
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      "Epoch 1686/2000\n",
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      "Epoch 1687/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1244 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1257 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1250 - categorical_accuracy: 0.9794\n",
      "Epoch 1688/2000\n",
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      "Epoch 1689/2000\n",
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      "Epoch 1690/2000\n",
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      "Epoch 1691/2000\n",
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      "Epoch 1692/2000\n",
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      "Epoch 1693/2000\n",
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      "Epoch 1694/2000\n",
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      "Epoch 1695/2000\n",
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      "Epoch 1696/2000\n",
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      "Epoch 1697/2000\n",
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      "Epoch 1698/2000\n",
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      "Epoch 1699/2000\n",
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      "Epoch 1700/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1217 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1266 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1235 - categorical_accuracy: 0.9798\n",
      "Epoch 1701/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1226 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1244 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1234 - categorical_accuracy: 0.9799\n",
      "Epoch 1702/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1279 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1246 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1233 - categorical_accuracy: 0.9800\n",
      "Epoch 1703/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1170 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1230 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1232 - categorical_accuracy: 0.9801\n",
      "Epoch 1704/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1253 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1228 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1231 - categorical_accuracy: 0.9801\n",
      "Epoch 1705/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1217 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1211 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1229 - categorical_accuracy: 0.9801\n",
      "Epoch 1706/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1253 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1238 - categorical_accuracy: 0.97 - 1s 55us/step - loss: 0.1228 - categorical_accuracy: 0.9801\n",
      "Epoch 1707/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1330 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1240 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1227 - categorical_accuracy: 0.9800\n",
      "Epoch 1708/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1224 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1214 - categorical_accuracy: 0.98 - 1s 56us/step - loss: 0.1226 - categorical_accuracy: 0.9800\n",
      "Epoch 1709/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1223 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1237 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1225 - categorical_accuracy: 0.9801\n",
      "Epoch 1710/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1185 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1211 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1224 - categorical_accuracy: 0.9801\n",
      "Epoch 1711/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1223 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1219 - categorical_accuracy: 0.98 - 0s 47us/step - loss: 0.1223 - categorical_accuracy: 0.9801\n",
      "Epoch 1712/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1279 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1213 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1222 - categorical_accuracy: 0.9803\n",
      "Epoch 1713/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1230 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1252 - categorical_accuracy: 0.97 - 1s 56us/step - loss: 0.1221 - categorical_accuracy: 0.9803\n",
      "Epoch 1714/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1213 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1212 - categorical_accuracy: 0.98 - 0s 47us/step - loss: 0.1219 - categorical_accuracy: 0.9804\n",
      "Epoch 1715/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1209 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1205 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1218 - categorical_accuracy: 0.9804\n",
      "Epoch 1716/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1169 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1197 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1217 - categorical_accuracy: 0.9804\n",
      "Epoch 1717/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1198 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1239 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1216 - categorical_accuracy: 0.9803\n",
      "Epoch 1718/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1250 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1216 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1215 - categorical_accuracy: 0.9803\n",
      "Epoch 1719/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1247 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1187 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1214 - categorical_accuracy: 0.9803\n",
      "Epoch 1720/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1181 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1233 - categorical_accuracy: 0.97 - 1s 53us/step - loss: 0.1213 - categorical_accuracy: 0.9804\n",
      "Epoch 1721/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1255 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1229 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1212 - categorical_accuracy: 0.9803\n",
      "Epoch 1722/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1188 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1233 - categorical_accuracy: 0.97 - 1s 51us/step - loss: 0.1211 - categorical_accuracy: 0.9804\n",
      "Epoch 1723/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1319 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1247 - categorical_accuracy: 0.97 - 1s 52us/step - loss: 0.1210 - categorical_accuracy: 0.9804\n",
      "Epoch 1724/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1253 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1208 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1208 - categorical_accuracy: 0.9804\n",
      "Epoch 1725/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1169 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1199 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1207 - categorical_accuracy: 0.9804\n",
      "Epoch 1726/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1183 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1211 - categorical_accuracy: 0.97 - 1s 50us/step - loss: 0.1206 - categorical_accuracy: 0.9804\n",
      "Epoch 1727/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1221 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1192 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1205 - categorical_accuracy: 0.9804\n",
      "Epoch 1728/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1195 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1181 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1204 - categorical_accuracy: 0.9804\n",
      "Epoch 1729/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1201 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1205 - categorical_accuracy: 0.97 - 1s 54us/step - loss: 0.1203 - categorical_accuracy: 0.9804\n",
      "Epoch 1730/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1211 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1225 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1202 - categorical_accuracy: 0.9806\n",
      "Epoch 1731/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1210 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1204 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1201 - categorical_accuracy: 0.9806\n",
      "Epoch 1732/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1150 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1180 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1200 - categorical_accuracy: 0.9807\n",
      "Epoch 1733/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1189 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1195 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1199 - categorical_accuracy: 0.9807\n",
      "Epoch 1734/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1215 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1210 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1198 - categorical_accuracy: 0.9807\n",
      "Epoch 1735/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1222 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1215 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1197 - categorical_accuracy: 0.9807\n",
      "Epoch 1736/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1166 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1196 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1195 - categorical_accuracy: 0.9807\n",
      "Epoch 1737/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1221 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1191 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1194 - categorical_accuracy: 0.9807\n",
      "Epoch 1738/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1200 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1189 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1193 - categorical_accuracy: 0.9807\n",
      "Epoch 1739/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1173 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1184 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.1192 - categorical_accuracy: 0.9807\n",
      "Epoch 1740/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1161 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1176 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1191 - categorical_accuracy: 0.9808\n",
      "Epoch 1741/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1120 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1191 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1190 - categorical_accuracy: 0.9808\n",
      "Epoch 1742/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1208 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1199 - categorical_accuracy: 0.98 - 1s 56us/step - loss: 0.1189 - categorical_accuracy: 0.9809\n",
      "Epoch 1743/2000\n"
     ]
    },
    {
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1203 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1194 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1188 - categorical_accuracy: 0.9808\n",
      "Epoch 1744/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1188 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1199 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1187 - categorical_accuracy: 0.9808\n",
      "Epoch 1745/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1177 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1164 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1186 - categorical_accuracy: 0.9810\n",
      "Epoch 1746/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1170 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1183 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1185 - categorical_accuracy: 0.9809\n",
      "Epoch 1747/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1145 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1191 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1184 - categorical_accuracy: 0.9809\n",
      "Epoch 1748/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1168 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1196 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1183 - categorical_accuracy: 0.9808\n",
      "Epoch 1749/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1188 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1194 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1182 - categorical_accuracy: 0.9807\n",
      "Epoch 1750/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1196 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1185 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1180 - categorical_accuracy: 0.9807\n",
      "Epoch 1751/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1228 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1204 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1179 - categorical_accuracy: 0.9807\n",
      "Epoch 1752/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1177 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1166 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1178 - categorical_accuracy: 0.9807\n",
      "Epoch 1753/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1217 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1199 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1177 - categorical_accuracy: 0.9806\n",
      "Epoch 1754/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1157 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1153 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1176 - categorical_accuracy: 0.9806\n",
      "Epoch 1755/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1197 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1147 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1175 - categorical_accuracy: 0.9807\n",
      "Epoch 1756/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1201 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1177 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1174 - categorical_accuracy: 0.9808\n",
      "Epoch 1757/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1176 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1170 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1173 - categorical_accuracy: 0.9809\n",
      "Epoch 1758/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1123 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1148 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.1172 - categorical_accuracy: 0.9811\n",
      "Epoch 1759/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1162 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1170 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.1171 - categorical_accuracy: 0.9810\n",
      "Epoch 1760/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1140 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1196 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1170 - categorical_accuracy: 0.9810\n",
      "Epoch 1761/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1218 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1180 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1169 - categorical_accuracy: 0.9810\n",
      "Epoch 1762/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1079 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1154 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1168 - categorical_accuracy: 0.9810\n",
      "Epoch 1763/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1188 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1189 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1167 - categorical_accuracy: 0.9810\n",
      "Epoch 1764/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1186 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1160 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1166 - categorical_accuracy: 0.9810\n",
      "Epoch 1765/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1179 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1189 - categorical_accuracy: 0.98 - 1s 57us/step - loss: 0.1165 - categorical_accuracy: 0.9810\n",
      "Epoch 1766/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1198 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1175 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1164 - categorical_accuracy: 0.9810\n",
      "Epoch 1767/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1163 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1163 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1163 - categorical_accuracy: 0.9809\n",
      "Epoch 1768/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1191 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1171 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1161 - categorical_accuracy: 0.9810\n",
      "Epoch 1769/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1191 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1157 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1160 - categorical_accuracy: 0.9811\n",
      "Epoch 1770/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1170 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1158 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1159 - categorical_accuracy: 0.9811\n",
      "Epoch 1771/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1155 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1161 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1158 - categorical_accuracy: 0.9811\n",
      "Epoch 1772/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1170 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1152 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1157 - categorical_accuracy: 0.9811\n",
      "Epoch 1773/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1127 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1154 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1156 - categorical_accuracy: 0.9811\n",
      "Epoch 1774/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1149 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1169 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1155 - categorical_accuracy: 0.9811\n",
      "Epoch 1775/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1154 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1168 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1154 - categorical_accuracy: 0.9812\n",
      "Epoch 1776/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1090 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1140 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1153 - categorical_accuracy: 0.9811\n",
      "Epoch 1777/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1138 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1171 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1152 - categorical_accuracy: 0.9812\n",
      "Epoch 1778/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1173 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1155 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1151 - categorical_accuracy: 0.9813\n",
      "Epoch 1779/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1082 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1135 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1150 - categorical_accuracy: 0.9813\n",
      "Epoch 1780/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1137 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1147 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1149 - categorical_accuracy: 0.9813\n",
      "Epoch 1781/2000\n",
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      "Epoch 1782/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1105 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1144 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1147 - categorical_accuracy: 0.9813\n",
      "Epoch 1783/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1142 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1167 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1146 - categorical_accuracy: 0.9813\n",
      "Epoch 1784/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1144 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1132 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1145 - categorical_accuracy: 0.9813\n",
      "Epoch 1785/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1171 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1142 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1144 - categorical_accuracy: 0.9813\n",
      "Epoch 1786/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1147 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1167 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1143 - categorical_accuracy: 0.9813\n",
      "Epoch 1787/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1198 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1149 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1142 - categorical_accuracy: 0.9813\n",
      "Epoch 1788/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1152 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1137 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1141 - categorical_accuracy: 0.9813\n",
      "Epoch 1789/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1163 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1168 - categorical_accuracy: 0.97 - 0s 48us/step - loss: 0.1140 - categorical_accuracy: 0.9812\n",
      "Epoch 1790/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1135 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1104 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1139 - categorical_accuracy: 0.9812\n",
      "Epoch 1791/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1145 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1123 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1138 - categorical_accuracy: 0.9812\n",
      "Epoch 1792/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1126 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1146 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1137 - categorical_accuracy: 0.9814\n",
      "Epoch 1793/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1151 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1143 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1136 - categorical_accuracy: 0.9814\n",
      "Epoch 1794/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1182 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1143 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1135 - categorical_accuracy: 0.9814\n",
      "Epoch 1795/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1207 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1169 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1134 - categorical_accuracy: 0.9814\n",
      "Epoch 1796/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1153 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1135 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1133 - categorical_accuracy: 0.9813\n",
      "Epoch 1797/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1142 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1135 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1132 - categorical_accuracy: 0.9814\n",
      "Epoch 1798/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1172 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1145 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1131 - categorical_accuracy: 0.9816\n",
      "Epoch 1799/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1178 - categorical_accuracy: 0.97 - ETA: 0s - loss: 0.1146 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1130 - categorical_accuracy: 0.9817\n",
      "Epoch 1800/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1153 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1139 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1129 - categorical_accuracy: 0.9818\n",
      "Epoch 1801/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1168 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1124 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1128 - categorical_accuracy: 0.9818\n",
      "Epoch 1802/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1117 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1116 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1127 - categorical_accuracy: 0.9819\n",
      "Epoch 1803/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1136 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1133 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1126 - categorical_accuracy: 0.9819\n",
      "Epoch 1804/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1138 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1140 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1125 - categorical_accuracy: 0.9820\n",
      "Epoch 1805/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1134 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1128 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1124 - categorical_accuracy: 0.9820\n",
      "Epoch 1806/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1144 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1143 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1123 - categorical_accuracy: 0.9822\n",
      "Epoch 1807/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1157 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1127 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1122 - categorical_accuracy: 0.9822\n",
      "Epoch 1808/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1174 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1116 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1121 - categorical_accuracy: 0.9821\n",
      "Epoch 1809/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1107 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1113 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1120 - categorical_accuracy: 0.9820\n",
      "Epoch 1810/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1150 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1150 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1119 - categorical_accuracy: 0.9821\n",
      "Epoch 1811/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1072 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1098 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1118 - categorical_accuracy: 0.9823\n",
      "Epoch 1812/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1067 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1113 - categorical_accuracy: 0.98 - 1s 56us/step - loss: 0.1117 - categorical_accuracy: 0.9822\n",
      "Epoch 1813/2000\n"
     ]
    },
    {
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1156 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1123 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1116 - categorical_accuracy: 0.9822\n",
      "Epoch 1814/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1107 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1111 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1115 - categorical_accuracy: 0.9823\n",
      "Epoch 1815/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1080 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1099 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1114 - categorical_accuracy: 0.9824\n",
      "Epoch 1816/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1075 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1113 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1113 - categorical_accuracy: 0.9824\n",
      "Epoch 1817/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1121 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1100 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1112 - categorical_accuracy: 0.9826\n",
      "Epoch 1818/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1083 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1114 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1111 - categorical_accuracy: 0.9824\n",
      "Epoch 1819/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1078 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1098 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1110 - categorical_accuracy: 0.9825\n",
      "Epoch 1820/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1074 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1100 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1109 - categorical_accuracy: 0.9825\n",
      "Epoch 1821/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1152 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1107 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1108 - categorical_accuracy: 0.9826\n",
      "Epoch 1822/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1091 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1099 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1107 - categorical_accuracy: 0.9826\n",
      "Epoch 1823/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1091 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1116 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1106 - categorical_accuracy: 0.9825\n",
      "Epoch 1824/2000\n",
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      "Epoch 1825/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1104 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1105 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.1104 - categorical_accuracy: 0.9825\n",
      "Epoch 1826/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1056 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1118 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1103 - categorical_accuracy: 0.9825\n",
      "Epoch 1827/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1071 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1097 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1102 - categorical_accuracy: 0.9826\n",
      "Epoch 1828/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1104 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1109 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1101 - categorical_accuracy: 0.9825\n",
      "Epoch 1829/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1109 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1093 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1100 - categorical_accuracy: 0.9827\n",
      "Epoch 1830/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1114 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1098 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1099 - categorical_accuracy: 0.9827\n",
      "Epoch 1831/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1094 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1105 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1098 - categorical_accuracy: 0.9826\n",
      "Epoch 1832/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1112 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1091 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1097 - categorical_accuracy: 0.9826\n",
      "Epoch 1833/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1107 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1092 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1096 - categorical_accuracy: 0.9826\n",
      "Epoch 1834/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1063 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1105 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1095 - categorical_accuracy: 0.9826\n",
      "Epoch 1835/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1043 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1098 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1094 - categorical_accuracy: 0.9826\n",
      "Epoch 1836/2000\n",
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      "Epoch 1838/2000\n",
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      "Epoch 1839/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1074 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1080 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1090 - categorical_accuracy: 0.9830\n",
      "Epoch 1840/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1103 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1081 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1089 - categorical_accuracy: 0.9830\n",
      "Epoch 1841/2000\n",
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      "Epoch 1842/2000\n",
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      "Epoch 1844/2000\n",
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      "Epoch 1845/2000\n",
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      "Epoch 1847/2000\n",
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      "Epoch 1848/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1105 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1094 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1081 - categorical_accuracy: 0.9831\n",
      "Epoch 1849/2000\n",
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      "Epoch 1850/2000\n",
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      "Epoch 1851/2000\n",
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      "Epoch 1853/2000\n",
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      "Epoch 1855/2000\n",
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      "Epoch 1856/2000\n",
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      "Epoch 1857/2000\n",
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      "Epoch 1860/2000\n",
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      "Epoch 1861/2000\n",
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      "Epoch 1862/2000\n",
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      "Epoch 1863/2000\n",
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      "Epoch 1864/2000\n",
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      "Epoch 1866/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1105 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1084 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1064 - categorical_accuracy: 0.9835\n",
      "Epoch 1867/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1080 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1059 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1063 - categorical_accuracy: 0.9835\n",
      "Epoch 1868/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1038 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1045 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1062 - categorical_accuracy: 0.9836\n",
      "Epoch 1869/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1020 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1045 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1061 - categorical_accuracy: 0.9836\n",
      "Epoch 1870/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1103 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1094 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1060 - categorical_accuracy: 0.9836\n",
      "Epoch 1871/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1054 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1065 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1059 - categorical_accuracy: 0.9837\n",
      "Epoch 1872/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0998 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1055 - categorical_accuracy: 0.98 - 1s 56us/step - loss: 0.1058 - categorical_accuracy: 0.9838\n",
      "Epoch 1873/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1043 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1055 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1058 - categorical_accuracy: 0.9838\n",
      "Epoch 1874/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1051 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1064 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1057 - categorical_accuracy: 0.9837\n",
      "Epoch 1875/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1026 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1051 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1056 - categorical_accuracy: 0.9835\n",
      "Epoch 1876/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1080 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1047 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1055 - categorical_accuracy: 0.9836\n",
      "Epoch 1877/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1066 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1073 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1054 - categorical_accuracy: 0.9836\n",
      "Epoch 1878/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1068 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1058 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1053 - categorical_accuracy: 0.9836\n",
      "Epoch 1879/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1104 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1038 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1052 - categorical_accuracy: 0.9836\n",
      "Epoch 1880/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1092 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1067 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.1051 - categorical_accuracy: 0.9836\n",
      "Epoch 1881/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1015 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1040 - categorical_accuracy: 0.98 - 1s 60us/step - loss: 0.1050 - categorical_accuracy: 0.9836\n",
      "Epoch 1882/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1034 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1045 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1049 - categorical_accuracy: 0.9837\n",
      "Epoch 1883/2000\n"
     ]
    },
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      "10240/10240 [==============================] - ETA: 0s - loss: 0.1005 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1043 - categorical_accuracy: 0.98 - 1s 57us/step - loss: 0.1048 - categorical_accuracy: 0.9837\n",
      "Epoch 1884/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1062 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1037 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1047 - categorical_accuracy: 0.9837\n",
      "Epoch 1885/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1050 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1049 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1046 - categorical_accuracy: 0.9837\n",
      "Epoch 1886/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1070 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1054 - categorical_accuracy: 0.98 - 0s 47us/step - loss: 0.1045 - categorical_accuracy: 0.9837\n",
      "Epoch 1887/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1022 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1037 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1044 - categorical_accuracy: 0.9837\n",
      "Epoch 1888/2000\n",
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      "Epoch 1889/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1033 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1050 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1043 - categorical_accuracy: 0.9838\n",
      "Epoch 1890/2000\n",
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      "Epoch 1891/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1023 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1050 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1041 - categorical_accuracy: 0.9836\n",
      "Epoch 1892/2000\n",
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      "Epoch 1893/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1019 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1034 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1039 - categorical_accuracy: 0.9837\n",
      "Epoch 1894/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1047 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1037 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1038 - categorical_accuracy: 0.9839\n",
      "Epoch 1895/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1039 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1046 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1037 - categorical_accuracy: 0.9840\n",
      "Epoch 1896/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1058 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1033 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1036 - categorical_accuracy: 0.9841\n",
      "Epoch 1897/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1075 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1037 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1035 - categorical_accuracy: 0.9842\n",
      "Epoch 1898/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1007 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1035 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1034 - categorical_accuracy: 0.9842\n",
      "Epoch 1899/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0979 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1039 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.1033 - categorical_accuracy: 0.9841\n",
      "Epoch 1900/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1021 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1025 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.1032 - categorical_accuracy: 0.9841\n",
      "Epoch 1901/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1010 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1001 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1031 - categorical_accuracy: 0.9842\n",
      "Epoch 1902/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1026 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1017 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.1031 - categorical_accuracy: 0.9842\n",
      "Epoch 1903/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1050 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1023 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1030 - categorical_accuracy: 0.9842\n",
      "Epoch 1904/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1061 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1032 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1029 - categorical_accuracy: 0.9842\n",
      "Epoch 1905/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1037 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1025 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.1028 - categorical_accuracy: 0.9842\n",
      "Epoch 1906/2000\n",
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      "Epoch 1907/2000\n",
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      "Epoch 1908/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1016 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1008 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1025 - categorical_accuracy: 0.9841\n",
      "Epoch 1909/2000\n",
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      "Epoch 1910/2000\n",
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      "Epoch 1911/2000\n",
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      "Epoch 1912/2000\n",
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      "Epoch 1913/2000\n",
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      "Epoch 1914/2000\n",
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      "Epoch 1915/2000\n",
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      "Epoch 1916/2000\n",
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      "Epoch 1917/2000\n",
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      "Epoch 1918/2000\n"
     ]
    },
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     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1087 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1018 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.1016 - categorical_accuracy: 0.9841\n",
      "Epoch 1919/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1016 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1022 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.1015 - categorical_accuracy: 0.9841\n",
      "Epoch 1920/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1026 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1011 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1014 - categorical_accuracy: 0.9841\n",
      "Epoch 1921/2000\n",
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      "Epoch 1922/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0992 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.1033 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.1012 - categorical_accuracy: 0.9843\n",
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      "Epoch 1924/2000\n",
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      "Epoch 1925/2000\n",
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      "Epoch 1926/2000\n",
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      "Epoch 1928/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1002 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0994 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.1007 - categorical_accuracy: 0.9844\n",
      "Epoch 1929/2000\n",
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      "Epoch 1930/2000\n",
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      "Epoch 1931/2000\n",
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      "Epoch 1932/2000\n",
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      "Epoch 1933/2000\n",
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      "Epoch 1934/2000\n",
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      "Epoch 1935/2000\n",
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      "Epoch 1936/2000\n",
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      "Epoch 1937/2000\n",
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      "Epoch 1938/2000\n",
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      "Epoch 1939/2000\n",
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      "Epoch 1945/2000\n",
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      "Epoch 1950/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0939 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0990 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.0987 - categorical_accuracy: 0.9849\n",
      "Epoch 1951/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0939 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0987 - categorical_accuracy: 0.9849\n",
      "Epoch 1952/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0987 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0998 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.0986 - categorical_accuracy: 0.9850\n",
      "Epoch 1953/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0996 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0979 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.0985 - categorical_accuracy: 0.9850\n",
      "Epoch 1954/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1001 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0993 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.0984 - categorical_accuracy: 0.9850\n",
      "Epoch 1955/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0986 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0988 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.0983 - categorical_accuracy: 0.9850\n",
      "Epoch 1956/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0974 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0967 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.0982 - categorical_accuracy: 0.9851\n",
      "Epoch 1957/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1022 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0993 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.0981 - categorical_accuracy: 0.9851\n",
      "Epoch 1958/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0988 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0988 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.0981 - categorical_accuracy: 0.9851\n",
      "Epoch 1959/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0946 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0992 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.0980 - categorical_accuracy: 0.9851\n",
      "Epoch 1960/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0949 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0949 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.0979 - categorical_accuracy: 0.9851\n",
      "Epoch 1961/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1004 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0986 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.0978 - categorical_accuracy: 0.9851\n",
      "Epoch 1962/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.1018 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0968 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0977 - categorical_accuracy: 0.9851\n",
      "Epoch 1963/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0965 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0978 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.0976 - categorical_accuracy: 0.9852\n",
      "Epoch 1964/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0963 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0962 - categorical_accuracy: 0.98 - 1s 53us/step - loss: 0.0975 - categorical_accuracy: 0.9851\n",
      "Epoch 1965/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0976 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0986 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.0975 - categorical_accuracy: 0.9852\n",
      "Epoch 1966/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0946 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0979 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0974 - categorical_accuracy: 0.9852\n",
      "Epoch 1967/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0956 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0972 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.0973 - categorical_accuracy: 0.9852\n",
      "Epoch 1968/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0984 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0974 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0972 - categorical_accuracy: 0.9853\n",
      "Epoch 1969/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0980 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.0971 - categorical_accuracy: 0.9853\n",
      "Epoch 1970/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0992 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0970 - categorical_accuracy: 0.9853\n",
      "Epoch 1971/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0957 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0969 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.0969 - categorical_accuracy: 0.9853\n",
      "Epoch 1972/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0992 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0968 - categorical_accuracy: 0.9853\n",
      "Epoch 1973/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0959 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0965 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.0968 - categorical_accuracy: 0.9854\n",
      "Epoch 1974/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0962 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0967 - categorical_accuracy: 0.9853\n",
      "Epoch 1975/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0971 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0974 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.0966 - categorical_accuracy: 0.9853\n",
      "Epoch 1976/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0930 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0960 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0965 - categorical_accuracy: 0.9853\n",
      "Epoch 1977/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0953 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0962 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0964 - categorical_accuracy: 0.9854\n",
      "Epoch 1978/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0942 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0983 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.0963 - categorical_accuracy: 0.9854\n",
      "Epoch 1979/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0955 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0962 - categorical_accuracy: 0.9854\n",
      "Epoch 1980/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0989 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0974 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.0962 - categorical_accuracy: 0.9855\n",
      "Epoch 1981/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0932 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0961 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0961 - categorical_accuracy: 0.9855\n",
      "Epoch 1982/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0944 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0965 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.0960 - categorical_accuracy: 0.9855\n",
      "Epoch 1983/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0966 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0956 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0959 - categorical_accuracy: 0.9856\n",
      "Epoch 1984/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0984 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0969 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.0958 - categorical_accuracy: 0.9856\n",
      "Epoch 1985/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0963 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0946 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0957 - categorical_accuracy: 0.9856\n",
      "Epoch 1986/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0962 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0973 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.0957 - categorical_accuracy: 0.9856\n",
      "Epoch 1987/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0977 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0959 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0956 - categorical_accuracy: 0.9856\n",
      "Epoch 1988/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0951 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0927 - categorical_accuracy: 0.98 - 1s 51us/step - loss: 0.0955 - categorical_accuracy: 0.9855\n",
      "Epoch 1989/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0921 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0932 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.0954 - categorical_accuracy: 0.9855\n",
      "Epoch 1990/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0959 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0972 - categorical_accuracy: 0.98 - 1s 49us/step - loss: 0.0953 - categorical_accuracy: 0.9855\n",
      "Epoch 1991/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0935 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0950 - categorical_accuracy: 0.98 - 1s 50us/step - loss: 0.0952 - categorical_accuracy: 0.9855\n",
      "Epoch 1992/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0984 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0958 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.0952 - categorical_accuracy: 0.9855\n",
      "Epoch 1993/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0930 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0930 - categorical_accuracy: 0.98 - 1s 52us/step - loss: 0.0951 - categorical_accuracy: 0.9855\n",
      "Epoch 1994/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0981 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0962 - categorical_accuracy: 0.98 - 1s 55us/step - loss: 0.0950 - categorical_accuracy: 0.9855\n",
      "Epoch 1995/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0960 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0939 - categorical_accuracy: 0.98 - 0s 49us/step - loss: 0.0949 - categorical_accuracy: 0.9856\n",
      "Epoch 1996/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0957 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0936 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0948 - categorical_accuracy: 0.9856\n",
      "Epoch 1997/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0985 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0966 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.0947 - categorical_accuracy: 0.9856\n",
      "Epoch 1998/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0987 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0949 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0946 - categorical_accuracy: 0.9856\n",
      "Epoch 1999/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0967 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0963 - categorical_accuracy: 0.98 - 0s 48us/step - loss: 0.0946 - categorical_accuracy: 0.9857\n",
      "Epoch 2000/2000\n",
      "10240/10240 [==============================] - ETA: 0s - loss: 0.0935 - categorical_accuracy: 0.98 - ETA: 0s - loss: 0.0940 - categorical_accuracy: 0.98 - 1s 54us/step - loss: 0.0945 - categorical_accuracy: 0.9858\n"
     ]
    }
   ],
   "source": [
    "his = model_top.fit(x = x_total,y = y_total,batch_size=batch_size_top, epochs=2000,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_final = model_top(extracted_features)\n",
    "model_final = Model(inputs=input_img,outputs=output_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_final.save_weights('dogbreed_final_k_fold_0203_incept.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_top.save_weights('dogbreed_top_k_fold_0203_incept.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_ids = df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_prediction(test_ids,batch_size,model_final):\n",
    "    buffer_id = []\n",
    "    buffer_img = np.zeros((batch_size,360,360,3))\n",
    "    preds = df_test.copy()\n",
    "    for i,id in enumerate(test_ids):\n",
    "        buffer_id.append(id)\n",
    "        pic = image.load_img('test//{}.jpg'.format(str(id)),target_size=(360,360))\n",
    "        pic = image.img_to_array(pic)\n",
    "        pic /= 255.\n",
    "        buffer_img[i%batch_size] = pic\n",
    "        if (i+1)%batch_size == 0:\n",
    "            prediction = model_final.predict_on_batch(buffer_img)\n",
    "            preds.loc[buffer_id] = prediction\n",
    "            buffer_id = []\n",
    "            buffer_img = np.zeros((batch_size,360,360,3))\n",
    "            if (i//batch_size +1)%10 == 0:\n",
    "                print('{} pieces finished'.format(i))\n",
    "\n",
    "    last_buffer = buffer_img[:len(buffer_id)]\n",
    "    last_prediction = model_final.predict_on_batch(last_buffer)\n",
    "    preds.loc[buffer_id] = last_prediction\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1279 pieces finished\n",
      "2559 pieces finished\n",
      "3839 pieces finished\n",
      "5119 pieces finished\n",
      "6399 pieces finished\n",
      "7679 pieces finished\n",
      "8959 pieces finished\n",
      "10239 pieces finished\n"
     ]
    }
   ],
   "source": [
    "prediction_incep = get_prediction(test_ids,128,model_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_incep.to_csv('prediction_incep0203_k_fold_2000early_stopping.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_xcep = pd.read_csv('prediction_xcep0203_k_fold.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10357, 120)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_xcep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_k_fold_ensemble = (prediction_incep + prediction_xcep)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_k_fold_ensemble.to_csv('prediction_k_fold_ensemble.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                                   affenpinscher  afghan_hound  \\\n",
       "id                                                              \n",
       "000621fb3cbb32d8935728e48679680e   6.856625e-06  2.185608e-06   \n",
       "00102ee9d8eb90812350685311fe5890   2.810299e-07  2.712543e-08   \n",
       "0012a730dfa437f5f3613fb75efcd4ce   1.608751e-06  5.193424e-04   \n",
       "001510bc8570bbeee98c8d80c8a95ec1   3.067466e-04  1.291603e-06   \n",
       "001a5f3114548acdefa3d4da05474c2e   1.973869e-02  4.469805e-05   \n",
       "00225dcd3e4d2410dd53239f95c0352f   3.548631e-05  2.954985e-02   \n",
       "002c2a3117c2193b4d26400ce431eebd   1.403570e-06  3.148713e-06   \n",
       "002c58d413a521ae8d1a5daeb35fc803   1.015625e-06  1.880997e-07   \n",
       "002f80396f1e3db687c5932d7978b196   8.303633e-07  7.711309e-06   \n",
       "0036c6bcec6031be9e62a257b1c3c442   3.395443e-07  1.003170e-06   \n",
       "0041940322116ae58c38130f5a6f71f9   4.268810e-06  2.260808e-07   \n",
       "0042d6bf3e5f3700865886db32689436   2.966088e-06  8.300391e-07   \n",
       "004476c96f575879af4af471af65cae8   4.581874e-06  2.224276e-07   \n",
       "00485d47de966a9437ad3b33ac193b6f   1.880470e-05  3.053587e-06   \n",
       "00496f65de6cc319145ce97bd6e90360   3.145942e-07  1.538850e-06   \n",
       "004bf14426d1a830d459a9e0c0721309   4.925891e-07  7.482272e-06   \n",
       "004c3721eb88358f462cdcec6b2380b7   6.949342e-07  4.083704e-07   \n",
       "00559f56aab7e0a7749220f6aed65162   3.789885e-05  1.343919e-04   \n",
       "005b281f1a4d6f29d527c9585e9bd33c   5.021403e-06  3.755615e-06   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e   5.129767e-09  2.009435e-05   \n",
       "006870b49353779b25eeb91fed43c31a   9.322973e-05  9.935306e-06   \n",
       "0068f3a21b159ece126a28580cdad7a0   5.113663e-06  3.826167e-06   \n",
       "0069b1cc4546fc98f84f981bf9a0696a   6.701255e-07  7.759869e-08   \n",
       "0077bc3c63486ff09d3774d956af8f76   1.653887e-06  2.895186e-08   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a   2.065613e-06  2.579377e-05   \n",
       "007ed71136966728f5c0936e23c8286b   5.502175e-06  5.587585e-06   \n",
       "0081831ceb49cd64212c32b884036b82   3.339412e-06  4.405820e-06   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6   1.757779e-06  3.500945e-07   \n",
       "0092bd9e90a13403373fc0e9e1218938   1.089738e-06  3.270037e-06   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760   2.060346e-06  5.023179e-07   \n",
       "...                                         ...           ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a   1.545552e-02  3.895315e-04   \n",
       "ff8c3ee47324ecf110e147f29747952a   2.997294e-02  7.302771e-04   \n",
       "ff8f9a768544fda79ffad62576f4d129   7.882552e-02  1.465936e-03   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda   1.515224e-01  7.743611e-04   \n",
       "ff9742bc22b5830176e85490cf54cdf7   5.492435e-02  3.658903e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd   5.829119e-02  2.041656e-03   \n",
       "ffa316c6db0919d6bad1a5ed483e6722   1.202181e-01  7.746521e-04   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3   1.044081e-01  8.583784e-04   \n",
       "ffadae851b94ece57602da7edf72d689   7.658143e-02  1.980107e-03   \n",
       "ffb3a78e4a162c173219194687eb6054   6.340751e-02  1.313225e-03   \n",
       "ffb55dbaa32939c109ef42df0668e077   9.292590e-02  2.157493e-03   \n",
       "ffb68ded4a2247b3c011840ece0a605c   1.543053e-01  1.156380e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454   6.030028e-02  1.802568e-03   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb   1.075870e-01  1.166914e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2   1.082398e-01  1.816175e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f   7.656079e-02  2.025982e-03   \n",
       "ffc0233904f4d03afe1484be791e09d6   3.169674e-02  5.379664e-04   \n",
       "ffd06687c72445b0c6e8a130a0a8711a   4.050584e-02  3.699106e-04   \n",
       "ffd304c521f43819f3824177fd9efeb0   6.030054e-02  1.087184e-03   \n",
       "ffd3ce0566df4be8049c767a329faaf7   1.599408e-02  6.961844e-04   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b   1.605025e-02  5.203460e-04   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b   3.316495e-02  8.070456e-04   \n",
       "ffe42f4b4c50d4f18c2b34500f391152   5.389535e-02  4.535618e-04   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96   3.061082e-02  9.582986e-04   \n",
       "ffe9717b7937c262f849416976f7620a   3.759038e-02  8.142348e-04   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8   3.929219e-02  6.452360e-04   \n",
       "fff1ec9e6e413275984966f745a313b0   4.727275e-02  3.379599e-04   \n",
       "fff74b59b758bbbf13a5793182a9bbe4   3.225160e-02  5.882088e-04   \n",
       "fff7d50d848e8014ac1e9172dc6762a3   4.347301e-02  5.400990e-04   \n",
       "fffbff22c1f51e3dc80c4bf04089545b   4.404085e-02  7.994283e-04   \n",
       "\n",
       "                                  african_hunting_dog      airedale  \\\n",
       "id                                                                    \n",
       "000621fb3cbb32d8935728e48679680e         2.373240e-06  5.912643e-07   \n",
       "00102ee9d8eb90812350685311fe5890         1.418380e-07  1.965889e-07   \n",
       "0012a730dfa437f5f3613fb75efcd4ce         2.135083e-05  1.044994e-05   \n",
       "001510bc8570bbeee98c8d80c8a95ec1         1.480836e-06  2.793684e-06   \n",
       "001a5f3114548acdefa3d4da05474c2e         1.833391e-05  3.693822e-06   \n",
       "00225dcd3e4d2410dd53239f95c0352f         1.601982e-05  4.498529e-03   \n",
       "002c2a3117c2193b4d26400ce431eebd         2.438181e-06  3.817633e-05   \n",
       "002c58d413a521ae8d1a5daeb35fc803         6.445587e-07  5.387639e-07   \n",
       "002f80396f1e3db687c5932d7978b196         7.453346e-07  2.628883e-05   \n",
       "0036c6bcec6031be9e62a257b1c3c442         5.254630e-07  1.504328e-08   \n",
       "0041940322116ae58c38130f5a6f71f9         1.038365e-07  5.613236e-08   \n",
       "0042d6bf3e5f3700865886db32689436         2.622075e-06  3.348096e-06   \n",
       "004476c96f575879af4af471af65cae8         9.227920e-08  3.129078e-08   \n",
       "00485d47de966a9437ad3b33ac193b6f         1.453471e-04  8.266991e-04   \n",
       "00496f65de6cc319145ce97bd6e90360         1.689323e-06  1.247821e-05   \n",
       "004bf14426d1a830d459a9e0c0721309         9.275054e-06  2.617304e-07   \n",
       "004c3721eb88358f462cdcec6b2380b7         2.514944e-06  2.599050e-07   \n",
       "00559f56aab7e0a7749220f6aed65162         9.001172e-06  1.002652e-04   \n",
       "005b281f1a4d6f29d527c9585e9bd33c         6.482986e-06  1.223470e-03   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e         1.632171e-08  9.589044e-08   \n",
       "006870b49353779b25eeb91fed43c31a         9.678991e-06  6.586251e-06   \n",
       "0068f3a21b159ece126a28580cdad7a0         5.279266e-06  3.014539e-06   \n",
       "0069b1cc4546fc98f84f981bf9a0696a         2.231174e-06  6.346257e-07   \n",
       "0077bc3c63486ff09d3774d956af8f76         2.894171e-07  3.803460e-07   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a         4.154957e-06  4.095939e-06   \n",
       "007ed71136966728f5c0936e23c8286b         5.377392e-06  3.281524e-06   \n",
       "0081831ceb49cd64212c32b884036b82         6.636193e-07  4.447602e-06   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6         1.594822e-06  6.814095e-07   \n",
       "0092bd9e90a13403373fc0e9e1218938         1.854748e-06  8.984814e-07   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760         5.575450e-07  5.267411e-01   \n",
       "...                                               ...           ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a         2.400681e-03  1.175850e-04   \n",
       "ff8c3ee47324ecf110e147f29747952a         7.023156e-03  9.449350e-04   \n",
       "ff8f9a768544fda79ffad62576f4d129         5.379388e-03  8.753772e-04   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda         2.351737e-03  1.346776e-03   \n",
       "ff9742bc22b5830176e85490cf54cdf7         4.305093e-03  5.749521e-04   \n",
       "ff9b317696e29ca8cd27da30a896edcd         1.665046e-02  3.520286e-04   \n",
       "ffa316c6db0919d6bad1a5ed483e6722         3.474963e-03  2.067870e-03   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3         1.055617e-02  7.955993e-04   \n",
       "ffadae851b94ece57602da7edf72d689         7.991725e-03  6.049943e-04   \n",
       "ffb3a78e4a162c173219194687eb6054         2.407515e-02  4.535268e-01   \n",
       "ffb55dbaa32939c109ef42df0668e077         1.441154e-02  5.206780e-04   \n",
       "ffb68ded4a2247b3c011840ece0a605c         4.000745e-03  1.066986e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454         1.218138e-02  7.723058e-04   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb         1.284375e-02  8.623574e-04   \n",
       "ffbcda9eb84339cc5be15fd9900596a2         9.661769e-03  1.109603e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f         1.409156e-02  3.295390e-04   \n",
       "ffc0233904f4d03afe1484be791e09d6         1.945509e-03  6.620277e-04   \n",
       "ffd06687c72445b0c6e8a130a0a8711a         2.852702e-03  2.998493e-04   \n",
       "ffd304c521f43819f3824177fd9efeb0         5.051193e-03  1.092208e-03   \n",
       "ffd3ce0566df4be8049c767a329faaf7         5.532330e-03  4.902702e-04   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b         4.940420e-03  5.805473e-04   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b         6.295801e-03  6.629938e-04   \n",
       "ffe42f4b4c50d4f18c2b34500f391152         1.851736e-03  3.518634e-04   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96         5.347224e-03  3.252150e-03   \n",
       "ffe9717b7937c262f849416976f7620a         5.885253e-03  6.126118e-04   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8         3.947852e-03  4.126320e-04   \n",
       "fff1ec9e6e413275984966f745a313b0         1.497800e-03  4.983732e-04   \n",
       "fff74b59b758bbbf13a5793182a9bbe4         4.629882e-03  5.521121e-04   \n",
       "fff7d50d848e8014ac1e9172dc6762a3         4.637235e-03  5.013845e-04   \n",
       "fffbff22c1f51e3dc80c4bf04089545b         1.782193e-03  4.923630e-04   \n",
       "\n",
       "                                  american_staffordshire_terrier  \\\n",
       "id                                                                 \n",
       "000621fb3cbb32d8935728e48679680e                    2.838876e-07   \n",
       "00102ee9d8eb90812350685311fe5890                    2.891809e-07   \n",
       "0012a730dfa437f5f3613fb75efcd4ce                    7.531194e-07   \n",
       "001510bc8570bbeee98c8d80c8a95ec1                    1.745625e-05   \n",
       "001a5f3114548acdefa3d4da05474c2e                    8.093979e-06   \n",
       "00225dcd3e4d2410dd53239f95c0352f                    2.414550e-05   \n",
       "002c2a3117c2193b4d26400ce431eebd                    1.700681e-06   \n",
       "002c58d413a521ae8d1a5daeb35fc803                    7.942456e-07   \n",
       "002f80396f1e3db687c5932d7978b196                    1.767270e-07   \n",
       "0036c6bcec6031be9e62a257b1c3c442                    8.284523e-08   \n",
       "0041940322116ae58c38130f5a6f71f9                    1.432773e-06   \n",
       "0042d6bf3e5f3700865886db32689436                    5.102695e-06   \n",
       "004476c96f575879af4af471af65cae8                    4.689591e-07   \n",
       "00485d47de966a9437ad3b33ac193b6f                    4.933850e-05   \n",
       "00496f65de6cc319145ce97bd6e90360                    1.365138e-05   \n",
       "004bf14426d1a830d459a9e0c0721309                    2.646260e-07   \n",
       "004c3721eb88358f462cdcec6b2380b7                    7.398022e-07   \n",
       "00559f56aab7e0a7749220f6aed65162                    2.791757e-05   \n",
       "005b281f1a4d6f29d527c9585e9bd33c                    3.367960e-05   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e                    3.000977e-09   \n",
       "006870b49353779b25eeb91fed43c31a                    2.095403e-05   \n",
       "0068f3a21b159ece126a28580cdad7a0                    1.893904e-05   \n",
       "0069b1cc4546fc98f84f981bf9a0696a                    8.688566e-06   \n",
       "0077bc3c63486ff09d3774d956af8f76                    2.836502e-03   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a                    3.003765e-07   \n",
       "007ed71136966728f5c0936e23c8286b                    2.255101e-07   \n",
       "0081831ceb49cd64212c32b884036b82                    1.662750e-07   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6                    2.681481e-05   \n",
       "0092bd9e90a13403373fc0e9e1218938                    1.220351e-06   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760                    1.321514e-05   \n",
       "...                                                          ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a                    4.642260e-04   \n",
       "ff8c3ee47324ecf110e147f29747952a                    1.812271e-03   \n",
       "ff8f9a768544fda79ffad62576f4d129                    5.400327e-03   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda                    4.004538e-03   \n",
       "ff9742bc22b5830176e85490cf54cdf7                    4.776880e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd                    2.109427e-03   \n",
       "ffa316c6db0919d6bad1a5ed483e6722                    4.556397e-03   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3                    5.678049e-03   \n",
       "ffadae851b94ece57602da7edf72d689                    1.024049e-02   \n",
       "ffb3a78e4a162c173219194687eb6054                    2.965860e-03   \n",
       "ffb55dbaa32939c109ef42df0668e077                    8.786884e-03   \n",
       "ffb68ded4a2247b3c011840ece0a605c                    3.787412e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454                    6.146535e-03   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb                    3.366787e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2                    3.598095e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f                    5.933423e-03   \n",
       "ffc0233904f4d03afe1484be791e09d6                    1.665974e-03   \n",
       "ffd06687c72445b0c6e8a130a0a8711a                    1.635980e-03   \n",
       "ffd304c521f43819f3824177fd9efeb0                    1.552718e-03   \n",
       "ffd3ce0566df4be8049c767a329faaf7                    2.198296e-03   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b                    2.295485e-03   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b                    2.707636e-03   \n",
       "ffe42f4b4c50d4f18c2b34500f391152                    3.255653e-03   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96                    1.506492e-03   \n",
       "ffe9717b7937c262f849416976f7620a                    2.550017e-03   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8                    2.043395e-03   \n",
       "fff1ec9e6e413275984966f745a313b0                    2.125483e-03   \n",
       "fff74b59b758bbbf13a5793182a9bbe4                    3.994368e-03   \n",
       "fff7d50d848e8014ac1e9172dc6762a3                    2.820864e-03   \n",
       "fffbff22c1f51e3dc80c4bf04089545b                    3.142327e-03   \n",
       "\n",
       "                                   appenzeller  australian_terrier  \\\n",
       "id                                                                   \n",
       "000621fb3cbb32d8935728e48679680e  7.517264e-07        6.374636e-07   \n",
       "00102ee9d8eb90812350685311fe5890  2.754098e-06        8.305668e-08   \n",
       "0012a730dfa437f5f3613fb75efcd4ce  7.630204e-06        3.726251e-07   \n",
       "001510bc8570bbeee98c8d80c8a95ec1  2.314876e-05        1.597922e-07   \n",
       "001a5f3114548acdefa3d4da05474c2e  6.798278e-05        1.182956e-04   \n",
       "00225dcd3e4d2410dd53239f95c0352f  9.057107e-05        1.555097e-05   \n",
       "002c2a3117c2193b4d26400ce431eebd  1.386132e-06        9.917780e-01   \n",
       "002c58d413a521ae8d1a5daeb35fc803  1.220843e-06        3.197826e-07   \n",
       "002f80396f1e3db687c5932d7978b196  1.667886e-06        3.561961e-08   \n",
       "0036c6bcec6031be9e62a257b1c3c442  1.547899e-08        1.232469e-07   \n",
       "0041940322116ae58c38130f5a6f71f9  7.251907e-07        8.676657e-07   \n",
       "0042d6bf3e5f3700865886db32689436  1.115556e-05        2.289612e-06   \n",
       "004476c96f575879af4af471af65cae8  7.692988e-08        2.871739e-07   \n",
       "00485d47de966a9437ad3b33ac193b6f  3.526766e-05        4.977604e-02   \n",
       "00496f65de6cc319145ce97bd6e90360  2.756005e-05        2.674852e-06   \n",
       "004bf14426d1a830d459a9e0c0721309  1.120943e-06        3.357595e-07   \n",
       "004c3721eb88358f462cdcec6b2380b7  4.525519e-06        1.006553e-06   \n",
       "00559f56aab7e0a7749220f6aed65162  4.660007e-05        1.054067e-05   \n",
       "005b281f1a4d6f29d527c9585e9bd33c  5.638427e-05        2.641277e-03   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e  6.542528e-08        9.251381e-09   \n",
       "006870b49353779b25eeb91fed43c31a  2.446894e-05        2.980990e-04   \n",
       "0068f3a21b159ece126a28580cdad7a0  3.640290e-05        3.874598e-06   \n",
       "0069b1cc4546fc98f84f981bf9a0696a  2.863649e-06        2.902629e-07   \n",
       "0077bc3c63486ff09d3774d956af8f76  3.465208e-05        1.576871e-07   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a  2.214815e-06        1.943551e-06   \n",
       "007ed71136966728f5c0936e23c8286b  1.476029e-05        6.862193e-06   \n",
       "0081831ceb49cd64212c32b884036b82  2.883864e-07        2.772674e-07   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6  5.877161e-06        2.444182e-07   \n",
       "0092bd9e90a13403373fc0e9e1218938  3.378267e-06        4.180883e-07   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760  1.515919e-05        1.875401e-06   \n",
       "...                                        ...                 ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a  1.300555e-03        3.493752e-05   \n",
       "ff8c3ee47324ecf110e147f29747952a  2.373030e-03        1.431556e-04   \n",
       "ff8f9a768544fda79ffad62576f4d129  2.145105e-03        1.094355e-03   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda  1.530316e-03        9.649527e-04   \n",
       "ff9742bc22b5830176e85490cf54cdf7  1.667600e-03        1.542459e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd  4.529475e-03        1.392036e-04   \n",
       "ffa316c6db0919d6bad1a5ed483e6722  1.824995e-03        6.684846e-04   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3  1.947110e-03        2.429562e-04   \n",
       "ffadae851b94ece57602da7edf72d689  1.620552e-03        4.938928e-04   \n",
       "ffb3a78e4a162c173219194687eb6054  2.009147e-03        4.868688e-04   \n",
       "ffb55dbaa32939c109ef42df0668e077  2.157794e-03        2.367395e-04   \n",
       "ffb68ded4a2247b3c011840ece0a605c  1.236551e-03        1.009614e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454  2.356103e-03        6.940794e-04   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb  1.764777e-03        3.886439e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2  1.196039e-03        9.402319e-04   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f  1.714167e-03        3.205845e-04   \n",
       "ffc0233904f4d03afe1484be791e09d6  8.564599e-03        1.519725e-04   \n",
       "ffd06687c72445b0c6e8a130a0a8711a  7.510814e-03        2.802414e-04   \n",
       "ffd304c521f43819f3824177fd9efeb0  2.989845e-03        1.633345e-04   \n",
       "ffd3ce0566df4be8049c767a329faaf7  3.804704e-03        1.097539e-04   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b  5.607524e-03        1.472996e-04   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b  5.452820e-03        1.674271e-04   \n",
       "ffe42f4b4c50d4f18c2b34500f391152  4.757547e-03        1.123573e-04   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96  2.881596e-03        1.475300e-04   \n",
       "ffe9717b7937c262f849416976f7620a  4.246422e-03        1.538172e-04   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8  3.963925e-03        1.867132e-04   \n",
       "fff1ec9e6e413275984966f745a313b0  4.840374e-03        3.665255e-04   \n",
       "fff74b59b758bbbf13a5793182a9bbe4  3.792905e-03        1.172706e-04   \n",
       "fff7d50d848e8014ac1e9172dc6762a3  3.659982e-03        1.246047e-04   \n",
       "fffbff22c1f51e3dc80c4bf04089545b  4.508420e-03        1.184475e-04   \n",
       "\n",
       "                                       basenji        basset        beagle  \\\n",
       "id                                                                           \n",
       "000621fb3cbb32d8935728e48679680e  1.138542e-06  5.295524e-07  1.671713e-06   \n",
       "00102ee9d8eb90812350685311fe5890  6.845991e-07  4.384441e-08  1.060744e-07   \n",
       "0012a730dfa437f5f3613fb75efcd4ce  5.094284e-06  1.988186e-06  2.987244e-06   \n",
       "001510bc8570bbeee98c8d80c8a95ec1  2.521180e-06  4.185580e-06  2.247464e-06   \n",
       "001a5f3114548acdefa3d4da05474c2e  4.320768e-05  3.842033e-05  1.152325e-05   \n",
       "00225dcd3e4d2410dd53239f95c0352f  8.157025e-05  2.440562e-05  1.308243e-05   \n",
       "002c2a3117c2193b4d26400ce431eebd  5.798462e-06  7.488680e-07  1.094499e-06   \n",
       "002c58d413a521ae8d1a5daeb35fc803  6.868203e-07  1.953330e-07  2.205490e-07   \n",
       "002f80396f1e3db687c5932d7978b196  4.559270e-07  9.025610e-08  3.555942e-07   \n",
       "0036c6bcec6031be9e62a257b1c3c442  8.196340e-08  3.811307e-07  1.922409e-07   \n",
       "0041940322116ae58c38130f5a6f71f9  3.831426e-07  3.132643e-08  5.843959e-08   \n",
       "0042d6bf3e5f3700865886db32689436  1.020360e-06  6.358574e-07  7.490871e-07   \n",
       "004476c96f575879af4af471af65cae8  2.040090e-07  1.613456e-07  1.137709e-07   \n",
       "00485d47de966a9437ad3b33ac193b6f  8.282285e-04  4.641476e-07  2.538918e-06   \n",
       "00496f65de6cc319145ce97bd6e90360  8.843924e-06  4.592328e-05  9.502746e-01   \n",
       "004bf14426d1a830d459a9e0c0721309  5.342852e-07  7.299965e-07  2.314917e-07   \n",
       "004c3721eb88358f462cdcec6b2380b7  1.656585e-05  1.198143e-06  1.177203e-05   \n",
       "00559f56aab7e0a7749220f6aed65162  1.561832e-05  5.449977e-06  8.332829e-06   \n",
       "005b281f1a4d6f29d527c9585e9bd33c  1.637804e-05  8.840294e-07  7.459274e-06   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e  1.068944e-08  2.439670e-08  1.712889e-07   \n",
       "006870b49353779b25eeb91fed43c31a  1.246416e-05  1.165807e-05  5.520835e-06   \n",
       "0068f3a21b159ece126a28580cdad7a0  1.117395e-05  4.981906e-06  3.099899e-05   \n",
       "0069b1cc4546fc98f84f981bf9a0696a  1.992994e-07  3.677425e-08  8.540162e-08   \n",
       "0077bc3c63486ff09d3774d956af8f76  5.918946e-06  5.125749e-07  6.456523e-05   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a  1.055445e-06  3.678948e-07  2.860778e-07   \n",
       "007ed71136966728f5c0936e23c8286b  1.143845e-06  7.785997e-07  5.510272e-07   \n",
       "0081831ceb49cd64212c32b884036b82  7.389158e-07  3.466668e-05  3.455241e-06   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6  3.549487e-07  4.810110e-07  1.899612e-07   \n",
       "0092bd9e90a13403373fc0e9e1218938  2.850261e-07  1.007065e-05  1.577331e-05   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760  4.108983e-07  2.445750e-07  2.709591e-05   \n",
       "...                                        ...           ...           ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a  3.801228e-05  1.071801e-04  9.021698e-04   \n",
       "ff8c3ee47324ecf110e147f29747952a  6.018826e-04  2.225977e-04  3.663004e-03   \n",
       "ff8f9a768544fda79ffad62576f4d129  4.404694e-03  9.120057e-04  1.070755e-02   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda  1.270954e-03  8.541468e-04  1.075319e-02   \n",
       "ff9742bc22b5830176e85490cf54cdf7  3.630414e-03  1.637290e-03  6.207847e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd  3.854585e-04  9.542147e-04  1.897959e-03   \n",
       "ffa316c6db0919d6bad1a5ed483e6722  1.801848e-03  6.136572e-04  3.913291e-03   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3  2.440300e-03  5.976217e-04  6.001817e-03   \n",
       "ffadae851b94ece57602da7edf72d689  2.155272e-03  9.777733e-04  7.263242e-03   \n",
       "ffb3a78e4a162c173219194687eb6054  8.812791e-04  6.552631e-04  2.024237e-03   \n",
       "ffb55dbaa32939c109ef42df0668e077  2.458669e-03  7.925562e-04  2.529558e-03   \n",
       "ffb68ded4a2247b3c011840ece0a605c  1.393462e-03  5.272316e-04  6.061883e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454  1.979806e-03  1.223785e-03  5.075458e-03   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb  1.331790e-03  5.773196e-04  4.737018e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2  2.035963e-03  1.000639e-03  6.521039e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f  1.987119e-03  1.295375e-03  2.219513e-03   \n",
       "ffc0233904f4d03afe1484be791e09d6  6.800379e-04  8.242325e-04  1.094350e-03   \n",
       "ffd06687c72445b0c6e8a130a0a8711a  7.049338e-04  6.432774e-04  7.585618e-04   \n",
       "ffd304c521f43819f3824177fd9efeb0  6.242495e-04  6.425820e-04  6.820821e-04   \n",
       "ffd3ce0566df4be8049c767a329faaf7  4.352314e-04  8.148920e-04  3.551004e-04   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b  5.852195e-04  5.846304e-04  4.009341e-04   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b  5.731375e-04  1.161916e-03  6.910041e-04   \n",
       "ffe42f4b4c50d4f18c2b34500f391152  4.878403e-04  6.502636e-04  4.577924e-04   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96  7.761112e-04  1.477554e-03  1.021049e-03   \n",
       "ffe9717b7937c262f849416976f7620a  6.520774e-04  1.046626e-03  7.882988e-04   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8  6.464651e-04  8.231904e-04  7.952304e-04   \n",
       "fff1ec9e6e413275984966f745a313b0  4.429328e-04  4.182810e-04  6.560971e-04   \n",
       "fff74b59b758bbbf13a5793182a9bbe4  5.787721e-04  9.730349e-04  5.859593e-04   \n",
       "fff7d50d848e8014ac1e9172dc6762a3  5.772741e-04  9.260603e-04  7.139900e-04   \n",
       "fffbff22c1f51e3dc80c4bf04089545b  3.419255e-04  1.047693e-03  5.124389e-04   \n",
       "\n",
       "                                        ...            toy_poodle  \\\n",
       "id                                      ...                         \n",
       "000621fb3cbb32d8935728e48679680e        ...          2.374248e-06   \n",
       "00102ee9d8eb90812350685311fe5890        ...          3.446077e-07   \n",
       "0012a730dfa437f5f3613fb75efcd4ce        ...          1.157210e-06   \n",
       "001510bc8570bbeee98c8d80c8a95ec1        ...          3.504332e-05   \n",
       "001a5f3114548acdefa3d4da05474c2e        ...          9.508636e-05   \n",
       "00225dcd3e4d2410dd53239f95c0352f        ...          1.865894e-02   \n",
       "002c2a3117c2193b4d26400ce431eebd        ...          9.305398e-07   \n",
       "002c58d413a521ae8d1a5daeb35fc803        ...          1.045681e-06   \n",
       "002f80396f1e3db687c5932d7978b196        ...          2.752967e-08   \n",
       "0036c6bcec6031be9e62a257b1c3c442        ...          3.614792e-07   \n",
       "0041940322116ae58c38130f5a6f71f9        ...          1.352233e-07   \n",
       "0042d6bf3e5f3700865886db32689436        ...          5.181406e-07   \n",
       "004476c96f575879af4af471af65cae8        ...          1.983922e-06   \n",
       "00485d47de966a9437ad3b33ac193b6f        ...          1.734436e-04   \n",
       "00496f65de6cc319145ce97bd6e90360        ...          9.165028e-06   \n",
       "004bf14426d1a830d459a9e0c0721309        ...          2.349713e-06   \n",
       "004c3721eb88358f462cdcec6b2380b7        ...          6.628173e-06   \n",
       "00559f56aab7e0a7749220f6aed65162        ...          1.454612e-05   \n",
       "005b281f1a4d6f29d527c9585e9bd33c        ...          5.866885e-06   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e        ...          4.997832e-08   \n",
       "006870b49353779b25eeb91fed43c31a        ...          1.737159e-06   \n",
       "0068f3a21b159ece126a28580cdad7a0        ...          8.764085e-06   \n",
       "0069b1cc4546fc98f84f981bf9a0696a        ...          3.516831e-07   \n",
       "0077bc3c63486ff09d3774d956af8f76        ...          1.203925e-06   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a        ...          1.059169e-06   \n",
       "007ed71136966728f5c0936e23c8286b        ...          5.808983e-07   \n",
       "0081831ceb49cd64212c32b884036b82        ...          6.070190e-07   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6        ...          2.046336e-06   \n",
       "0092bd9e90a13403373fc0e9e1218938        ...          5.938203e-06   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760        ...          6.152976e-06   \n",
       "...                                     ...                   ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a        ...          1.439974e-04   \n",
       "ff8c3ee47324ecf110e147f29747952a        ...          1.955336e-03   \n",
       "ff8f9a768544fda79ffad62576f4d129        ...          7.947063e-03   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda        ...          5.605059e-03   \n",
       "ff9742bc22b5830176e85490cf54cdf7        ...          3.544759e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd        ...          9.999453e-04   \n",
       "ffa316c6db0919d6bad1a5ed483e6722        ...          3.676284e-03   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3        ...          7.520513e-03   \n",
       "ffadae851b94ece57602da7edf72d689        ...          4.855643e-03   \n",
       "ffb3a78e4a162c173219194687eb6054        ...          4.273310e-03   \n",
       "ffb55dbaa32939c109ef42df0668e077        ...          3.954197e-03   \n",
       "ffb68ded4a2247b3c011840ece0a605c        ...          3.589567e-01   \n",
       "ffb8bceeac5b3f587f67391ad346d454        ...          3.958312e-03   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb        ...          4.580667e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2        ...          3.305100e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f        ...          2.310285e-03   \n",
       "ffc0233904f4d03afe1484be791e09d6        ...          9.257492e-04   \n",
       "ffd06687c72445b0c6e8a130a0a8711a        ...          2.789450e-03   \n",
       "ffd304c521f43819f3824177fd9efeb0        ...          7.082867e-04   \n",
       "ffd3ce0566df4be8049c767a329faaf7        ...          9.939217e-04   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b        ...          9.690985e-04   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b        ...          1.270012e-03   \n",
       "ffe42f4b4c50d4f18c2b34500f391152        ...          7.885401e-04   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96        ...          1.428608e-03   \n",
       "ffe9717b7937c262f849416976f7620a        ...          1.139909e-03   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8        ...          1.431956e-03   \n",
       "fff1ec9e6e413275984966f745a313b0        ...          5.744582e-04   \n",
       "fff74b59b758bbbf13a5793182a9bbe4        ...          9.694375e-04   \n",
       "fff7d50d848e8014ac1e9172dc6762a3        ...          1.242786e-03   \n",
       "fffbff22c1f51e3dc80c4bf04089545b        ...          9.957794e-04   \n",
       "\n",
       "                                   toy_terrier        vizsla  walker_hound  \\\n",
       "id                                                                           \n",
       "000621fb3cbb32d8935728e48679680e  1.785193e-06  4.055153e-06  1.484346e-06   \n",
       "00102ee9d8eb90812350685311fe5890  1.318348e-07  1.287874e-07  1.945945e-06   \n",
       "0012a730dfa437f5f3613fb75efcd4ce  3.566540e-06  1.383989e-05  3.890146e-05   \n",
       "001510bc8570bbeee98c8d80c8a95ec1  6.719402e-07  1.364336e-06  1.684433e-05   \n",
       "001a5f3114548acdefa3d4da05474c2e  3.068427e-05  1.742652e-05  1.058816e-04   \n",
       "00225dcd3e4d2410dd53239f95c0352f  1.217611e-05  1.546544e-05  1.013526e-04   \n",
       "002c2a3117c2193b4d26400ce431eebd  1.358219e-05  1.120014e-06  1.153565e-06   \n",
       "002c58d413a521ae8d1a5daeb35fc803  2.712029e-07  1.615188e-07  1.336588e-06   \n",
       "002f80396f1e3db687c5932d7978b196  4.133975e-08  5.317919e-07  7.292456e-07   \n",
       "0036c6bcec6031be9e62a257b1c3c442  4.235946e-08  2.724380e-06  4.110805e-08   \n",
       "0041940322116ae58c38130f5a6f71f9  2.916574e-08  2.446995e-07  9.924033e-07   \n",
       "0042d6bf3e5f3700865886db32689436  7.872485e-07  8.700670e-07  1.091815e-06   \n",
       "004476c96f575879af4af471af65cae8  1.423407e-08  1.753844e-07  6.684213e-07   \n",
       "00485d47de966a9437ad3b33ac193b6f  8.405676e-05  3.903449e-05  3.148508e-05   \n",
       "00496f65de6cc319145ce97bd6e90360  1.764651e-06  4.469842e-07  4.586359e-02   \n",
       "004bf14426d1a830d459a9e0c0721309  1.182951e-07  2.521046e-07  2.395564e-06   \n",
       "004c3721eb88358f462cdcec6b2380b7  4.003020e-01  2.093371e-06  1.750241e-05   \n",
       "00559f56aab7e0a7749220f6aed65162  1.239206e-05  3.594315e-06  1.773534e-05   \n",
       "005b281f1a4d6f29d527c9585e9bd33c  9.111279e-06  2.522157e-04  1.639060e-05   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e  3.976060e-09  3.838167e-08  3.488314e-07   \n",
       "006870b49353779b25eeb91fed43c31a  1.449591e-05  3.727661e-06  2.154292e-05   \n",
       "0068f3a21b159ece126a28580cdad7a0  1.813295e-05  4.743918e-06  4.416525e-05   \n",
       "0069b1cc4546fc98f84f981bf9a0696a  4.951608e-05  2.932255e-07  3.877816e-07   \n",
       "0077bc3c63486ff09d3774d956af8f76  2.351800e-06  1.280780e-05  6.202739e-04   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a  1.513557e-06  5.257825e-05  3.852849e-06   \n",
       "007ed71136966728f5c0936e23c8286b  6.502476e-07  8.211035e-07  3.222061e-06   \n",
       "0081831ceb49cd64212c32b884036b82  6.812440e-08  2.154399e-06  1.924009e-05   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6  5.281859e-07  1.900404e-07  1.368106e-04   \n",
       "0092bd9e90a13403373fc0e9e1218938  1.131037e-06  1.778295e-06  2.006641e-05   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760  3.347550e-06  2.728103e-06  5.332314e-06   \n",
       "...                                        ...           ...           ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a  8.780586e-04  2.629821e-04  1.137564e-03   \n",
       "ff8c3ee47324ecf110e147f29747952a  2.017706e-03  4.806453e-04  1.350435e-03   \n",
       "ff8f9a768544fda79ffad62576f4d129  2.633009e-03  9.520095e-04  8.126812e-04   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda  1.750768e-03  7.412827e-04  5.526019e-04   \n",
       "ff9742bc22b5830176e85490cf54cdf7  3.070124e-03  7.178568e-04  6.159370e-04   \n",
       "ff9b317696e29ca8cd27da30a896edcd  3.042459e-03  9.122242e-04  4.627034e-04   \n",
       "ffa316c6db0919d6bad1a5ed483e6722  4.330271e-03  5.044659e-04  3.978292e-04   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3  3.441467e-03  1.898998e-03  1.071761e-03   \n",
       "ffadae851b94ece57602da7edf72d689  3.172681e-03  1.186823e-03  5.828710e-04   \n",
       "ffb3a78e4a162c173219194687eb6054  3.330858e-03  1.778421e-03  4.751439e-04   \n",
       "ffb55dbaa32939c109ef42df0668e077  3.756594e-03  1.055770e-03  6.218482e-04   \n",
       "ffb68ded4a2247b3c011840ece0a605c  2.090885e-03  4.334618e-04  3.028422e-04   \n",
       "ffb8bceeac5b3f587f67391ad346d454  2.913087e-03  1.802972e-03  7.366999e-04   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb  2.538815e-03  1.540784e-03  7.188167e-04   \n",
       "ffbcda9eb84339cc5be15fd9900596a2  2.301659e-03  1.128342e-03  6.505231e-04   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f  2.519349e-03  1.331496e-03  6.071968e-04   \n",
       "ffc0233904f4d03afe1484be791e09d6  2.307885e-03  6.949962e-04  3.656098e-03   \n",
       "ffd06687c72445b0c6e8a130a0a8711a  2.958815e-03  6.831227e-04  4.514118e-03   \n",
       "ffd304c521f43819f3824177fd9efeb0  1.498983e-03  7.218044e-04  3.843926e-03   \n",
       "ffd3ce0566df4be8049c767a329faaf7  1.396697e-03  5.923077e-04  1.994371e-03   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b  1.666153e-03  6.423439e-04  3.441659e-03   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b  3.019352e-03  1.496552e-03  4.600452e-03   \n",
       "ffe42f4b4c50d4f18c2b34500f391152  1.583360e-03  3.379470e-04  3.437413e-03   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96  1.923084e-03  1.893288e-03  5.240478e-03   \n",
       "ffe9717b7937c262f849416976f7620a  1.976757e-03  1.025917e-03  4.733603e-03   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8  2.501904e-03  7.785773e-04  2.157065e-03   \n",
       "fff1ec9e6e413275984966f745a313b0  2.391617e-03  1.006589e-02  2.863772e-03   \n",
       "fff74b59b758bbbf13a5793182a9bbe4  1.822702e-03  1.311348e-03  6.736250e-03   \n",
       "fff7d50d848e8014ac1e9172dc6762a3  2.055848e-03  8.381747e-04  5.095882e-03   \n",
       "fffbff22c1f51e3dc80c4bf04089545b  1.364771e-03  8.391312e-04  4.765088e-03   \n",
       "\n",
       "                                    weimaraner  welsh_springer_spaniel  \\\n",
       "id                                                                       \n",
       "000621fb3cbb32d8935728e48679680e  2.086060e-06            1.321018e-06   \n",
       "00102ee9d8eb90812350685311fe5890  4.204300e-07            8.368110e-08   \n",
       "0012a730dfa437f5f3613fb75efcd4ce  1.493861e-05            6.236738e-04   \n",
       "001510bc8570bbeee98c8d80c8a95ec1  7.183248e-07            1.022453e-06   \n",
       "001a5f3114548acdefa3d4da05474c2e  2.795637e-05            4.793436e-06   \n",
       "00225dcd3e4d2410dd53239f95c0352f  7.207926e-05            6.348755e-05   \n",
       "002c2a3117c2193b4d26400ce431eebd  7.245778e-07            6.739002e-07   \n",
       "002c58d413a521ae8d1a5daeb35fc803  1.216229e-06            3.748230e-07   \n",
       "002f80396f1e3db687c5932d7978b196  8.282490e-07            4.376617e-07   \n",
       "0036c6bcec6031be9e62a257b1c3c442  4.909796e-07            7.135275e-07   \n",
       "0041940322116ae58c38130f5a6f71f9  3.195797e-07            3.625490e-08   \n",
       "0042d6bf3e5f3700865886db32689436  3.783539e-06            1.977628e-06   \n",
       "004476c96f575879af4af471af65cae8  6.720700e-08            1.243804e-07   \n",
       "00485d47de966a9437ad3b33ac193b6f  1.416259e-06            1.674485e-05   \n",
       "00496f65de6cc319145ce97bd6e90360  6.851596e-06            1.743069e-07   \n",
       "004bf14426d1a830d459a9e0c0721309  5.289388e-07            1.438315e-06   \n",
       "004c3721eb88358f462cdcec6b2380b7  3.682175e-07            1.418128e-06   \n",
       "00559f56aab7e0a7749220f6aed65162  1.670246e-05            1.358856e-05   \n",
       "005b281f1a4d6f29d527c9585e9bd33c  3.362257e-06            4.610071e-05   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e  8.601581e-08            2.387110e-08   \n",
       "006870b49353779b25eeb91fed43c31a  1.913749e-05            2.108690e-05   \n",
       "0068f3a21b159ece126a28580cdad7a0  9.589452e-06            3.252769e-06   \n",
       "0069b1cc4546fc98f84f981bf9a0696a  5.792164e-06            3.181638e-07   \n",
       "0077bc3c63486ff09d3774d956af8f76  1.476550e-05            2.270051e-07   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a  2.223439e-06            3.454544e-04   \n",
       "007ed71136966728f5c0936e23c8286b  1.315071e-06            2.122696e-06   \n",
       "0081831ceb49cd64212c32b884036b82  3.209104e-06            2.362931e-07   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6  4.682070e-05            7.165315e-08   \n",
       "0092bd9e90a13403373fc0e9e1218938  2.904002e-06            7.728474e-04   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760  1.422944e-05            1.117088e-06   \n",
       "...                                        ...                     ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a  1.252794e-03            1.874199e-03   \n",
       "ff8c3ee47324ecf110e147f29747952a  3.452987e-03            2.277133e-03   \n",
       "ff8f9a768544fda79ffad62576f4d129  3.811576e-03            1.805652e-03   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda  3.918224e-03            2.268838e-03   \n",
       "ff9742bc22b5830176e85490cf54cdf7  5.290909e-03            1.568944e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd  3.846206e-03            4.199636e-03   \n",
       "ffa316c6db0919d6bad1a5ed483e6722  3.216823e-03            1.930966e-03   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3  4.172950e-03            1.097154e-02   \n",
       "ffadae851b94ece57602da7edf72d689  8.071044e-03            2.346577e-03   \n",
       "ffb3a78e4a162c173219194687eb6054  5.290998e-03            2.434424e-03   \n",
       "ffb55dbaa32939c109ef42df0668e077  1.026116e-02            1.445651e-03   \n",
       "ffb68ded4a2247b3c011840ece0a605c  2.993664e-03            1.619336e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454  6.406320e-03            2.928309e-03   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb  7.437547e-03            1.799076e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2  5.518061e-03            2.442129e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f  1.550712e-02            2.922016e-03   \n",
       "ffc0233904f4d03afe1484be791e09d6  2.288956e-03            4.978699e-03   \n",
       "ffd06687c72445b0c6e8a130a0a8711a  1.749005e-03            5.467598e-03   \n",
       "ffd304c521f43819f3824177fd9efeb0  2.672940e-03            3.593700e-03   \n",
       "ffd3ce0566df4be8049c767a329faaf7  7.162467e-03            4.793789e-03   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b  2.923312e-03            4.671426e-03   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b  2.928839e-03            6.609668e-03   \n",
       "ffe42f4b4c50d4f18c2b34500f391152  1.948142e-03            3.390699e-03   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96  3.230336e-03            6.001286e-03   \n",
       "ffe9717b7937c262f849416976f7620a  3.438285e-03            3.377267e-03   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8  4.873876e-03            4.003550e-03   \n",
       "fff1ec9e6e413275984966f745a313b0  4.649872e-01            2.306704e-03   \n",
       "fff74b59b758bbbf13a5793182a9bbe4  5.211489e-03            4.461931e-03   \n",
       "fff7d50d848e8014ac1e9172dc6762a3  3.258373e-03            5.514011e-03   \n",
       "fffbff22c1f51e3dc80c4bf04089545b  2.458749e-03            3.850045e-03   \n",
       "\n",
       "                                  west_highland_white_terrier       whippet  \\\n",
       "id                                                                            \n",
       "000621fb3cbb32d8935728e48679680e                 1.111028e-06  7.935586e-07   \n",
       "00102ee9d8eb90812350685311fe5890                 5.671037e-06  1.338578e-07   \n",
       "0012a730dfa437f5f3613fb75efcd4ce                 7.554130e-07  7.291586e-06   \n",
       "001510bc8570bbeee98c8d80c8a95ec1                 4.088128e-06  7.775871e-06   \n",
       "001a5f3114548acdefa3d4da05474c2e                 8.988655e-05  6.529916e-05   \n",
       "00225dcd3e4d2410dd53239f95c0352f                 1.420171e-05  3.713567e-04   \n",
       "002c2a3117c2193b4d26400ce431eebd                 8.679771e-07  2.407699e-06   \n",
       "002c58d413a521ae8d1a5daeb35fc803                 2.529896e-05  8.843904e-07   \n",
       "002f80396f1e3db687c5932d7978b196                 5.864220e-07  1.067287e-05   \n",
       "0036c6bcec6031be9e62a257b1c3c442                 3.333645e-07  1.955779e-08   \n",
       "0041940322116ae58c38130f5a6f71f9                 4.948351e-08  1.334656e-06   \n",
       "0042d6bf3e5f3700865886db32689436                 1.440975e-05  2.402185e-06   \n",
       "004476c96f575879af4af471af65cae8                 3.945402e-08  6.421771e-08   \n",
       "00485d47de966a9437ad3b33ac193b6f                 6.256734e-06  9.121104e-06   \n",
       "00496f65de6cc319145ce97bd6e90360                 8.440037e-06  7.948117e-06   \n",
       "004bf14426d1a830d459a9e0c0721309                 1.991412e-07  2.112528e-06   \n",
       "004c3721eb88358f462cdcec6b2380b7                 5.866858e-06  6.982168e-07   \n",
       "00559f56aab7e0a7749220f6aed65162                 2.508750e-05  1.399347e-04   \n",
       "005b281f1a4d6f29d527c9585e9bd33c                 1.827039e-06  8.560093e-06   \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e                 5.967052e-09  2.862737e-05   \n",
       "006870b49353779b25eeb91fed43c31a                 1.171934e-04  1.968024e-05   \n",
       "0068f3a21b159ece126a28580cdad7a0                 1.097376e-05  5.653644e-05   \n",
       "0069b1cc4546fc98f84f981bf9a0696a                 5.579333e-07  8.525297e-05   \n",
       "0077bc3c63486ff09d3774d956af8f76                 2.747134e-06  4.593367e-06   \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a                 6.509767e-07  1.387930e-06   \n",
       "007ed71136966728f5c0936e23c8286b                 1.851179e-06  3.009313e-06   \n",
       "0081831ceb49cd64212c32b884036b82                 5.259891e-07  1.100556e-06   \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6                 3.290382e-07  1.567415e-04   \n",
       "0092bd9e90a13403373fc0e9e1218938                 1.654582e-06  3.836514e-07   \n",
       "009a3c4f6626e4750f74ceb8e8ed8760                 8.578095e-07  4.364680e-06   \n",
       "...                                                       ...           ...   \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a                 3.623060e-04  4.769360e-04   \n",
       "ff8c3ee47324ecf110e147f29747952a                 4.116343e-03  4.140505e-03   \n",
       "ff8f9a768544fda79ffad62576f4d129                 3.328612e-03  2.842917e-03   \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda                 2.686395e-03  1.732053e-02   \n",
       "ff9742bc22b5830176e85490cf54cdf7                 2.715854e-03  4.493236e-03   \n",
       "ff9b317696e29ca8cd27da30a896edcd                 4.624036e-03  6.984702e-03   \n",
       "ffa316c6db0919d6bad1a5ed483e6722                 3.885622e-03  4.733626e-03   \n",
       "ffa76e07dc686174c2a55fdbdca68bb3                 4.053976e-03  5.841836e-03   \n",
       "ffadae851b94ece57602da7edf72d689                 3.977664e-03  4.067934e-03   \n",
       "ffb3a78e4a162c173219194687eb6054                 2.051270e-03  7.263437e-03   \n",
       "ffb55dbaa32939c109ef42df0668e077                 5.045913e-03  4.442484e-03   \n",
       "ffb68ded4a2247b3c011840ece0a605c                 2.341817e-03  4.307736e-03   \n",
       "ffb8bceeac5b3f587f67391ad346d454                 4.925454e-03  4.628229e-03   \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb                 3.963049e-03  3.167774e-03   \n",
       "ffbcda9eb84339cc5be15fd9900596a2                 4.117927e-03  2.891217e-03   \n",
       "ffbcf032231bb0b025f9070d42ab7e8f                 4.385806e-03  6.422954e-03   \n",
       "ffc0233904f4d03afe1484be791e09d6                 1.814516e-03  3.363931e-03   \n",
       "ffd06687c72445b0c6e8a130a0a8711a                 1.796329e-03  2.439900e-03   \n",
       "ffd304c521f43819f3824177fd9efeb0                 3.677064e-03  4.713271e-03   \n",
       "ffd3ce0566df4be8049c767a329faaf7                 2.617827e-03  4.349492e-03   \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b                 3.592029e-03  4.729989e-03   \n",
       "ffe2315cf566e039516f5a4a5e52ff1b                 3.935489e-03  7.174117e-03   \n",
       "ffe42f4b4c50d4f18c2b34500f391152                 3.402301e-03  4.505563e-03   \n",
       "ffe563b1b8c0dcd0797c4362c6754b96                 4.334966e-03  6.661402e-03   \n",
       "ffe9717b7937c262f849416976f7620a                 2.772563e-03  3.641548e-03   \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8                 3.855485e-03  3.290834e-03   \n",
       "fff1ec9e6e413275984966f745a313b0                 1.733376e-03  2.121599e-03   \n",
       "fff74b59b758bbbf13a5793182a9bbe4                 3.645908e-03  4.619906e-03   \n",
       "fff7d50d848e8014ac1e9172dc6762a3                 4.018990e-03  4.193271e-03   \n",
       "fffbff22c1f51e3dc80c4bf04089545b                 2.556928e-03  5.781852e-03   \n",
       "\n",
       "                                  wire-haired_fox_terrier  yorkshire_terrier  \n",
       "id                                                                            \n",
       "000621fb3cbb32d8935728e48679680e             8.508604e-06       2.108932e-06  \n",
       "00102ee9d8eb90812350685311fe5890             1.910671e-07       2.255847e-07  \n",
       "0012a730dfa437f5f3613fb75efcd4ce             4.387619e-05       1.134878e-05  \n",
       "001510bc8570bbeee98c8d80c8a95ec1             3.313334e-07       3.766275e-06  \n",
       "001a5f3114548acdefa3d4da05474c2e             1.827006e-04       2.694604e-04  \n",
       "00225dcd3e4d2410dd53239f95c0352f             3.056568e-03       7.871884e-05  \n",
       "002c2a3117c2193b4d26400ce431eebd             2.469649e-05       1.877901e-04  \n",
       "002c58d413a521ae8d1a5daeb35fc803             8.565381e-07       7.672539e-07  \n",
       "002f80396f1e3db687c5932d7978b196             5.662501e-06       3.030166e-07  \n",
       "0036c6bcec6031be9e62a257b1c3c442             2.747989e-07       3.235333e-07  \n",
       "0041940322116ae58c38130f5a6f71f9             5.199559e-07       5.959630e-07  \n",
       "0042d6bf3e5f3700865886db32689436             3.921869e-03       3.285132e-06  \n",
       "004476c96f575879af4af471af65cae8             3.140024e-07       6.257542e-08  \n",
       "00485d47de966a9437ad3b33ac193b6f             1.602485e-05       2.216573e-03  \n",
       "00496f65de6cc319145ce97bd6e90360             4.035751e-06       3.604878e-06  \n",
       "004bf14426d1a830d459a9e0c0721309             4.178343e-07       1.644624e-07  \n",
       "004c3721eb88358f462cdcec6b2380b7             6.776182e-06       4.207237e-06  \n",
       "00559f56aab7e0a7749220f6aed65162             6.265052e-02       3.270642e-05  \n",
       "005b281f1a4d6f29d527c9585e9bd33c             3.343322e-03       3.881075e-05  \n",
       "005b6c6c76fefd6b458ef6fb6e54da6e             2.311107e-07       1.975831e-07  \n",
       "006870b49353779b25eeb91fed43c31a             5.036361e-04       4.825609e-05  \n",
       "0068f3a21b159ece126a28580cdad7a0             1.013314e-05       6.716750e-06  \n",
       "0069b1cc4546fc98f84f981bf9a0696a             1.172837e-06       5.560750e-07  \n",
       "0077bc3c63486ff09d3774d956af8f76             2.316305e-06       3.562065e-06  \n",
       "00780e5d2bf4f7e4b5f96d08ddde669a             9.946339e-06       5.185900e-06  \n",
       "007ed71136966728f5c0936e23c8286b             5.615147e-07       1.632190e-06  \n",
       "0081831ceb49cd64212c32b884036b82             1.027620e-06       3.220683e-07  \n",
       "00846c0edd5aa4f10ee5e9b84d7310a6             7.675574e-06       1.275189e-06  \n",
       "0092bd9e90a13403373fc0e9e1218938             9.305375e-06       1.988961e-06  \n",
       "009a3c4f6626e4750f74ceb8e8ed8760             3.400943e-01       2.388982e-06  \n",
       "...                                                   ...                ...  \n",
       "ff7e00f66ebf0042c5ccb74170eecb1a             5.703698e-05       8.677351e-05  \n",
       "ff8c3ee47324ecf110e147f29747952a             1.060958e-03       1.679272e-03  \n",
       "ff8f9a768544fda79ffad62576f4d129             4.866639e-03       1.436774e-02  \n",
       "ff8ff2e36aa2a20d5d8585e2f686dbda             2.826977e-03       2.275816e-02  \n",
       "ff9742bc22b5830176e85490cf54cdf7             6.109527e-03       1.469883e-02  \n",
       "ff9b317696e29ca8cd27da30a896edcd             7.696662e-04       2.305705e-03  \n",
       "ffa316c6db0919d6bad1a5ed483e6722             2.206168e-03       1.679346e-02  \n",
       "ffa76e07dc686174c2a55fdbdca68bb3             3.863165e-03       1.009820e-02  \n",
       "ffadae851b94ece57602da7edf72d689             4.056240e-03       9.175685e-03  \n",
       "ffb3a78e4a162c173219194687eb6054             4.758568e-02       6.344813e-03  \n",
       "ffb55dbaa32939c109ef42df0668e077             4.424215e-03       1.369816e-02  \n",
       "ffb68ded4a2247b3c011840ece0a605c             2.073114e-03       1.570046e-02  \n",
       "ffb8bceeac5b3f587f67391ad346d454             4.613846e-03       1.095974e-02  \n",
       "ffbb6663b38e903dbd1efa5dce2d0bbb             3.079897e-03       5.119002e-03  \n",
       "ffbcda9eb84339cc5be15fd9900596a2             3.324875e-03       1.484843e-02  \n",
       "ffbcf032231bb0b025f9070d42ab7e8f             5.449563e-03       8.415007e-03  \n",
       "ffc0233904f4d03afe1484be791e09d6             1.470909e-03       1.435996e-03  \n",
       "ffd06687c72445b0c6e8a130a0a8711a             1.073239e-03       3.927403e-03  \n",
       "ffd304c521f43819f3824177fd9efeb0             8.053449e-04       1.894725e-03  \n",
       "ffd3ce0566df4be8049c767a329faaf7             1.070310e-03       1.691112e-03  \n",
       "ffd87c3e44faa0e3ee5fbbdc4c63b59b             9.088660e-04       1.684781e-03  \n",
       "ffe2315cf566e039516f5a4a5e52ff1b             1.778893e-03       2.545959e-03  \n",
       "ffe42f4b4c50d4f18c2b34500f391152             8.631645e-04       1.938521e-03  \n",
       "ffe563b1b8c0dcd0797c4362c6754b96             1.053633e-03       2.239241e-03  \n",
       "ffe9717b7937c262f849416976f7620a             1.149837e-03       2.278682e-03  \n",
       "ffeda8623d4eee33c6d1156a2ecbfcf8             1.790841e-03       3.308820e-03  \n",
       "fff1ec9e6e413275984966f745a313b0             1.188881e-03       2.337447e-03  \n",
       "fff74b59b758bbbf13a5793182a9bbe4             1.176102e-03       1.544361e-03  \n",
       "fff7d50d848e8014ac1e9172dc6762a3             1.300832e-03       1.777664e-03  \n",
       "fffbff22c1f51e3dc80c4bf04089545b             9.931800e-04       1.940245e-03  \n",
       "\n",
       "[10357 rows x 120 columns]>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_k_fold_ensemble.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# def test_and_tell():\n",
    "#     i = 0\n",
    "#     total_acc = 0\n",
    "#     errors_list = []\n",
    "#     for x,y in val_generator:\n",
    "#         val_out = model_final.predict_on_batch(x)\n",
    "#         prediction = np.argmax(val_out,axis=1)\n",
    "#         answer = np.argmax(y,axis=1)\n",
    "#         correctness = (prediction == answer)\n",
    "#         accuracy = np.mean(correctness)\n",
    "#         total_acc += accuracy\n",
    "#         print('accuracy = {}    ---------------------------------------------------------------------'.format(accuracy))\n",
    "#         for j in range(len(correctness)):\n",
    "#             if not correctness[j]:\n",
    "#                 title = 'should be {}, estimated to be {}'.format(idx_to_targets_dict[answer[j]],idx_to_targets_dict[prediction[j]])\n",
    "#                 error_pic = x[j]\n",
    "#                 errors_list.append((error_pic,title))\n",
    "#         if i >= len(val_generator):\n",
    "#             break\n",
    "#         i += 1 \n",
    "#     print('total accuracy = {}    ---------------------------------------------------------------------'.format(total_acc/(i-1)))\n",
    "#     return errors_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# errors_list = test_and_tell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model_final.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model_final.trainable = True\n",
    "\n",
    "# set_trainable = False\n",
    "# for layer in model_final.layers:\n",
    "#     if layer.name == 'block12_sepconv1_act':\n",
    "#         set_trainable = True\n",
    "#     if set_trainable:\n",
    "#         layer.trainable = True\n",
    "#     else:\n",
    "#         layer.trainable = False\n",
    "\n",
    "# model_final.trainable_weights\n",
    "\n",
    "# learning_rate = 0.00001\n",
    "\n",
    "# model_final.compile(optimizer=keras.optimizers.Adam(lr=learning_rate),\n",
    "#                     loss=keras.losses.categorical_crossentropy,\n",
    "#                     metrics=[keras.metrics.categorical_accuracy])\n",
    "\n",
    "# callbacks_list_final = [\n",
    "#     keras.callbacks.ModelCheckpoint(\n",
    "#         save_best_only=True,\n",
    "#         filepath='dogbreed_fituning_0203.hdf5')\n",
    "#     ]\n",
    "\n",
    "# his = model_final.fit_generator(test_generator, epochs=100,callbacks=callbacks_list_final,validation_data=val_generator)"
   ]
  }
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