train_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose GPU settings, import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:02:28.538177Z",
     "start_time": "2020-06-09T23:02:25.442135Z"
    },
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "apJbCsBHl-2A"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "num_GPU = len(os.environ[\"CUDA_VISIBLE_DEVICES\"].split(',')) \n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.datasets import mnist\n",
    "\n",
    "from keras.layers import Conv2D, MaxPool2D\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical, multi_gpu_model, plot_model\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Dropout\n",
    "from keras.models import Model\n",
    "\n",
    "config = tf.ConfigProto() \n",
    "config.gpu_options.allow_growth=True \n",
    "session = tf.Session(config=config)\n",
    "K.set_session(session)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:02:29.027621Z",
     "start_time": "2020-06-09T23:02:28.542281Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import glob\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline\n",
    "\n",
    "import keras_helper\n",
    "from keras_helper import *\n",
    "from skimage.transform import resize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some training parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:02:29.035343Z",
     "start_time": "2020-06-09T23:02:29.031387Z"
    }
   },
   "outputs": [],
   "source": [
    "h,w=64,64\n",
    "nepochs=75\n",
    "wt_path = 'wts4' #Where to store network weights\n",
    "batch_size = 32\n",
    "\n",
    "if (not os.path.isdir(wt_path)):\n",
    "    os.mkdir(wt_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read experimental diffraction data and reconstructed images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:02:41.612879Z",
     "start_time": "2020-06-09T23:02:29.038488Z"
    }
   },
   "outputs": [],
   "source": [
    "data_diffr = np.load('data/20191008_39_diff.npz')['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:18.751866Z",
     "start_time": "2020-06-09T23:02:41.616917Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(161, 161, 256, 256)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24ef44751d8f408abc03f94d314213a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=161), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(data_diffr.shape)\n",
    "#plt.matshow(np.log10(data_diffr[0,0]))\n",
    "\n",
    "data_diffr_red = np.zeros((data_diffr.shape[0],data_diffr.shape[1],64,64), float)\n",
    "for i in tqdm(range(data_diffr.shape[0])):\n",
    "    for j in range(data_diffr.shape[1]):\n",
    "        data_diffr_red[i,j] = resize(data_diffr[i,j,32:-32,32:-32],(64,64),preserve_range=True, anti_aliasing=True)\n",
    "        data_diffr_red[i,j] = np.where(data_diffr_red[i,j]<3,0,data_diffr_red[i,j])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:22.185270Z",
     "start_time": "2020-06-09T23:03:18.754735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161, 161, 64, 64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#amp = np.load('../expt_data/s26_data/20191008_30_10nm_amp.npz')['arr_0']\n",
    "#ph = np.load('../expt_data/s26_data/20191008_39_10nm.npz')['arr_0']\n",
    "real_space = np.load('data/20191008_39_amp_pha_10nm_full.npy')\n",
    "amp = np.abs(real_space)\n",
    "ph = np.angle(real_space)\n",
    "amp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:22.895041Z",
     "start_time": "2020-06-09T23:03:22.187370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7a7c9339b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0609 23:03:22.267590 140165287601984 font_manager.py:1282] findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(20,10))\n",
    "ax[0].imshow(amp[:,:,32,32])\n",
    "ax[1].imshow(ph[:,:,32,32])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data and then shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:22.900677Z",
     "start_time": "2020-06-09T23:03:22.897049Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n"
     ]
    }
   ],
   "source": [
    "nlines = 100 #How many lines of data to use for training?\n",
    "nltest = 60 #How many lines for the test set?\n",
    "tst_strt = amp.shape[0]-nltest #Where to index from\n",
    "print(tst_strt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:23.783547Z",
     "start_time": "2020-06-09T23:03:22.903186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16100, 64, 64, 1) (3600, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train = data_diffr_red[:nlines,:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "X_test = data_diffr_red[tst_strt:,tst_strt:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_I_train = amp[:nlines,:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_I_test = amp[tst_strt:,tst_strt:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_phi_train = ph[:nlines,:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_phi_test = ph[tst_strt:,tst_strt:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "\n",
    "ntrain = X_train.shape[0]*X_train.shape[1]\n",
    "ntest = X_test.shape[0]*X_test.shape[1]\n",
    "\n",
    "print(X_train.shape, X_test.shape)\n",
    "\n",
    "X_train, Y_I_train, Y_phi_train = shuffle(X_train, Y_I_train, Y_phi_train, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the test data for later use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:24.069064Z",
     "start_time": "2020-06-09T23:03:23.785738Z"
    }
   },
   "outputs": [],
   "source": [
    "np.save('data/X_test.npy', X_test) #Diffraction data\n",
    "np.save('data/Y_I_test.npy', Y_I_test) #Intensity data\n",
    "np.save('data/Y_phi_test.npy', Y_phi_test) #Intensity data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the network structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:03:24.572470Z",
     "start_time": "2020-06-09T23:03:24.071581Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0609 23:03:24.084986 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "W0609 23:03:24.086560 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0609 23:03:24.091528 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0609 23:03:24.094970 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0609 23:03:24.098064 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0609 23:03:24.127780 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0609 23:03:24.213057 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
      "\n",
      "W0609 23:03:24.525224 140165287601984 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 64, 64, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 64, 64, 32)   320         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 64, 64, 32)   9248        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 32)   0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 32, 32, 64)   18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 32, 32, 64)   36928       conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 64)   0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 16, 16, 128)  73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 16, 16, 128)  147584      conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 128)    0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 8, 8, 128)    147584      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 8, 8, 128)    147584      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 8, 8, 128)    147584      conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 8, 8, 128)    147584      conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 16, 16, 128)  0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 16, 16, 128)  0           conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 16, 64)   73792       up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 64)   73792       up_sampling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 32, 32, 64)   0           conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2D)  (None, 32, 32, 64)   0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 32)   18464       up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 32, 32, 32)   18464       up_sampling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 64, 64, 32)   0           conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_6 (UpSampling2D)  (None, 64, 64, 32)   0           conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 64, 64, 1)    289         up_sampling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 64, 64, 1)    289         up_sampling2d_6[0][0]            \n",
      "==================================================================================================\n",
      "Total params: 1,154,210\n",
      "Trainable params: 1,154,210\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "K.clear_session()\n",
    "np.random.seed(123)\n",
    "model=Sequential()\n",
    "\n",
    "files=glob.glob('%s/*' %wt_path)\n",
    "for file in files:\n",
    "    os.remove(file)\n",
    "\n",
    "\n",
    "input_img = Input(shape=(h, w, 1)) \n",
    "\n",
    "x = Conv_Pool_block(input_img,32,w1=3,w2=3,p1=2,p2=2, padding='same', data_format='channels_last')\n",
    "x = Conv_Pool_block(x,64,w1=3,w2=3,p1=2,p2=2, padding='same', data_format='channels_last')\n",
    "x = Conv_Pool_block(x,128,w1=3,w2=3,p1=2,p2=2, padding='same', data_format='channels_last')\n",
    "#Activations are all ReLu\n",
    "\n",
    "encoded=x\n",
    "\n",
    "#Decoding arm 1\n",
    "x1=Conv_Up_block(encoded,128,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x1=Conv_Up_block(x1,64,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x1=Conv_Up_block(x1,32,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "\n",
    "decoded1 = Conv2D(1, (3, 3), padding='same')(x1)\n",
    "\n",
    "\n",
    "#Decoding arm 2\n",
    "x2=Conv_Up_block(encoded,128,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x2=Conv_Up_block(x2,64,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x2=Conv_Up_block(x2,32,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "\n",
    "decoded2 = Conv2D(1, (3, 3), padding='same')(x2)\n",
    "\n",
    "#Put together\n",
    "autoencoder = Model(input_img, [decoded1, decoded2])\n",
    "parallel_model = ModelMGPU(autoencoder, gpus=num_GPU)\n",
    "parallel_model.compile(optimizer='adam', loss='mean_absolute_error')\n",
    "\n",
    "print (autoencoder.summary())\n",
    "#plot_model(autoencoder, to_file='paper_data/str_model.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:17:41.204720Z",
     "start_time": "2020-06-09T23:03:24.574491Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15295 samples, validate on 805 samples\n",
      "Epoch 1/75\n",
      "15295/15295 [==============================] - 16s 1ms/step - loss: 0.6867 - conv2d_13_loss: 0.0408 - conv2d_20_loss: 0.6459 - val_loss: 0.4768 - val_conv2d_13_loss: 0.0075 - val_conv2d_20_loss: 0.4693\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.47680, saving model to wts4/weights.01.hdf5\n",
      "Epoch 2/75\n",
      "15295/15295 [==============================] - 14s 884us/step - loss: 0.4574 - conv2d_13_loss: 0.0073 - conv2d_20_loss: 0.4501 - val_loss: 0.4283 - val_conv2d_13_loss: 0.0068 - val_conv2d_20_loss: 0.4215\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.47680 to 0.42832, saving model to wts4/weights.02.hdf5\n",
      "Epoch 3/75\n",
      "15295/15295 [==============================] - 12s 801us/step - loss: 0.3968 - conv2d_13_loss: 0.0067 - conv2d_20_loss: 0.3901 - val_loss: 0.3754 - val_conv2d_13_loss: 0.0065 - val_conv2d_20_loss: 0.3689\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.42832 to 0.37540, saving model to wts4/weights.03.hdf5\n",
      "Epoch 4/75\n",
      "15295/15295 [==============================] - 12s 767us/step - loss: 0.3638 - conv2d_13_loss: 0.0065 - conv2d_20_loss: 0.3573 - val_loss: 0.3483 - val_conv2d_13_loss: 0.0064 - val_conv2d_20_loss: 0.3420\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.37540 to 0.34834, saving model to wts4/weights.04.hdf5\n",
      "Epoch 5/75\n",
      "15295/15295 [==============================] - 12s 754us/step - loss: 0.3355 - conv2d_13_loss: 0.0063 - conv2d_20_loss: 0.3292 - val_loss: 0.3295 - val_conv2d_13_loss: 0.0062 - val_conv2d_20_loss: 0.3233\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.34834 to 0.32951, saving model to wts4/weights.05.hdf5\n",
      "Epoch 6/75\n",
      "15295/15295 [==============================] - 11s 750us/step - loss: 0.3173 - conv2d_13_loss: 0.0063 - conv2d_20_loss: 0.3110 - val_loss: 0.3156 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.3095\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.32951 to 0.31562, saving model to wts4/weights.06.hdf5\n",
      "Epoch 7/75\n",
      "15295/15295 [==============================] - 11s 735us/step - loss: 0.3005 - conv2d_13_loss: 0.0062 - conv2d_20_loss: 0.2943 - val_loss: 0.2979 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.2918\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.31562 to 0.29786, saving model to wts4/weights.07.hdf5\n",
      "Epoch 8/75\n",
      "15295/15295 [==============================] - 11s 742us/step - loss: 0.2855 - conv2d_13_loss: 0.0061 - conv2d_20_loss: 0.2794 - val_loss: 0.2856 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.2795\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.29786 to 0.28555, saving model to wts4/weights.08.hdf5\n",
      "Epoch 9/75\n",
      "15295/15295 [==============================] - 12s 758us/step - loss: 0.2745 - conv2d_13_loss: 0.0061 - conv2d_20_loss: 0.2685 - val_loss: 0.2881 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.2821\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.28555\n",
      "Epoch 10/75\n",
      "15295/15295 [==============================] - 11s 724us/step - loss: 0.2625 - conv2d_13_loss: 0.0060 - conv2d_20_loss: 0.2565 - val_loss: 0.2586 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.2527\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.28555 to 0.25860, saving model to wts4/weights.10.hdf5\n",
      "Epoch 11/75\n",
      "15295/15295 [==============================] - 12s 760us/step - loss: 0.2555 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.2496 - val_loss: 0.2604 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.2545\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.25860\n",
      "Epoch 12/75\n",
      "15295/15295 [==============================] - 12s 760us/step - loss: 0.2444 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.2385 - val_loss: 0.2493 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.2435\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.25860 to 0.24930, saving model to wts4/weights.12.hdf5\n",
      "Epoch 13/75\n",
      "15295/15295 [==============================] - 11s 742us/step - loss: 0.2356 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.2298 - val_loss: 0.2445 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.2388\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.24930 to 0.24452, saving model to wts4/weights.13.hdf5\n",
      "Epoch 14/75\n",
      "15295/15295 [==============================] - 11s 735us/step - loss: 0.2282 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.2225 - val_loss: 0.2402 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.2344\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.24452 to 0.24025, saving model to wts4/weights.14.hdf5\n",
      "Epoch 15/75\n",
      "15295/15295 [==============================] - 11s 702us/step - loss: 0.2213 - conv2d_13_loss: 0.0057 - conv2d_20_loss: 0.2156 - val_loss: 0.2358 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.2301\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.24025 to 0.23584, saving model to wts4/weights.15.hdf5\n",
      "Epoch 16/75\n",
      "15295/15295 [==============================] - 12s 782us/step - loss: 0.2164 - conv2d_13_loss: 0.0057 - conv2d_20_loss: 0.2108 - val_loss: 0.2271 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.2215\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.23584 to 0.22715, saving model to wts4/weights.16.hdf5\n",
      "Epoch 17/75\n",
      "15295/15295 [==============================] - 11s 695us/step - loss: 0.2098 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.2042 - val_loss: 0.2237 - val_conv2d_13_loss: 0.0056 - val_conv2d_20_loss: 0.2180\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.22715 to 0.22366, saving model to wts4/weights.17.hdf5\n",
      "Epoch 18/75\n",
      "15295/15295 [==============================] - 11s 726us/step - loss: 0.2032 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.1976 - val_loss: 0.2201 - val_conv2d_13_loss: 0.0056 - val_conv2d_20_loss: 0.2145\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.22366 to 0.22012, saving model to wts4/weights.18.hdf5\n",
      "Epoch 19/75\n",
      "15295/15295 [==============================] - 11s 689us/step - loss: 0.1986 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.1930 - val_loss: 0.2134 - val_conv2d_13_loss: 0.0056 - val_conv2d_20_loss: 0.2079\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.22012 to 0.21343, saving model to wts4/weights.19.hdf5\n",
      "Epoch 20/75\n",
      "15295/15295 [==============================] - 12s 753us/step - loss: 0.1937 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.1882 - val_loss: 0.2095 - val_conv2d_13_loss: 0.0055 - val_conv2d_20_loss: 0.2040\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.21343 to 0.20953, saving model to wts4/weights.20.hdf5\n",
      "Epoch 21/75\n",
      "15295/15295 [==============================] - 11s 723us/step - loss: 0.1922 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.1867 - val_loss: 0.2150 - val_conv2d_13_loss: 0.0055 - val_conv2d_20_loss: 0.2095\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.20953\n",
      "Epoch 22/75\n",
      "15295/15295 [==============================] - 11s 707us/step - loss: 0.1894 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.1840 - val_loss: 0.2095 - val_conv2d_13_loss: 0.0055 - val_conv2d_20_loss: 0.2040\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.20953 to 0.20948, saving model to wts4/weights.22.hdf5\n",
      "\n",
      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 23/75\n",
      "15295/15295 [==============================] - 11s 733us/step - loss: 0.1594 - conv2d_13_loss: 0.0052 - conv2d_20_loss: 0.1542 - val_loss: 0.1821 - val_conv2d_13_loss: 0.0053 - val_conv2d_20_loss: 0.1768\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.20948 to 0.18209, saving model to wts4/weights.23.hdf5\n",
      "Epoch 24/75\n",
      "15295/15295 [==============================] - 11s 750us/step - loss: 0.1482 - conv2d_13_loss: 0.0051 - conv2d_20_loss: 0.1431 - val_loss: 0.1702 - val_conv2d_13_loss: 0.0053 - val_conv2d_20_loss: 0.1650\n",
      "\n",
      "Epoch 00024: val_loss improved from 0.18209 to 0.17021, saving model to wts4/weights.24.hdf5\n",
      "Epoch 25/75\n",
      "15295/15295 [==============================] - 11s 716us/step - loss: 0.1440 - conv2d_13_loss: 0.0050 - conv2d_20_loss: 0.1390 - val_loss: 0.1695 - val_conv2d_13_loss: 0.0052 - val_conv2d_20_loss: 0.1643\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.17021 to 0.16948, saving model to wts4/weights.25.hdf5\n",
      "Epoch 26/75\n",
      "15295/15295 [==============================] - 12s 758us/step - loss: 0.1411 - conv2d_13_loss: 0.0050 - conv2d_20_loss: 0.1361 - val_loss: 0.1667 - val_conv2d_13_loss: 0.0052 - val_conv2d_20_loss: 0.1615\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.16948 to 0.16669, saving model to wts4/weights.26.hdf5\n",
      "Epoch 27/75\n",
      "15295/15295 [==============================] - 11s 726us/step - loss: 0.1385 - conv2d_13_loss: 0.0049 - conv2d_20_loss: 0.1335 - val_loss: 0.1657 - val_conv2d_13_loss: 0.0051 - val_conv2d_20_loss: 0.1605\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00027: val_loss improved from 0.16669 to 0.16568, saving model to wts4/weights.27.hdf5\n",
      "Epoch 28/75\n",
      "15295/15295 [==============================] - 11s 738us/step - loss: 0.1357 - conv2d_13_loss: 0.0049 - conv2d_20_loss: 0.1308 - val_loss: 0.1648 - val_conv2d_13_loss: 0.0052 - val_conv2d_20_loss: 0.1596\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.16568 to 0.16476, saving model to wts4/weights.28.hdf5\n",
      "Epoch 29/75\n",
      "15295/15295 [==============================] - 12s 753us/step - loss: 0.1336 - conv2d_13_loss: 0.0049 - conv2d_20_loss: 0.1287 - val_loss: 0.1644 - val_conv2d_13_loss: 0.0051 - val_conv2d_20_loss: 0.1592\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.16476 to 0.16435, saving model to wts4/weights.29.hdf5\n",
      "Epoch 30/75\n",
      "15295/15295 [==============================] - 11s 747us/step - loss: 0.1317 - conv2d_13_loss: 0.0048 - conv2d_20_loss: 0.1268 - val_loss: 0.1623 - val_conv2d_13_loss: 0.0051 - val_conv2d_20_loss: 0.1572\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.16435 to 0.16235, saving model to wts4/weights.30.hdf5\n",
      "Epoch 31/75\n",
      "15295/15295 [==============================] - 11s 741us/step - loss: 0.1299 - conv2d_13_loss: 0.0048 - conv2d_20_loss: 0.1251 - val_loss: 0.1597 - val_conv2d_13_loss: 0.0050 - val_conv2d_20_loss: 0.1547\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.16235 to 0.15970, saving model to wts4/weights.31.hdf5\n",
      "Epoch 32/75\n",
      "15295/15295 [==============================] - 11s 702us/step - loss: 0.1280 - conv2d_13_loss: 0.0047 - conv2d_20_loss: 0.1233 - val_loss: 0.1582 - val_conv2d_13_loss: 0.0050 - val_conv2d_20_loss: 0.1531\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.15970 to 0.15819, saving model to wts4/weights.32.hdf5\n",
      "Epoch 33/75\n",
      "15295/15295 [==============================] - 11s 710us/step - loss: 0.1263 - conv2d_13_loss: 0.0047 - conv2d_20_loss: 0.1216 - val_loss: 0.1599 - val_conv2d_13_loss: 0.0050 - val_conv2d_20_loss: 0.1549\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.15819\n",
      "Epoch 34/75\n",
      "15295/15295 [==============================] - 12s 760us/step - loss: 0.1250 - conv2d_13_loss: 0.0047 - conv2d_20_loss: 0.1204 - val_loss: 0.1552 - val_conv2d_13_loss: 0.0050 - val_conv2d_20_loss: 0.1502\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.15819 to 0.15520, saving model to wts4/weights.34.hdf5\n",
      "Epoch 35/75\n",
      "15295/15295 [==============================] - 11s 710us/step - loss: 0.1235 - conv2d_13_loss: 0.0046 - conv2d_20_loss: 0.1188 - val_loss: 0.1556 - val_conv2d_13_loss: 0.0050 - val_conv2d_20_loss: 0.1506\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.15520\n",
      "Epoch 36/75\n",
      "15295/15295 [==============================] - 11s 736us/step - loss: 0.1217 - conv2d_13_loss: 0.0046 - conv2d_20_loss: 0.1171 - val_loss: 0.1558 - val_conv2d_13_loss: 0.0050 - val_conv2d_20_loss: 0.1508\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.15520\n",
      "\n",
      "Epoch 00036: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 37/75\n",
      "15295/15295 [==============================] - 11s 740us/step - loss: 0.1091 - conv2d_13_loss: 0.0044 - conv2d_20_loss: 0.1047 - val_loss: 0.1437 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1388\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.15520 to 0.14366, saving model to wts4/weights.37.hdf5\n",
      "Epoch 38/75\n",
      "15295/15295 [==============================] - 11s 710us/step - loss: 0.1042 - conv2d_13_loss: 0.0043 - conv2d_20_loss: 0.0999 - val_loss: 0.1429 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1381\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.14366 to 0.14290, saving model to wts4/weights.38.hdf5\n",
      "Epoch 39/75\n",
      "15295/15295 [==============================] - 11s 708us/step - loss: 0.1025 - conv2d_13_loss: 0.0043 - conv2d_20_loss: 0.0982 - val_loss: 0.1438 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1390\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.14290\n",
      "Epoch 40/75\n",
      "15295/15295 [==============================] - 11s 732us/step - loss: 0.1011 - conv2d_13_loss: 0.0042 - conv2d_20_loss: 0.0969 - val_loss: 0.1419 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1371\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.14290 to 0.14189, saving model to wts4/weights.40.hdf5\n",
      "Epoch 41/75\n",
      "15295/15295 [==============================] - 11s 712us/step - loss: 0.1003 - conv2d_13_loss: 0.0042 - conv2d_20_loss: 0.0961 - val_loss: 0.1420 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1372\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.14189\n",
      "Epoch 42/75\n",
      "15295/15295 [==============================] - 11s 739us/step - loss: 0.0993 - conv2d_13_loss: 0.0042 - conv2d_20_loss: 0.0951 - val_loss: 0.1419 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1371\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.14189 to 0.14189, saving model to wts4/weights.42.hdf5\n",
      "\n",
      "Epoch 00042: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 43/75\n",
      "15295/15295 [==============================] - 11s 718us/step - loss: 0.0934 - conv2d_13_loss: 0.0041 - conv2d_20_loss: 0.0894 - val_loss: 0.1385 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1337\n",
      "\n",
      "Epoch 00043: val_loss improved from 0.14189 to 0.13846, saving model to wts4/weights.43.hdf5\n",
      "Epoch 44/75\n",
      "15295/15295 [==============================] - 11s 708us/step - loss: 0.0911 - conv2d_13_loss: 0.0040 - conv2d_20_loss: 0.0871 - val_loss: 0.1385 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1338\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.13846\n",
      "Epoch 45/75\n",
      "15295/15295 [==============================] - 11s 711us/step - loss: 0.0901 - conv2d_13_loss: 0.0040 - conv2d_20_loss: 0.0861 - val_loss: 0.1384 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1336\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.13846 to 0.13838, saving model to wts4/weights.45.hdf5\n",
      "\n",
      "Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.0001.\n",
      "Epoch 46/75\n",
      "15295/15295 [==============================] - 11s 716us/step - loss: 0.0886 - conv2d_13_loss: 0.0040 - conv2d_20_loss: 0.0846 - val_loss: 0.1378 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1331\n",
      "\n",
      "Epoch 00046: val_loss improved from 0.13838 to 0.13783, saving model to wts4/weights.46.hdf5\n",
      "Epoch 47/75\n",
      "15295/15295 [==============================] - 11s 723us/step - loss: 0.0878 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0839 - val_loss: 0.1378 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1331\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.13783 to 0.13780, saving model to wts4/weights.47.hdf5\n",
      "Epoch 48/75\n",
      "15295/15295 [==============================] - 11s 734us/step - loss: 0.0873 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0834 - val_loss: 0.1379 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1331\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.13780\n",
      "Epoch 49/75\n",
      "15295/15295 [==============================] - 11s 720us/step - loss: 0.0869 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0829 - val_loss: 0.1376 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1328\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.13780 to 0.13757, saving model to wts4/weights.49.hdf5\n",
      "Epoch 50/75\n",
      "15295/15295 [==============================] - 11s 733us/step - loss: 0.0862 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0823 - val_loss: 0.1378 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1331\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.13757\n",
      "Epoch 51/75\n",
      "15295/15295 [==============================] - 12s 789us/step - loss: 0.0858 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0819 - val_loss: 0.1375 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.13757 to 0.13749, saving model to wts4/weights.51.hdf5\n",
      "Epoch 52/75\n",
      "15295/15295 [==============================] - 11s 712us/step - loss: 0.0854 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0816 - val_loss: 0.1375 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1328\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.13749\n",
      "Epoch 53/75\n",
      "15295/15295 [==============================] - 11s 707us/step - loss: 0.0850 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0812 - val_loss: 0.1375 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00053: val_loss improved from 0.13749 to 0.13748, saving model to wts4/weights.53.hdf5\n",
      "Epoch 54/75\n",
      "15295/15295 [==============================] - 11s 717us/step - loss: 0.0846 - conv2d_13_loss: 0.0039 - conv2d_20_loss: 0.0807 - val_loss: 0.1374 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1326\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00054: val_loss improved from 0.13748 to 0.13736, saving model to wts4/weights.54.hdf5\n",
      "Epoch 55/75\n",
      "15295/15295 [==============================] - 12s 764us/step - loss: 0.0842 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0804 - val_loss: 0.1375 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1328\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.13736\n",
      "Epoch 56/75\n",
      "15295/15295 [==============================] - 12s 765us/step - loss: 0.0839 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0801 - val_loss: 0.1374 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.13736\n",
      "Epoch 57/75\n",
      "15295/15295 [==============================] - 11s 722us/step - loss: 0.0835 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0797 - val_loss: 0.1378 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1331\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.13736\n",
      "Epoch 58/75\n",
      "15295/15295 [==============================] - 11s 719us/step - loss: 0.0832 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0794 - val_loss: 0.1373 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1326\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.13736 to 0.13735, saving model to wts4/weights.58.hdf5\n",
      "Epoch 59/75\n",
      "15295/15295 [==============================] - 12s 801us/step - loss: 0.0830 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0792 - val_loss: 0.1374 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.13735\n",
      "Epoch 60/75\n",
      "15295/15295 [==============================] - 11s 724us/step - loss: 0.0826 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0788 - val_loss: 0.1373 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1326\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.13735 to 0.13731, saving model to wts4/weights.60.hdf5\n",
      "Epoch 61/75\n",
      "15295/15295 [==============================] - 11s 735us/step - loss: 0.0823 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0785 - val_loss: 0.1371 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1324\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.13731 to 0.13711, saving model to wts4/weights.61.hdf5\n",
      "Epoch 62/75\n",
      "15295/15295 [==============================] - 12s 783us/step - loss: 0.0820 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0782 - val_loss: 0.1373 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1326\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.13711\n",
      "Epoch 63/75\n",
      "15295/15295 [==============================] - 11s 719us/step - loss: 0.0818 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0780 - val_loss: 0.1373 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1326\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.13711\n",
      "Epoch 64/75\n",
      "15295/15295 [==============================] - 12s 760us/step - loss: 0.0815 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0778 - val_loss: 0.1375 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.13711\n",
      "Epoch 65/75\n",
      "15295/15295 [==============================] - 11s 744us/step - loss: 0.0813 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0775 - val_loss: 0.1374 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.13711\n",
      "Epoch 66/75\n",
      "15295/15295 [==============================] - 11s 696us/step - loss: 0.0810 - conv2d_13_loss: 0.0038 - conv2d_20_loss: 0.0772 - val_loss: 0.1369 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1322\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.13711 to 0.13695, saving model to wts4/weights.66.hdf5\n",
      "Epoch 67/75\n",
      "15295/15295 [==============================] - 11s 745us/step - loss: 0.0808 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0770 - val_loss: 0.1373 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1326\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.13695\n",
      "Epoch 68/75\n",
      "15295/15295 [==============================] - 12s 762us/step - loss: 0.0806 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0768 - val_loss: 0.1373 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1325\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.13695\n",
      "Epoch 69/75\n",
      "15295/15295 [==============================] - 11s 711us/step - loss: 0.0804 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0767 - val_loss: 0.1377 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1330\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.13695\n",
      "Epoch 70/75\n",
      "15295/15295 [==============================] - 11s 734us/step - loss: 0.0802 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0764 - val_loss: 0.1371 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1323\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.13695\n",
      "Epoch 71/75\n",
      "15295/15295 [==============================] - 11s 751us/step - loss: 0.0799 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0762 - val_loss: 0.1375 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.13695\n",
      "Epoch 72/75\n",
      "15295/15295 [==============================] - 11s 726us/step - loss: 0.0797 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0760 - val_loss: 0.1374 - val_conv2d_13_loss: 0.0048 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.13695\n",
      "Epoch 73/75\n",
      "15295/15295 [==============================] - 11s 723us/step - loss: 0.0796 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0759 - val_loss: 0.1370 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1323\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.13695\n",
      "Epoch 74/75\n",
      "15295/15295 [==============================] - 11s 725us/step - loss: 0.0793 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0756 - val_loss: 0.1376 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1328\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.13695\n",
      "Epoch 75/75\n",
      "15295/15295 [==============================] - 12s 771us/step - loss: 0.0792 - conv2d_13_loss: 0.0037 - conv2d_20_loss: 0.0755 - val_loss: 0.1374 - val_conv2d_13_loss: 0.0047 - val_conv2d_20_loss: 0.1327\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.13695\n"
     ]
    }
   ],
   "source": [
    "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5,\n",
    "                              patience=2, min_lr=0.0001, verbose=1)\n",
    "\n",
    "checkpoints=keras.callbacks.ModelCheckpoint('%s/weights.{epoch:02d}.hdf5' %wt_path, \n",
    "                                            monitor='val_loss', verbose=1, save_best_only=True, \n",
    "                                            save_weights_only=False, mode='auto', period=1)\n",
    "\n",
    "history=parallel_model.fit(X_train, [Y_I_train, Y_phi_train], shuffle=True, batch_size=batch_size, verbose=1, \n",
    "                           epochs=nepochs, validation_split = 0.05, callbacks=[checkpoints, reduce_lr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot metrics of the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:17:42.503980Z",
     "start_time": "2020-06-09T23:17:41.209545Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0609 23:17:41.347186 140165287601984 font_manager.py:1282] findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=history\n",
    "epochs=np.asarray(history.epoch)+1\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "plt.style.use('seaborn-white')\n",
    "matplotlib.rc('font',family='Times New Roman')\n",
    "matplotlib.rcParams['font.size'] = 20\n",
    "\n",
    "f, axarr = plt.subplots(3, sharex=True, figsize=(12, 8))\n",
    "\n",
    "axarr[0].set(ylabel='Loss')\n",
    "axarr[0].plot(epochs,hist.history['loss'], 'C3o', label='Total Training')\n",
    "axarr[0].plot(epochs,hist.history['val_loss'], 'C3-', label='Total Validation')\n",
    "axarr[0].grid()\n",
    "axarr[0].legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "\n",
    "axarr[1].set(ylabel='Loss')\n",
    "axarr[1].plot(epochs,hist.history['conv2d_13_loss'], 'C0o', label='Structure Training')\n",
    "axarr[1].plot(epochs,hist.history['val_conv2d_13_loss'], 'C0-', label='Structure Validation')\n",
    "axarr[1].legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "plt.xlabel('Epochs')\n",
    "plt.tight_layout()\n",
    "axarr[1].grid()\n",
    "\n",
    "\n",
    "axarr[2].set(ylabel='Loss')\n",
    "axarr[2].plot(epochs,hist.history['conv2d_20_loss'], 'C0o', label='Phase Training')\n",
    "axarr[2].plot(epochs,hist.history['val_conv2d_20_loss'], 'C0-', label='Phase Validation')\n",
    "axarr[2].legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "plt.tight_layout()\n",
    "axarr[2].grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:17:42.512917Z",
     "start_time": "2020-06-09T23:17:42.506575Z"
    }
   },
   "outputs": [],
   "source": [
    "np.save('str_history', history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the epoch with the lowest validation loss\n",
    "(PtychoNN will use these weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-09T23:17:42.524368Z",
     "start_time": "2020-06-09T23:17:42.514737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66\n"
     ]
    }
   ],
   "source": [
    "val_losses=hist.history['val_loss']\n",
    "min_epoch=np.argmin(val_losses)+1\n",
    "print (min_epoch)\n",
    "np.save('%s/min_epoch' %(wt_path), min_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}