Deep Network Visualization Diff weights (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional autoencoder\n",
    "\n",
    "Since our inputs are images, it makes sense to use convolutional neural networks (convnets) as encoders and decoders. In practical settings, autoencoders applied to images are always convolutional autoencoders --they simply perform much better.\n",
    "\n",
    "Let's implement one. The encoder will consist in a stack of Conv2D and MaxPooling2D layers (max pooling being used for spatial down-sampling), while the decoder will consist in a stack of Conv2D and UpSampling2D layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "\n",
    "input_img = Input(shape=(32, 32, 3))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x2 = MaxPooling2D((2, 2), padding='same')(x1)\n",
    "x3 = Conv2D(8, (6, 6), activation='relu', padding='same')(x2)\n",
    "x4 = MaxPooling2D((2, 2), padding='same')(x3)\n",
    "x5 = Conv2D(8, (9, 9), activation='relu', padding='same')(x4)\n",
    "encoded = MaxPooling2D((2, 2), padding='same')(x5)\n",
    "\n",
    "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
    "\n",
    "x6 = Conv2D(8, (9, 9), activation='relu', padding='same')(encoded)\n",
    "x7 = UpSampling2D((2, 2))(x6)\n",
    "x8 = Conv2D(8, (6, 6), activation='relu', padding='same')(x7)\n",
    "x9 = UpSampling2D((2, 2))(x8)\n",
    "x10 = Conv2D(16, (3, 3), activation='relu', padding='same')(x9)\n",
    "x11 = UpSampling2D((2, 2))(x10)\n",
    "decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x11)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='adagrad', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "import numpy as np\n",
    "\n",
    "(x_train, _), (x_test, _) = cifar10.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = np.reshape(x_train, (len(x_train), 32, 32, 3))  # adapt this if using `channels_first` image data format\n",
    "x_test = np.reshape(x_test, (len(x_test), 32, 32, 3))  # adapt this if using `channels_first` image data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.6373 - val_loss: 0.6144\n",
      "Epoch 2/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.6095 - val_loss: 0.6055\n",
      "Epoch 3/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.6035 - val_loss: 0.6015\n",
      "Epoch 4/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5995 - val_loss: 0.5982\n",
      "Epoch 5/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5964 - val_loss: 0.5952\n",
      "Epoch 6/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5939 - val_loss: 0.5937\n",
      "Epoch 7/50\n",
      "50000/50000 [==============================] - 147s - loss: 0.5918 - val_loss: 0.5911\n",
      "Epoch 8/50\n",
      "50000/50000 [==============================] - 147s - loss: 0.5904 - val_loss: 0.5906\n",
      "Epoch 9/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5894 - val_loss: 0.5897\n",
      "Epoch 10/50\n",
      "50000/50000 [==============================] - 147s - loss: 0.5887 - val_loss: 0.5891\n",
      "Epoch 11/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5879 - val_loss: 0.5890\n",
      "Epoch 12/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5873 - val_loss: 0.5877\n",
      "Epoch 13/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5867 - val_loss: 0.5870\n",
      "Epoch 14/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5862 - val_loss: 0.5868\n",
      "Epoch 15/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5857 - val_loss: 0.5858\n",
      "Epoch 16/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5852 - val_loss: 0.5861\n",
      "Epoch 17/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5849 - val_loss: 0.5859\n",
      "Epoch 18/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5845 - val_loss: 0.5848\n",
      "Epoch 19/50\n",
      "50000/50000 [==============================] - 150s - loss: 0.5842 - val_loss: 0.5846\n",
      "Epoch 20/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5838 - val_loss: 0.5857\n",
      "Epoch 21/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5836 - val_loss: 0.5838\n",
      "Epoch 22/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5832 - val_loss: 0.5845\n",
      "Epoch 23/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5830 - val_loss: 0.5834\n",
      "Epoch 24/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5827 - val_loss: 0.5833\n",
      "Epoch 25/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5824 - val_loss: 0.5830\n",
      "Epoch 26/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5822 - val_loss: 0.5832\n",
      "Epoch 27/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5819 - val_loss: 0.5824\n",
      "Epoch 28/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5818 - val_loss: 0.5821\n",
      "Epoch 29/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5816 - val_loss: 0.5834\n",
      "Epoch 30/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5814 - val_loss: 0.5818\n",
      "Epoch 31/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5813 - val_loss: 0.5820\n",
      "Epoch 32/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5811 - val_loss: 0.5821\n",
      "Epoch 33/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5809 - val_loss: 0.5813\n",
      "Epoch 34/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5808 - val_loss: 0.5821\n",
      "Epoch 35/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5806 - val_loss: 0.5814\n",
      "Epoch 36/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5805 - val_loss: 0.5811\n",
      "Epoch 37/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5804 - val_loss: 0.5809\n",
      "Epoch 38/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5802 - val_loss: 0.5807\n",
      "Epoch 39/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5802 - val_loss: 0.5810\n",
      "Epoch 40/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5800 - val_loss: 0.5811\n",
      "Epoch 41/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5799 - val_loss: 0.5806\n",
      "Epoch 42/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5799 - val_loss: 0.5810\n",
      "Epoch 43/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5797 - val_loss: 0.5805\n",
      "Epoch 44/50\n",
      "50000/50000 [==============================] - 148s - loss: 0.5796 - val_loss: 0.5804\n",
      "Epoch 45/50\n",
      "50000/50000 [==============================] - 149s - loss: 0.5796 - val_loss: 0.5807\n",
      "Epoch 46/50\n",
      "50000/50000 [==============================] - 150s - loss: 0.5795 - val_loss: 0.5801\n",
      "Epoch 47/50\n",
      "50000/50000 [==============================] - 173s - loss: 0.5794 - val_loss: 0.5800\n",
      "Epoch 48/50\n",
      "50000/50000 [==============================] - 180s - loss: 0.5793 - val_loss: 0.5804\n",
      "Epoch 49/50\n",
      "50000/50000 [==============================] - 181s - loss: 0.5793 - val_loss: 0.5806\n",
      "Epoch 50/50\n",
      "50000/50000 [==============================] - 180s - loss: 0.5792 - val_loss: 0.5800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x121c5b70>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=128,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "#autoencoder.save('cifar10_autoencoders.h5')  # creates a HDF5 file 'my_model.h5'\n",
    "#del model  # deletes the existing model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "autoencoder = load_model('cifar10_autoencoders.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5ycUDkG/4jIRnIWgVQ9R3hNiFnCqGvHkE9x9K18d0BRpxTTdfHOU9uitR/m8g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\nE0giCnDYCEEn3z8k91P0tDjWvUBxZxIj+mCnLWqltwU6P+i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Ark4nlgLU+XRt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\nd8QeNdAdkL8p4D6Deae4N9Bn6O9ER1RI1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631/drsuTRtYP5TazxvW7wXYs87nXScuz1Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121817b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "decoded_imgs = autoencoder.predict(x_test)\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(32, 32, 3))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + n + 1)\n",
    "    plt.imshow(decoded_imgs[i].reshape(32, 32, 3))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the weights from the first layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEsAAABLCAYAAAA4TnrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAAU1JREFUeJzt2jFqhUAUQNFnCOICTClZg+AG3Lid\nhTCbCJapBbGZNLHWm/nhE3JP/XgOF23UKuccuufl2Qf4S4wFGAswFmAswFiAsQBjAcYCXslwXde5\naZrii9Z1XbzjdBxH8Y593+M4jupqDsVqmiaGYfj5qb51XVe847Sua/GOlNKtOR9DwFiAsQBjAcYC\njAUYCzAWYCzAWICxAGMBxgKMBRgLqMjn+6qqHvKtfxzHR6yJiIhpmop3DMMQKaXLl3/eWYCxAGMB\nxgKMBRgLMBZgLMBYgLEAYwHGAowFGAswFmAswFiAsQD0m2Tf9zHPc/FF27Yt3nFalqV4x7Ztt+a8\nswBjAcYCjAUYCzAWYCzAWICxAGMBxgKMBRgLMBZgLMBYgLEA+gPuZ0R8/N5xnuY95/x2NYRi/Xc+\nhoCxAGMBxgKMBRgLMBZgLMBYwBck7jD9nUjWWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12712fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7a25ba8>"
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     "output_type": "display_data"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEsAAABLCAYAAAA4TnrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAAVlJREFUeJzt2zFqhEAYQOExBGwsTZ2x2qN4Di+x\n4A1stvdQXiKFhZtGrIVJE+v1ZQhLyPvqn3+GhzayW6SUgs55efYF/hJjAcYCjAUYCzAWYCzAWICx\ngFcyXNd1ijFmH7osS/aOQ1mW2Tvu93vYtq14NIdixRjDNE0/v9W32+2WvePQNE32juv1emrO1xAw\nFmAswFiAsQBjAcYCjAUYCzAWYCzAWICxAGMBxgLQ96x5nkPf99mHDsOQvePQdV32jnVdT835ZAHG\nAowFGAswFmAswFiAsQBjAcYCjAUYCzAWYCzAWICxAGMBxgIK8q+wy+WSxnHMPrSqquwdh7Zts3ds\n2xb2fX/4m1KfLMBYgLEAYwHGAowFGAswFmAswFiAsQBjAcYCjAUYCzAWYCwAfSktiuIzhPDxe9d5\nmveU0tujIRTrv/M1BIwFGAswFmAswFiAsQBjAcYCvgCHbzViOJ3kswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x781e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEsAAABLCAYAAAA4TnrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAAUdJREFUeJzt3DFuhDAUAFE7AoREnYIqHIKCu8BV\nOAsFBzOpwx2cinoZeVerKPPqr29rBA1abcw5B93z8e4L/CXGAowFGAswFmAswFiAsQBjARUarqpc\n13XxoV3XFe+4DMNQvOM4jnCeZ3w0h2LVdf2Uy03TVLzjsm1b8Y5xHG/N+RoCxgKMBRgLMBZgLMBY\ngLEAYwHGAowFGAswFmAswFgA+p7V931Y17X40LZti3dclmUp3pFSujXnkwUYCzAWYCzAWICxAGMB\nxgKMBRgLMBZgLMBYgLEAYwHGAowFGAtAn5VTSmGe5+JD930v3nF5xs82m6a5NeeTBRgLMBZgLMBY\ngLEAYwHGAowFGAswFmAswFiAsQBjAcYCjAVE8v9ZMcafEML3667zNl85589HQyjWf+drCBgLMBZg\nLMBYgLEAYwHGAowF/AL0LSS3cS4/nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7afcac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEsAAABLCAYAAAA4TnrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ae1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEsAAABLCAYAAAA4TnrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAAUNJREFUeJzt2zGKhEAQQNHqxRMIE29qbuQBjA09\niBfyQF7CWEMjk95o4vHTDsOw/8VFKR8FFUw559A1P58+gW9iLMBYgLEAYwHGAowFGAswFlCR4ZTS\nLY/7bdvesSYiIu54A1nXNfZ9T6/mUKy7LMty267zPIt3dF13ac7bEDAWYCzAWICxAGMBxgKMBRgL\nMBZgLMBYgLEAYwHGAtD3rKZpYp7n4oP2fV+842kYhuId27ZdmvPKAowFGAswFmAswFiAsQBjAcYC\njAUYCzAWYCzAWICxAGMBxgKMBaDPylVVRV3XxQcdx7F4x9M0TcU7juO4NOeVBRgLMBZgLMBYgLEA\nYwHGAowFGAswFmAswFiAsQBjAcYCjAUk8vd6SmmLiPV9p/Mxvznnx6shFOu/8zYEjAUYCzAWYCzA\nWICxAGMBxgL+AAz8KuGEaJYtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7b889e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEsAAABLCAYAAAA4TnrqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121d0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n = 8\n",
    "\n",
    "for i in range(n):\n",
    "    fig = plt.figure(figsize=(1,1))\n",
    "    conv_1 = np.asarray(autoencoder.layers[1].get_weights())[0][:,:,0,i]\n",
    "    ax = fig.add_subplot(111)\n",
    "    plt.imshow(conv_1.transpose(), cmap = 'gray')\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[[  8.15488771e-03,   6.07780479e-02,   3.52038592e-02, ...,\n",
       "            -2.57759839e-02,   2.50405464e-02,   1.37642130e-01],\n",
       "          [ -3.99612933e-02,  -4.31965627e-02,   1.55702168e-02, ...,\n",
       "            -8.22575167e-02,  -4.86114547e-02,  -1.07376175e-02],\n",
       "          [  2.30774172e-02,  -7.45867491e-02,   1.08525492e-01, ...,\n",
       "             1.43861622e-01,  -6.99539408e-02,  -7.41299391e-02],\n",
       "          ..., \n",
       "          [  7.24775046e-02,   8.09480175e-02,   3.54284123e-02, ...,\n",
       "             5.34150703e-03,  -2.38414649e-02,   1.27396472e-02],\n",
       "          [ -2.81182081e-02,  -6.24120310e-02,  -6.73905341e-03, ...,\n",
       "            -4.32974026e-02,   2.54292060e-02,   1.36202216e-01],\n",
       "          [ -2.24752557e-02,   5.53492494e-02,   5.22947162e-02, ...,\n",
       "            -1.27160829e-02,   6.90510496e-02,  -5.76434610e-03]],\n",
       " \n",
       "         [[  2.35560397e-03,  -2.85049598e-03,  -4.39689942e-02, ...,\n",
       "             3.96229811e-02,  -5.18459417e-02,  -1.98029634e-02],\n",
       "          [  4.41494137e-02,   2.84471195e-02,  -3.18532400e-02, ...,\n",
       "            -7.00206608e-02,  -8.59402269e-02,  -4.19642627e-02],\n",
       "          [ -2.08359919e-02,   1.25960514e-01,   1.35708213e-01, ...,\n",
       "             1.28474548e-01,   4.85068560e-02,  -1.24842830e-01],\n",
       "          ..., \n",
       "          [  1.10193323e-02,   9.11960155e-02,  -2.42395755e-02, ...,\n",
       "             7.30983764e-02,  -4.56133112e-02,  -8.70422870e-02],\n",
       "          [ -4.40177619e-02,   4.56810519e-02,  -8.63666181e-03, ...,\n",
       "             8.98648333e-03,  -1.56854078e-01,  -6.16108961e-02],\n",
       "          [  9.71770845e-03,   9.55173373e-03,   1.03723682e-01, ...,\n",
       "             1.99029520e-02,   9.76625085e-02,   2.23657861e-02]],\n",
       " \n",
       "         [[  2.43661441e-02,  -3.17463130e-02,   1.04382243e-02, ...,\n",
       "             1.61210019e-02,  -8.25976059e-02,   9.17967409e-02],\n",
       "          [ -9.65607241e-02,   1.78900640e-03,  -5.01459800e-02, ...,\n",
       "             2.50101257e-02,  -1.12152867e-01,   2.57570613e-02],\n",
       "          [  3.13362777e-02,   6.66133091e-02,   9.68927443e-02, ...,\n",
       "             1.86010331e-01,   3.86416614e-02,  -4.07423601e-02],\n",
       "          ..., \n",
       "          [ -5.56126906e-05,   6.33124560e-02,  -2.01575700e-02, ...,\n",
       "             1.87463332e-02,  -7.48115554e-02,  -7.21468683e-03],\n",
       "          [ -4.41328995e-02,  -5.79332607e-03,   5.15018292e-02, ...,\n",
       "            -2.82765925e-02,  -1.21696703e-01,  -4.30125520e-02],\n",
       "          [  3.73466723e-02,  -6.54583797e-02,   1.15404688e-02, ...,\n",
       "             9.43128541e-02,   8.45833570e-02,   6.29317537e-02]],\n",
       " \n",
       "         [[ -3.77502739e-02,  -7.01722130e-02,  -5.84836714e-02, ...,\n",
       "             2.73709036e-02,  -5.31258248e-02,   5.31927086e-02],\n",
       "          [ -5.59373647e-02,  -5.11206780e-03,   3.37519087e-02, ...,\n",
       "            -2.45643798e-02,   6.01391271e-02,   1.43368626e-02],\n",
       "          [  5.79551831e-02,   5.45302816e-02,   9.11809206e-02, ...,\n",
       "             1.81425631e-01,   1.26154833e-02,   2.66123679e-03],\n",
       "          ..., \n",
       "          [ -2.12858319e-02,   6.40505925e-02,   1.04396589e-01, ...,\n",
       "             1.43214524e-01,   5.23820408e-02,   6.15856536e-02],\n",
       "          [  5.47284707e-02,  -2.38099732e-02,  -2.01224033e-02, ...,\n",
       "            -8.12831894e-02,  -6.62480965e-02,  -1.84862435e-01],\n",
       "          [ -2.03584004e-02,  -3.72815616e-02,  -3.12591001e-04, ...,\n",
       "            -2.36115288e-02,   7.04920441e-02,  -4.06279340e-02]],\n",
       " \n",
       "         [[ -2.15785624e-03,   3.36857922e-02,   4.65018675e-02, ...,\n",
       "            -2.47731097e-02,   2.13127863e-02,  -6.59838365e-03],\n",
       "          [ -1.13975313e-02,   1.50718319e-04,  -5.76116033e-02, ...,\n",
       "            -5.47358319e-02,  -7.89858922e-02,   7.31434673e-02],\n",
       "          [  5.95194586e-02,  -2.77179293e-02,   1.09195877e-02, ...,\n",
       "             3.25132087e-02,  -2.31842082e-02,  -1.75454274e-01],\n",
       "          ..., \n",
       "          [  8.33434686e-02,  -2.39252076e-02,   6.62853718e-02, ...,\n",
       "             1.31794605e-02,  -5.14929593e-02,  -1.34523064e-01],\n",
       "          [  6.70597656e-03,  -2.96540763e-02,  -7.46163726e-02, ...,\n",
       "            -1.16696861e-02,  -1.03112403e-02,   4.25161123e-02],\n",
       "          [ -1.04142077e-01,  -1.85377393e-02,   7.08616003e-02, ...,\n",
       "             8.46524090e-02,   4.51045819e-02,  -1.12381592e-01]],\n",
       " \n",
       "         [[  1.88587271e-02,  -4.94744480e-02,  -5.29769226e-04, ...,\n",
       "            -3.66450511e-02,  -1.11116230e-01,   9.06324759e-03],\n",
       "          [ -2.87081990e-02,   6.12530410e-02,  -6.09595291e-02, ...,\n",
       "            -9.54228733e-03,  -6.96167815e-04,   5.51921874e-02],\n",
       "          [  6.47173524e-02,   9.35782120e-02,   1.57151241e-02, ...,\n",
       "             3.40428464e-02,  -1.31126851e-01,  -9.10089463e-02],\n",
       "          ..., \n",
       "          [ -3.91775332e-02,  -8.89271125e-02,   3.58739495e-02, ...,\n",
       "             6.52120542e-03,  -1.53603479e-02,  -1.10902034e-01],\n",
       "          [ -3.58873489e-03,   3.64888385e-02,   5.49892634e-02, ...,\n",
       "             6.08769618e-03,   4.23621573e-02,   1.52414158e-01],\n",
       "          [ -6.45089969e-02,   4.93879914e-02,  -7.26864533e-03, ...,\n",
       "            -4.10980079e-03,   2.91536804e-02,  -7.60001317e-02]]],\n",
       " \n",
       " \n",
       "        [[[  1.00557342e-01,  -2.71129664e-02,  -5.28943911e-02, ...,\n",
       "            -1.40729388e-02,   1.25879077e-02,   6.41918331e-02],\n",
       "          [ -8.03123415e-02,  -2.09099483e-02,  -7.26153553e-02, ...,\n",
       "            -1.18201144e-01,  -4.32270765e-02,  -8.17033052e-02],\n",
       "          [ -5.04983962e-02,  -1.37400091e-01,  -6.03797697e-02, ...,\n",
       "             9.21723247e-02,   5.36192581e-02,   1.15747517e-03],\n",
       "          ..., \n",
       "          [  5.67950495e-02,  -1.94253977e-02,  -2.52444856e-02, ...,\n",
       "             1.18387900e-02,  -5.71467634e-03,  -3.42605775e-03],\n",
       "          [ -3.25414836e-02,   7.12487102e-02,   4.15924489e-02, ...,\n",
       "             1.54236713e-02,  -2.84408834e-02,   3.32619585e-02],\n",
       "          [ -7.52423555e-02,  -3.19194458e-02,   5.57624437e-02, ...,\n",
       "             1.13002189e-01,   1.59068033e-01,  -3.27825081e-03]],\n",
       " \n",
       "         [[ -1.98856108e-02,  -4.74649370e-02,  -4.60873246e-02, ...,\n",
       "             8.42018984e-03,   4.72627720e-03,   6.90516531e-02],\n",
       "          [ -1.77549701e-02,  -1.60417929e-01,  -1.34817332e-01, ...,\n",
       "            -2.22158618e-02,  -1.23625092e-01,  -5.52937668e-03],\n",
       "          [ -2.14281660e-02,   5.20871617e-02,   3.18478122e-02, ...,\n",
       "             2.36526310e-01,   1.04377568e-01,  -6.98784813e-02],\n",
       "          ..., \n",
       "          [  1.06353596e-01,   1.73708603e-01,  -3.85744944e-02, ...,\n",
       "             1.22929543e-01,   1.86013766e-02,  -1.78970248e-02],\n",
       "          [ -1.39227184e-02,   1.66608673e-02,  -3.32555287e-02, ...,\n",
       "            -4.10671048e-02,  -1.32577315e-01,   1.83035638e-02],\n",
       "          [ -9.45393369e-02,  -9.74400491e-02,   3.55494469e-02, ...,\n",
       "             4.75995578e-02,   9.21674743e-02,  -4.79348451e-02]],\n",
       " \n",
       "         [[  2.57226564e-02,  -6.67381957e-02,  -2.74203252e-02, ...,\n",
       "            -7.48067051e-02,   5.16217090e-02,   1.09181486e-01],\n",
       "          [  9.69948806e-03,  -9.13356990e-02,  -2.19279397e-02, ...,\n",
       "            -3.56516726e-02,  -1.16810121e-01,  -5.72131090e-02],\n",
       "          [  2.33353134e-02,  -5.50339855e-02,  -1.71788391e-02, ...,\n",
       "             1.09318726e-01,   5.18999696e-02,  -9.34572741e-02],\n",
       "          ..., \n",
       "          [  1.09444655e-01,   1.58705432e-02,   3.15426327e-02, ...,\n",
       "             9.18552354e-02,  -2.35716859e-03,   3.48371305e-02],\n",
       "          [ -3.95224290e-03,  -4.18616198e-02,  -1.62127092e-02, ...,\n",
       "            -3.32983918e-02,  -7.91325346e-02,   6.65472075e-02],\n",
       "          [ -1.09098265e-02,  -9.12308246e-02,   1.03960820e-01, ...,\n",
       "             9.72316861e-02,   1.75331861e-01,   8.19618702e-02]],\n",
       " \n",
       "         [[ -7.81249404e-02,   2.07060315e-02,   2.19846773e-03, ...,\n",
       "            -1.04751572e-01,   7.38447951e-03,  -2.43146904e-02],\n",
       "          [  6.89462870e-02,  -1.04056284e-01,  -9.25099477e-02, ...,\n",
       "            -2.23668348e-02,  -7.35798255e-02,   6.56073987e-02],\n",
       "          [ -1.21484742e-01,   1.60056222e-02,   4.35819402e-02, ...,\n",
       "             1.16918705e-01,   9.09676254e-02,  -1.32970616e-01],\n",
       "          ..., \n",
       "          [  1.27910860e-02,   1.82573110e-01,   1.52800595e-02, ...,\n",
       "             9.70081240e-02,   2.58851610e-03,   3.74579430e-02],\n",
       "          [  6.74975887e-02,   6.07646890e-02,  -2.23683622e-02, ...,\n",
       "            -4.13589589e-02,  -3.58287944e-03,  -1.21026270e-01],\n",
       "          [ -8.42484683e-02,  -8.37746635e-02,   2.44115479e-02, ...,\n",
       "             3.83624695e-02,   2.38783211e-02,   9.63523611e-03]],\n",
       " \n",
       "         [[ -4.34817262e-02,  -1.34269651e-02,  -6.49513751e-02, ...,\n",
       "            -1.94570869e-02,  -4.63641109e-03,  -3.69829088e-02],\n",
       "          [  7.38101080e-02,  -1.35274306e-01,  -3.66121307e-02, ...,\n",
       "            -1.00958273e-01,  -1.95575561e-02,   2.09086519e-02],\n",
       "          [  9.48411226e-03,   1.10780254e-01,  -9.21369810e-03, ...,\n",
       "             1.54104710e-01,  -2.96171140e-02,  -9.03490260e-02],\n",
       "          ..., \n",
       "          [  4.55689989e-02,   6.15534410e-02,  -3.72874327e-02, ...,\n",
       "             1.05997346e-01,   4.17429842e-02,  -1.30916998e-01],\n",
       "          [  7.37766996e-02,   2.07799207e-02,   4.91999201e-02, ...,\n",
       "            -2.08070781e-02,   4.45860578e-03,  -1.87225584e-02],\n",
       "          [ -5.58097064e-02,   2.91696806e-02,   9.32755768e-02, ...,\n",
       "             1.07358083e-01,   6.41658232e-02,  -1.21161580e-01]],\n",
       " \n",
       "         [[ -6.88453903e-03,   2.43811477e-02,   2.88314968e-02, ...,\n",
       "            -1.05299447e-02,   4.79967222e-02,   3.29353958e-02],\n",
       "          [  2.55747046e-02,   7.63856620e-02,   2.90590664e-03, ...,\n",
       "             4.23175693e-02,  -1.44153098e-02,   2.53778528e-02],\n",
       "          [  3.60132894e-04,  -7.72211403e-02,   1.23625563e-03, ...,\n",
       "             1.43027641e-02,   2.16762582e-03,  -1.78529307e-01],\n",
       "          ..., \n",
       "          [  8.71259496e-02,   1.03167295e-02,   2.72142068e-02, ...,\n",
       "             7.68349618e-02,   4.68206443e-02,   2.09151977e-03],\n",
       "          [ -2.62128226e-02,   2.25753952e-02,   2.43017375e-02, ...,\n",
       "             3.25826518e-02,   3.74532342e-02,   3.86447944e-02],\n",
       "          [  3.90551165e-02,  -3.52230631e-02,  -7.95772765e-03, ...,\n",
       "             9.63470489e-02,   1.10747647e-02,  -3.65248583e-02]]],\n",
       " \n",
       " \n",
       "        [[[  7.38220811e-02,  -1.04348641e-02,  -3.70120769e-03, ...,\n",
       "            -4.02443334e-02,  -5.93658648e-02,   5.22378422e-02],\n",
       "          [ -4.28777980e-03,  -7.73533732e-02,  -7.25436881e-02, ...,\n",
       "             3.78630590e-03,  -3.48392241e-02,  -7.05557987e-02],\n",
       "          [ -4.03252095e-02,   4.49285656e-02,   2.79249959e-02, ...,\n",
       "             7.65607134e-02,   1.55531364e-02,   1.12538159e-01],\n",
       "          ..., \n",
       "          [  7.73540214e-02,   1.46121919e-01,   3.80067602e-02, ...,\n",
       "            -3.39148194e-02,   1.37618423e-01,  -3.10116373e-02],\n",
       "          [ -2.97735841e-03,   4.22500409e-02,  -4.39314172e-02, ...,\n",
       "             4.22640778e-02,   3.47272567e-02,  -1.44610053e-03],\n",
       "          [  5.25474660e-02,  -7.80408606e-02,  -2.61579566e-02, ...,\n",
       "             4.69771624e-02,  -1.92886754e-03,   1.66885573e-02]],\n",
       " \n",
       "         [[ -3.81727070e-02,   1.12471148e-01,  -1.84427463e-02, ...,\n",
       "            -2.98753027e-02,   2.22496837e-02,   2.40008309e-02],\n",
       "          [ -3.57828811e-02,  -1.44622684e-01,  -5.98841859e-03, ...,\n",
       "            -5.13469949e-02,  -6.48094565e-02,  -1.20593339e-01],\n",
       "          [  2.51975339e-02,   3.77182424e-01,   4.37984727e-02, ...,\n",
       "             6.12337291e-02,   1.27647847e-01,   1.04484670e-01],\n",
       "          ..., \n",
       "          [  7.57888379e-03,   2.50402600e-01,   6.03508279e-02, ...,\n",
       "            -1.42036220e-02,   8.91295448e-02,  -1.91243906e-02],\n",
       "          [  1.25446236e-02,   2.64064334e-02,   1.71057805e-02, ...,\n",
       "            -2.21416112e-02,  -1.10313296e-01,  -1.79377478e-02],\n",
       "          [ -2.74797976e-02,  -9.89770330e-03,  -1.25115123e-02, ...,\n",
       "             1.09301753e-01,   6.08573407e-02,  -2.75517423e-02]],\n",
       " \n",
       "         [[  6.25979006e-02,  -8.95343125e-02,  -8.35877657e-02, ...,\n",
       "            -1.33486345e-01,  -8.92534014e-03,   7.36310557e-02],\n",
       "          [ -6.21470995e-02,  -4.07660902e-02,   8.57180431e-02, ...,\n",
       "            -5.82653694e-02,  -5.85885048e-02,  -1.28959924e-01],\n",
       "          [  4.95768003e-02,   1.32710606e-01,  -8.78357887e-02, ...,\n",
       "             5.99528365e-02,   7.66934827e-02,   6.91758329e-03],\n",
       "          ..., \n",
       "          [ -6.09158501e-02,   4.90465350e-02,  -4.61659506e-02, ...,\n",
       "            -4.53452617e-02,   4.81924117e-02,   5.58741279e-02],\n",
       "          [  1.96587723e-02,  -7.12864846e-02,  -9.53217596e-02, ...,\n",
       "             9.61192790e-03,  -1.05619922e-01,   5.71334101e-02],\n",
       "          [ -9.62554961e-02,  -6.62179291e-02,   2.68824622e-02, ...,\n",
       "             7.70729408e-02,   5.76471239e-02,   1.39062077e-01]],\n",
       " \n",
       "         [[ -9.52606797e-02,  -3.34034525e-02,  -1.57031976e-02, ...,\n",
       "            -6.95927010e-04,   5.02294861e-02,  -1.01794355e-01],\n",
       "          [  2.01416723e-02,  -6.36048689e-02,  -1.62611157e-02, ...,\n",
       "             2.68885191e-03,  -4.79802527e-02,  -9.70436260e-02],\n",
       "          [  2.12394744e-02,   1.44262120e-01,   2.92877164e-02, ...,\n",
       "             6.58633709e-02,   2.03250293e-02,   1.15779236e-01],\n",
       "          ..., \n",
       "          [  1.02278262e-01,   1.92665562e-01,   1.20504173e-02, ...,\n",
       "             9.34756547e-02,   1.32473543e-01,   7.49316290e-02],\n",
       "          [  5.88040128e-02,  -6.64554089e-02,   6.62606210e-02, ...,\n",
       "             7.23148957e-02,  -1.05008893e-01,   5.71983233e-02],\n",
       "          [ -6.53296039e-02,   1.07204542e-01,   6.29804283e-02, ...,\n",
       "             3.67047749e-02,   8.39167088e-02,  -8.47633369e-03]],\n",
       " \n",
       "         [[  2.56206766e-02,   6.12446433e-03,  -2.97362749e-02, ...,\n",
       "            -2.43622661e-02,   4.18509319e-02,   4.00221162e-02],\n",
       "          [ -4.09736671e-03,  -1.63696539e-02,  -4.71577421e-02, ...,\n",
       "             2.04951260e-02,  -1.73173547e-02,  -1.89826805e-02],\n",
       "          [  9.56271738e-02,   1.68021038e-01,   4.89185974e-02, ...,\n",
       "             1.47411585e-01,   3.51788700e-02,   8.04694518e-02],\n",
       "          ..., \n",
       "          [ -8.92448705e-03,   9.18883309e-02,  -7.77343959e-02, ...,\n",
       "             4.27523591e-02,   3.03159617e-02,  -2.56711822e-02],\n",
       "          [ -6.50334917e-03,  -6.96459562e-02,   2.82282885e-02, ...,\n",
       "             2.66418327e-02,  -1.02086384e-02,   6.15069903e-02],\n",
       "          [ -1.33430406e-01,  -4.01847214e-02,   1.99399125e-02, ...,\n",
       "             6.72378317e-02,   8.56390372e-02,  -2.87672803e-02]],\n",
       " \n",
       "         [[ -4.29908708e-02,   9.05078277e-02,  -4.94915508e-02, ...,\n",
       "            -6.90933019e-02,   2.50092838e-02,  -1.63542591e-02],\n",
       "          [ -3.32709961e-02,  -5.61802536e-02,   2.08179746e-02, ...,\n",
       "             5.10710403e-02,   1.95197985e-02,   4.88534160e-02],\n",
       "          [  5.11306487e-02,   1.52355075e-01,  -1.85144786e-02, ...,\n",
       "            -5.10574225e-03,   6.62518814e-02,   8.51666778e-02],\n",
       "          ..., \n",
       "          [ -3.68064269e-02,   3.07428185e-02,   6.00146875e-02, ...,\n",
       "            -3.25805955e-02,  -3.30161564e-02,   1.06424421e-01],\n",
       "          [  7.94030428e-02,  -1.25503801e-02,   2.55726799e-02, ...,\n",
       "            -4.11240384e-02,   1.95444450e-02,   2.54104808e-02],\n",
       "          [ -1.35958895e-01,   9.94959474e-02,   6.05127029e-02, ...,\n",
       "             4.12885025e-02,   7.22690076e-02,   6.20775558e-02]]],\n",
       " \n",
       " \n",
       "        [[[  4.87984344e-02,   3.82635603e-03,  -3.02646067e-02, ...,\n",
       "            -3.01345531e-02,  -7.51951709e-02,  -1.72073096e-02],\n",
       "          [ -1.29382946e-02,  -1.33416340e-01,   3.85282002e-02, ...,\n",
       "            -2.41633691e-02,  -1.88254640e-02,  -2.45915391e-02],\n",
       "          [  6.77572116e-02,   4.28191684e-02,   7.84342363e-03, ...,\n",
       "             2.74902489e-02,   5.33572137e-02,  -2.07346864e-02],\n",
       "          ..., \n",
       "          [ -9.65531822e-03,  -5.30826300e-02,   2.07172744e-02, ...,\n",
       "             7.22508803e-02,  -3.09450906e-02,   1.09063037e-01],\n",
       "          [ -5.26475012e-02,  -2.03607995e-02,   1.08744958e-02, ...,\n",
       "            -2.22909823e-02,  -2.86886543e-02,   1.20479941e-01],\n",
       "          [ -8.89099464e-02,   4.07430017e-03,   1.03902720e-01, ...,\n",
       "            -9.85616893e-02,  -4.61084619e-02,   2.94793863e-02]],\n",
       " \n",
       "         [[ -5.66372387e-02,   3.38524394e-03,  -4.00966033e-02, ...,\n",
       "            -9.81810912e-02,  -4.79210429e-02,  -4.41820771e-02],\n",
       "          [ -1.73185077e-02,  -7.17924908e-02,   1.88963730e-02, ...,\n",
       "            -4.85109463e-02,  -7.72249177e-02,  -8.07375684e-02],\n",
       "          [  4.17446019e-03,   1.06469490e-01,   5.92258424e-02, ...,\n",
       "             4.27900208e-03,  -4.92631197e-02,   6.43537641e-02],\n",
       "          ..., \n",
       "          [  1.84694268e-02,   8.09295997e-02,  -3.62539142e-02, ...,\n",
       "             7.97649622e-02,   7.36091658e-02,   3.25016007e-02],\n",
       "          [  1.04266955e-02,   7.74856284e-02,   4.42023799e-02, ...,\n",
       "             6.08078316e-02,  -9.15103182e-02,  -3.09844818e-02],\n",
       "          [ -1.79493707e-02,   7.35788941e-02,   1.18655764e-01, ...,\n",
       "             8.88407454e-02,  -1.83795393e-02,   1.23137675e-01]],\n",
       " \n",
       "         [[  5.79181015e-02,  -1.90426931e-02,  -8.26691538e-02, ...,\n",
       "             2.53518075e-02,  -5.01923375e-02,   7.10078776e-02],\n",
       "          [ -7.84346461e-02,  -4.90113236e-02,   6.77821562e-02, ...,\n",
       "            -7.79582411e-02,  -1.12035476e-01,  -8.28219727e-02],\n",
       "          [  1.07843742e-01,   1.28193662e-01,   8.43294561e-02, ...,\n",
       "             1.06410272e-01,   2.80122161e-02,  -2.69562546e-02],\n",
       "          ..., \n",
       "          [  1.01791978e-01,   9.99219790e-02,  -3.76271904e-02, ...,\n",
       "             8.64723772e-02,   5.52418679e-02,   2.28500590e-02],\n",
       "          [  8.10204148e-02,  -1.54143095e-01,  -9.96950641e-02, ...,\n",
       "             5.39316982e-02,   3.82038318e-02,   9.37618129e-03],\n",
       "          [ -8.55061710e-02,   6.30031824e-02,  -2.60896496e-02, ...,\n",
       "             5.37981912e-02,   4.99940105e-02,   5.72227351e-02]],\n",
       " \n",
       "         [[ -3.53978388e-02,  -5.35722189e-02,  -2.35678768e-03, ...,\n",
       "            -9.26132724e-02,  -1.32917181e-01,  -9.01023000e-02],\n",
       "          [  2.98457383e-03,   3.34196258e-03,   9.95259508e-02, ...,\n",
       "            -7.06269965e-03,  -2.14789081e-02,  -1.95803475e-02],\n",
       "          [  8.59327242e-02,   1.14443801e-01,   9.64036286e-02, ...,\n",
       "             2.91637629e-02,  -5.27102761e-02,   9.85997394e-02],\n",
       "          ..., \n",
       "          [  5.77987656e-02,  -2.42412537e-02,  -1.81995879e-03, ...,\n",
       "            -5.82536943e-02,   5.39437693e-04,   1.01234801e-01],\n",
       "          [ -6.70049340e-02,  -8.89468417e-02,  -3.79131660e-02, ...,\n",
       "             2.05968749e-02,   4.02817354e-02,   5.49851507e-02],\n",
       "          [  1.53711147e-03,   1.05730228e-01,   1.91255175e-02, ...,\n",
       "             8.97959061e-03,   1.11910813e-02,   1.33069577e-02]],\n",
       " \n",
       "         [[  4.38440181e-02,   1.73315103e-03,   3.51136178e-02, ...,\n",
       "            -8.37618634e-02,   7.06536788e-03,  -4.31708992e-02],\n",
       "          [  8.09320286e-02,  -4.38162573e-02,   2.59434860e-02, ...,\n",
       "            -1.34519329e-02,  -1.55890370e-02,  -6.49991184e-02],\n",
       "          [  3.83570641e-02,   8.10671374e-02,   1.56622361e-02, ...,\n",
       "             3.39561626e-02,  -2.63408776e-02,   1.69951953e-02],\n",
       "          ..., \n",
       "          [ -3.63198705e-02,   4.05097120e-02,   9.43309721e-03, ...,\n",
       "             9.73996297e-02,   6.96874410e-02,  -2.43969690e-02],\n",
       "          [ -8.97622854e-02,  -2.91408356e-02,  -1.08442716e-01, ...,\n",
       "             4.26894724e-02,   4.85990234e-02,   4.65343706e-03],\n",
       "          [ -9.71526206e-02,  -4.21003923e-02,  -6.47213450e-03, ...,\n",
       "             7.35112745e-03,   3.87683101e-02,  -4.31007519e-02]],\n",
       " \n",
       "         [[ -3.58572677e-02,   5.91715723e-02,  -4.00302373e-02, ...,\n",
       "             5.33690080e-02,   3.06813475e-02,   2.35909894e-02],\n",
       "          [ -9.20638517e-02,  -1.05211340e-01,  -3.77955958e-02, ...,\n",
       "            -2.66206078e-02,  -6.69788523e-03,  -5.47943600e-02],\n",
       "          [  1.18634067e-01,   1.32305428e-01,   5.19942911e-03, ...,\n",
       "            -6.10516220e-03,   1.91619352e-03,  -5.05829901e-02],\n",
       "          ..., \n",
       "          [ -1.78921558e-02,   4.49858308e-02,  -5.39338868e-03, ...,\n",
       "            -1.09130725e-01,  -6.37488440e-02,   7.20574055e-03],\n",
       "          [  3.67182717e-02,  -4.82475497e-02,   1.73559301e-02, ...,\n",
       "            -2.10308954e-02,  -3.57839167e-02,   1.20252576e-02],\n",
       "          [  2.90314816e-02,   1.01917060e-02,  -1.83971375e-02, ...,\n",
       "            -1.13641657e-02,  -2.54682004e-02,   6.68117031e-02]]],\n",
       " \n",
       " \n",
       "        [[[ -8.99978802e-02,  -2.94925719e-02,  -9.42782965e-04, ...,\n",
       "             2.40799543e-02,  -5.88887110e-02,   2.69635692e-02],\n",
       "          [ -1.60340092e-03,  -4.38594967e-02,  -6.14226237e-02, ...,\n",
       "            -4.20257114e-02,  -3.19956280e-02,  -8.71935561e-02],\n",
       "          [  4.58673798e-02,  -7.94417039e-02,   2.13008188e-02, ...,\n",
       "             4.86062728e-02,   1.44674780e-03,  -3.11743282e-03],\n",
       "          ..., \n",
       "          [  2.57989727e-02,   1.98712777e-02,   1.09319068e-01, ...,\n",
       "             3.84522229e-02,  -8.79694670e-02,   6.44393265e-02],\n",
       "          [  5.01418067e-03,   7.85017386e-03,  -4.24489379e-02, ...,\n",
       "            -6.72300905e-02,  -3.08005209e-03,   1.39766529e-01],\n",
       "          [  3.24308500e-02,   3.72905992e-02,  -2.95851212e-02, ...,\n",
       "            -4.35414538e-03,   8.49785190e-03,  -3.24718319e-02]],\n",
       " \n",
       "         [[  1.38068935e-02,   1.67125631e-02,   4.12911735e-02, ...,\n",
       "             2.52964869e-02,  -1.46642745e-01,  -4.63260449e-02],\n",
       "          [ -6.31405935e-02,  -6.74366429e-02,   4.19389382e-02, ...,\n",
       "             3.14314552e-02,  -9.52009484e-02,  -1.24035381e-01],\n",
       "          [  1.26097396e-01,  -9.19949263e-02,   1.44375503e-01, ...,\n",
       "            -2.31679194e-02,   2.52599129e-03,  -1.45270489e-02],\n",
       "          ..., \n",
       "          [ -2.27691839e-03,  -1.44634480e-02,   1.54149206e-02, ...,\n",
       "             8.49698260e-02,  -1.94311459e-02,   2.11889092e-02],\n",
       "          [  8.79088119e-02,  -3.52468081e-02,  -3.02978475e-02, ...,\n",
       "            -4.53305021e-02,  -4.03332449e-02,  -4.10134867e-02],\n",
       "          [ -3.72284465e-03,  -8.71449430e-03,   8.16605464e-02, ...,\n",
       "            -9.18604527e-03,   8.30645636e-02,   3.36735584e-02]],\n",
       " \n",
       "         [[ -1.64409727e-02,  -6.61622286e-02,  -5.81443571e-02, ...,\n",
       "            -6.68718992e-03,   4.16901745e-02,   9.00167972e-02],\n",
       "          [  6.12213388e-02,  -1.51829952e-02,   2.78297625e-02, ...,\n",
       "            -2.05440074e-02,  -1.42934490e-02,  -1.06355056e-01],\n",
       "          [  3.39766853e-02,   3.30882482e-02,  -2.73365155e-02, ...,\n",
       "             6.16643988e-02,  -1.28084114e-02,  -1.48612878e-03],\n",
       "          ..., \n",
       "          [ -4.32896614e-03,  -3.10563669e-03,   8.11247097e-04, ...,\n",
       "            -4.59941141e-02,   3.80932688e-05,   1.06197886e-01],\n",
       "          [ -2.94177197e-02,  -1.25971004e-01,  -1.04876094e-01, ...,\n",
       "            -7.69111281e-03,   2.86235251e-02,   5.26867658e-02],\n",
       "          [ -7.83698112e-02,  -1.69695523e-02,   8.35068151e-02, ...,\n",
       "            -1.05786724e-02,   4.93102893e-02,   9.57765877e-02]],\n",
       " \n",
       "         [[  3.46232927e-03,   7.80474916e-02,   1.94680449e-02, ...,\n",
       "            -6.42592609e-02,  -3.32256891e-02,  -5.18384613e-02],\n",
       "          [ -3.21319848e-02,  -5.63893504e-02,  -6.34378046e-02, ...,\n",
       "            -7.09324926e-02,   8.84460509e-02,   4.69471626e-02],\n",
       "          [ -3.37296585e-03,  -7.71213025e-02,   4.95207123e-02, ...,\n",
       "             1.07580811e-01,  -1.42026665e-02,   1.85216144e-02],\n",
       "          ..., \n",
       "          [ -3.56784149e-04,   8.19529127e-03,   8.90885442e-02, ...,\n",
       "            -7.00715184e-03,   6.60436749e-02,   2.87629850e-02],\n",
       "          [  5.19402698e-02,  -1.08111478e-01,  -1.13961056e-01, ...,\n",
       "             2.66744401e-02,   8.05245936e-02,  -2.40285862e-02],\n",
       "          [ -6.67934073e-04,  -5.73506989e-02,   1.05042040e-01, ...,\n",
       "             3.36055248e-03,   4.52501550e-02,   6.41404360e-04]],\n",
       " \n",
       "         [[ -6.96594492e-02,  -1.32638244e-02,  -5.40756918e-02, ...,\n",
       "            -2.74760388e-02,  -8.98154080e-02,  -2.07541306e-02],\n",
       "          [ -3.11560314e-02,  -5.51240332e-02,   1.82107482e-02, ...,\n",
       "             1.46228410e-02,  -7.23394752e-03,  -8.59502852e-02],\n",
       "          [  2.96264701e-02,  -8.76072049e-02,   2.21866313e-02, ...,\n",
       "             1.03564737e-02,  -1.37905568e-01,   3.83651964e-02],\n",
       "          ..., \n",
       "          [ -5.44812344e-02,   4.87144664e-02,  -3.25739873e-03, ...,\n",
       "             8.56959671e-02,  -2.21441370e-02,   2.94217914e-02],\n",
       "          [ -4.61037904e-02,   6.71050474e-02,  -4.57235463e-02, ...,\n",
       "            -8.17189738e-02,  -1.56176630e-02,   3.67605798e-02],\n",
       "          [ -5.05104288e-02,   1.88069381e-02,  -6.60914704e-02, ...,\n",
       "             8.78059864e-02,  -1.68475695e-02,   2.40638154e-03]],\n",
       " \n",
       "         [[ -4.90406267e-02,  -4.05920185e-02,   1.98162440e-02, ...,\n",
       "            -3.82538028e-02,  -5.59659675e-02,   7.94997364e-02],\n",
       "          [ -5.87546937e-02,   2.97586521e-05,   9.25237220e-03, ...,\n",
       "            -1.44370692e-02,   1.43929841e-02,   5.44852279e-02],\n",
       "          [  8.25015679e-02,  -4.50904034e-02,   7.50404922e-03, ...,\n",
       "             8.59871283e-02,  -4.44176793e-02,   5.17368689e-02],\n",
       "          ..., \n",
       "          [ -4.27185409e-02,  -1.18686870e-01,  -2.39925217e-02, ...,\n",
       "            -3.24152894e-02,   4.00165915e-02,   4.51863781e-02],\n",
       "          [ -5.10140322e-02,   2.39307582e-02,  -3.69259790e-02, ...,\n",
       "            -1.29959616e-03,  -9.09777731e-02,   4.88356836e-02],\n",
       "          [ -1.31681664e-02,  -5.46850301e-02,  -5.89500777e-02, ...,\n",
       "             7.24122748e-02,  -3.55940424e-02,   8.66353661e-02]]],\n",
       " \n",
       " \n",
       "        [[[  3.37732658e-02,   4.70141061e-02,  -2.73390524e-02, ...,\n",
       "             5.69345690e-02,   6.10719472e-02,   7.68560022e-02],\n",
       "          [ -1.14177978e-02,  -1.90311596e-02,  -7.45443404e-02, ...,\n",
       "            -7.30257854e-02,   1.35493996e-02,  -6.40842393e-02],\n",
       "          [ -5.03449403e-02,  -2.13133898e-02,   7.42788389e-02, ...,\n",
       "             7.12022558e-02,  -2.28929780e-02,   2.41481364e-02],\n",
       "          ..., \n",
       "          [  7.39556178e-02,  -1.59551781e-02,   9.36700106e-02, ...,\n",
       "             3.27616893e-02,   1.98335778e-02,   1.76787987e-01],\n",
       "          [  1.25069758e-02,   4.91577154e-03,   1.06137153e-02, ...,\n",
       "             3.75298061e-03,  -7.81062394e-02,   9.54881012e-02],\n",
       "          [ -3.28685045e-02,  -5.08762673e-02,   5.41699752e-02, ...,\n",
       "            -9.86825302e-02,   5.94573542e-02,   8.96935642e-04]],\n",
       " \n",
       "         [[ -6.18150411e-03,   5.30510172e-02,  -3.33897956e-02, ...,\n",
       "            -3.12347561e-02,  -1.00131124e-01,  -1.36266008e-01],\n",
       "          [  1.26323318e-02,  -8.47624149e-04,  -3.29525247e-02, ...,\n",
       "             4.28067259e-02,  -3.24072852e-03,  -4.84415367e-02],\n",
       "          [ -3.77446935e-02,  -1.18086869e-02,   4.53118235e-02, ...,\n",
       "             3.04672997e-02,  -5.49185500e-02,   4.19702865e-02],\n",
       "          ..., \n",
       "          [ -3.67772114e-03,   5.47842458e-02,  -3.79045047e-02, ...,\n",
       "             7.27431923e-02,  -2.79709250e-02,  -1.08677097e-01],\n",
       "          [ -4.47631478e-02,  -1.37410313e-02,  -7.24175498e-02, ...,\n",
       "             1.26788523e-02,  -4.95044664e-02,  -7.02855065e-02],\n",
       "          [ -4.06055599e-02,  -1.31344013e-02,   7.53779113e-02, ...,\n",
       "             6.59692734e-02,   1.16551612e-02,   4.23581749e-02]],\n",
       " \n",
       "         [[  6.59258515e-02,   6.45051152e-02,  -1.23667106e-01, ...,\n",
       "            -1.80889815e-02,   4.77154180e-02,  -6.71910122e-02],\n",
       "          [  5.17802872e-02,  -7.67704397e-02,  -3.30441967e-02, ...,\n",
       "            -1.01587828e-02,  -3.20533440e-02,  -5.29243052e-02],\n",
       "          [ -3.64961592e-03,   9.33625698e-02,   3.20100673e-02, ...,\n",
       "             7.16386214e-02,   1.13253230e-02,   8.25790614e-02],\n",
       "          ..., \n",
       "          [ -2.60269456e-02,   7.87197426e-02,   1.46252208e-03, ...,\n",
       "             5.34540601e-02,   3.10727656e-02,   8.08137581e-02],\n",
       "          [  5.57148270e-02,  -4.73770089e-02,  -1.13500394e-01, ...,\n",
       "            -6.16307184e-02,   9.66131911e-02,   4.80613373e-02],\n",
       "          [ -9.22719687e-02,   1.40383681e-02,  -3.24634090e-02, ...,\n",
       "            -6.20407350e-02,   4.16204073e-02,  -4.93977591e-03]],\n",
       " \n",
       "         [[ -4.17911485e-02,   1.41658587e-02,   2.45441925e-02, ...,\n",
       "             3.89956608e-02,  -3.02535165e-02,  -9.82441157e-02],\n",
       "          [  3.82802896e-02,  -1.15586612e-02,  -1.90582220e-02, ...,\n",
       "            -2.77688019e-02,  -3.01499404e-02,  -7.45150894e-02],\n",
       "          [ -6.84433281e-02,  -6.71280622e-02,  -2.77484376e-02, ...,\n",
       "             9.10072401e-02,  -1.54824443e-02,  -9.51138884e-03],\n",
       "          ..., \n",
       "          [ -6.21385910e-02,   1.56319533e-02,   8.63514319e-02, ...,\n",
       "             3.24561894e-02,   6.52559474e-02,   1.14827760e-01],\n",
       "          [ -4.80031930e-02,   4.71999906e-02,  -7.94476345e-02, ...,\n",
       "             5.91021515e-02,   6.27506850e-03,  -2.42961217e-02],\n",
       "          [ -2.43720300e-02,   3.59190032e-02,   1.04922689e-01, ...,\n",
       "             1.36359632e-02,   1.65708307e-02,   5.79521060e-02]],\n",
       " \n",
       "         [[ -4.03942093e-02,  -3.77871189e-03,  -2.07064953e-02, ...,\n",
       "             2.17887331e-02,  -8.81602243e-02,  -1.03900902e-01],\n",
       "          [ -1.24612134e-02,   6.83446601e-02,   6.68213740e-02, ...,\n",
       "            -6.80247024e-02,   3.67267728e-02,  -2.34887786e-02],\n",
       "          [  1.84228662e-02,  -1.72797050e-02,   6.87586963e-02, ...,\n",
       "             1.09961599e-01,  -8.23691022e-03,  -7.89036322e-03],\n",
       "          ..., \n",
       "          [  1.06461933e-02,  -3.28336284e-02,   5.04519753e-02, ...,\n",
       "             5.63745424e-02,  -6.77858442e-02,  -7.88537264e-02],\n",
       "          [ -7.04284385e-03,   6.31514266e-02,  -1.05008706e-02, ...,\n",
       "            -1.16969302e-01,   9.90040749e-02,  -5.53973354e-02],\n",
       "          [  3.06298379e-02,  -6.69901818e-02,  -3.57860923e-02, ...,\n",
       "            -7.93785322e-03,  -8.93748328e-02,   1.29387714e-03]],\n",
       " \n",
       "         [[ -3.93752865e-02,  -9.70603619e-03,   2.24248115e-02, ...,\n",
       "            -4.15351130e-02,  -4.95579615e-02,  -6.59849495e-02],\n",
       "          [  1.64657086e-02,   4.40087058e-02,  -1.09629463e-02, ...,\n",
       "            -4.51412387e-02,  -6.94123805e-02,  -1.48318075e-02],\n",
       "          [  2.08701491e-02,  -3.85760367e-02,   4.55899164e-02, ...,\n",
       "            -3.78295197e-03,   5.31342924e-02,   9.89002138e-02],\n",
       "          ..., \n",
       "          [ -8.55726842e-03,  -5.22630960e-02,  -3.86141129e-02, ...,\n",
       "            -8.16830993e-02,   5.95119372e-02,   3.92528921e-02],\n",
       "          [ -2.67234035e-02,   7.82927349e-02,   3.57640255e-03, ...,\n",
       "            -2.48036105e-02,  -4.16145101e-02,   4.75415960e-03],\n",
       "          [ -5.27525209e-02,   2.00328810e-04,  -3.93935591e-02, ...,\n",
       "             4.37910147e-02,   3.50354868e-03,  -3.08327992e-02]]]], dtype=float32),\n",
       " array([-0.03785726, -0.05633533,  0.02821722, -0.05617409, -0.0169664 ,\n",
       "         0.12677667,  0.07227429,  0.07783621], dtype=float32)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.layers[3].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K.learning_phase() is a flag that indicates if the network is in training or\n",
    "# predict phase. It allow layer (e.g. Dropout) to only be applied during training\n",
    "inputs = [K.learning_phase()] + autoencoder.inputs\n",
    "\n",
    "_layer1_f = K.function(inputs, [x2])\n",
    "def convout1_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _layer1_f([0] + [X])\n",
    "\n",
    "#_lay_f = K.function(inputs, [x1])\n",
    "#def convout1_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "#    return _layer1_f([0] + [X])\n",
    "\n",
    "_layer2_f = K.function(inputs, [x4])\n",
    "def convout2_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _layer2_f([0] + [X])\n",
    "\n",
    "_layer3_f = K.function(inputs, [encoded])\n",
    "def convout3_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _layer3_f([0] + [X])\n",
    "\n",
    "_up_layer1_f = K.function(inputs, [x6])\n",
    "def convout4_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _up_layer1_f([0] + [X])\n",
    "\n",
    "_up_layer2_f = K.function(inputs, [x8])\n",
    "def convout5_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _up_layer2_f([0] + [X])\n",
    "\n",
    "_up_layer3_f = K.function(inputs, [x10])\n",
    "def convout6_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _up_layer3_f([0] + [X])\n",
    "\n",
    "_up_layer4_f = K.function(inputs, [decoded])\n",
    "def convout7_f(X):\n",
    "    # The [0] is to disable the training phase flag\n",
    "    return _up_layer4_f([0] + [X])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'max_pooling2d_4/MaxPool:0' shape=(?, 16, 16, 16) dtype=float32>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = 1\n",
    "x = x_test[i:i+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the first convnet/output layer_1 with sample first test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16, 16)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+sCorK1ttA/vdKV5//fVAzkZHUj8jRozwDzzwQNAn7xOu4MZV0/785z8rWVak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\nxsY023Nnx97oDMNIPDbRGYaReGyiMww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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16defc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x148e10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cddddPW7j9D3uK1/DUg/jMuv/8z//49hcFkuGyvB7g75D2S/+bn7605967Q8/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\nHh3oFEWJPTrQKYoSe/4f5ChwunKX0MgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e974e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157c1da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EgCNHjgTlqOuv2JSXl3thIzrl1KRJk0K/y1mZ9XQrnmp5/fXXe/Yrr7zi2boN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e8bc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16338cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C8vGfH+/58Z/t/v37h54nzJbDEF6xiVcjk2EEADB69OigzENiORUqbFjekck3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\nMGKPdXSGYcSe/x8Y5fJZjE65hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1681f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting conv_1\n",
    "for i in range(4):\n",
    "    #i = 3\n",
    "    x = x_test[i:i+1]\n",
    "    check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)\n",
    "\n",
    "    temp = x[0,:,:,:]\n",
    "    fig, axes = plt.subplots(1, 1, figsize=(3, 3))\n",
    "    plt.imshow(temp)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    k = 0\n",
    "    while k < check.shape[2]:\n",
    "        #plt.figure()\n",
    "        #plt.subplot(231 + i)\n",
    "        fig, axes = plt.subplots(4, 4, figsize=(5, 5))\n",
    "        for i in range(4):\n",
    "            for j in range(4):\n",
    "                axes[i,j].imshow(check[:,:,k], cmap = 'gray')\n",
    "                k += 1\n",
    "        #axes[0, 0].imshow(R, cmap='jet')\n",
    "        #plt.imshow(check[:,:,i])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16, 16)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the second convnet/output layer_2 with sample test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 8, 8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 3\n",
    "x = x_test[i:i+1]\n",
    "check = np.squeeze(np.squeeze(np.array(convout2_f(x)),0),0)\n",
    "check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e646a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AnQVctsmGGHDIBYel8IrzXC7+1FNPTdrsJ0pbGnDFihWBzCEwNlSES33btCf2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\nkMETQkSDDJ4QIhr+P1DaxIrPjY/4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15467a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLu3DvjWbLsWhIewP49Ae9nfZsKC0fbF/a/369YHMyzieffbZSZtLaNm0NA6t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\nDJ4QIhpk8IQQ0SCDJ4SIBhk8IUQ0/D+ECnB2hNOHzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16315128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x79feef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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63SXaXD0aAO6++27PZt3z2Wef9Wx3CTK3zhvIfM9c211MjilvsZkrhd7hrQOw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\npwuIiZgdkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16806d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WT7fddltGXwaTVGVlJYwx0fpjd71trjHi21jyrGHevHkZx/h2nbzG121bBfht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\nCU8IEQxKeEKIYPh/d/PC65s+Mc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148cce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157a6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3Ozs7UVtbi0mTJqGtrS25DeW5c+fQ3d0N51xzMT6qqakJ3ju/r0up21WK119/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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f04cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LrqPisy8GFxSM3kcjZbga3hzv/REAKP5fdrVB59zDzrk251xbTAXN+Xw++aWz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\nJ4SIBgU8IUQ0KOAJIaJBAU8IEQ3/D/6jpMjZdzxmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16577518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1624ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fMsZsM8Zs661vg96grq4uuoLNxkfFGl8p0NzcHF2FxfnI9c/p06eLOcReJ3U1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1510b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLvoYXXrppXk/kOV6+g3+ihZ9jCqlXA1vqve+HQBy/59f6InOududc5ucc5vK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15802b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting conv_2\n",
    "for i in range(4):\n",
    "    #i = 3\n",
    "    x = x_test[i:i+1]\n",
    "    check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)\n",
    "\n",
    "    temp = x[0,:,:,:]\n",
    "    fig, axes = plt.subplots(1, 1, figsize=(3, 3))\n",
    "    plt.imshow(temp)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    k = 0\n",
    "    while k < check.shape[2]:\n",
    "        #plt.figure()\n",
    "        #plt.subplot(231 + i)\n",
    "        fig, axes = plt.subplots(2, 4, figsize=(5, 5))\n",
    "        for i in range(2):\n",
    "            for j in range(4):\n",
    "                axes[i,j].imshow(check[:,:,k])\n",
    "                k += 1\n",
    "        #axes[0, 0].imshow(R, cmap='jet')\n",
    "        #plt.imshow(check[:,:,i])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the third convnet/output layer_3 with sample test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 8)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 3\n",
    "x = x_test[i:i+1]\n",
    "check = np.squeeze(np.squeeze(np.array(convout3_f(x)),0),0)\n",
    "check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14895438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AnQVctsmGGHDIBYel8IrzXC7+1FNPTdrsJ0pbGnDFihWBzCEwNlSES33btCf2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\nkMETQkSDDJ4QIhr+P1DaxIrPjY/4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17097d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLu3DvjWbLsWhIewP49Ae9nfZsKC0fbF/a/369YHMyzieffbZSZtLaNm0NA6t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\nDJ4QIhpk8IQQ0SCDJ4SIBhk8IUQ0/D+ECnB2hNOHzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15471048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1460db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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63SXaXD0aAO6++27PZt3z2Wef9Wx3CTK3zhvIfM9c211MjilvsZkrhd7hrQOw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\npwuIiZgdkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x167fbfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WT7fddltGXwaTVGVlJYwx0fpjd71trjHi21jyrGHevHkZx/h2nbzG121bBfht\n7+OumljScF8bMLTFu7vW1q2XHBgYQCqVivTXuro6XLp0qQLIPUYVFRVei/0f/ciX/NzbJbDW/a1v\nfcuzb7nlFs/+6KOPPPtXv/pVnDslRb5XeM0AVqa3VwJ4c2TcGTtUV1e7HyjFaBhuv/12t/BYMRqG\ndOH5YCZWjAok9grPGPNnAP8DoMEY0wbgZwB+CeA1Y8x3ARwG8EjmPYx9jh8/josXL6K/vx/Hjx/H\nhAkTMGHCBHR3d8MYsweKEf72t7/h0KFD6O7uxk9+8hMsW7YMDzzwAN577z3FKE1LSwtOnjyJy5cv\n4wc/+AGWL1+OZcuW4a233pqoGI0MsQnPWvtohqH/HWFfrlumTZsWbbtTzvLycvT29mqND4CHH344\n2nbLfqZOnYrDhw8rRrh6JzTuyA1gt7V2adEdGoMUdS2tMcarP2JN6ec//3m0za3NuTaO65z+/ve/\ne7bbD89NSMDQGj933S0wVB/M1suMtRu3nXyht9sbbh+ulsZaJMNrZ3/4wx96tvtecP821r94X6wX\nsp9uy3dXTwL8dc8j/UWWu7Y5bk00+8yv0dXwWFPm21KyzbCG58aXeyrOnj07677iKC8v984T7vPn\n6q/pspcI1mL5tgEjcU6PJlpaJoQIBiU8IUQwKOEJIYKhqBpeRUWFd98F1i5cbY01ojiNhPt/Pffc\nczn75dZuAX6/NmCofuPqdnwLyEJ6vQ0Hr1llOwlch+ZqVLzulo/z3//+17O5Zo3rzmpra6NtriV0\ntUO+vWMcVVVV3vvFa6xPnTrFT8kZfp/dNdf8+njFB/ce5DpGNx6AHwPu21eorllWVga3+JjXgrtr\nhLnOjs8D1j1nzpzp2e7tVB977DFvjGs3OSZxn+lrga7whBDBoIQnhAiGok5prbXeVJU7Q7hfl/PX\n3zxlYLhsxb3zOk+/uOSCp0E8zeK2QtlKa9zyA54i5YIxxiuDyedWj5lYt26dZ+/bty/a5nZbjY2N\nns2lEvx+8PPdKRy/z26Mkk5py8rKvH0nfX4S3Gk9yxzc2p+nvHzOsZ/uecPxYTsp/f39nu8nTpzw\nxl0pic9tlpIYHnfLWr797W9nPA4A/Pa3v82672KgKzwhRDAo4QkhgkEJTwgRDEVfWubqE9wW2/1K\nnJdzsUbES3X4K/B77rkn2uaWNnxcbv3DZRT81bx7yz4uPeHnJqWiosJbVpetVX1SeEmdqztxuydu\nd85lGKyDsobqvj+sE/F7mYSBgQFP1+T3xi0ZYr2JdTR+LvvlniespXL5RtwtIRk3JhyfQnVJa62n\nsW7evNkbd/VZjgG/r6zV8vlfvP6SAAACLUlEQVS+ffv2aHv9+vX5OVxEdIUnhAgGJTwhRDAo4Qkh\ngsEUoqckPpgxJwEcAtAAoCPm4aPBSPs121o7Jf5hVynxGF0LnxLFqMTjAyhGuTBqMSpqwosOakxr\nKTY0LCW/SsmXQUrJp1LyxaWU/ColX1xG0y9NaYUQwaCEJ4QIhtFKeKvjHzIqlJJfpeTLIKXkUyn5\n4lJKfpWSLy6j5teoaHhCCDEaaEorhAgGJTwhRDAUNeEZYx40xuwyxuw1xqwq5rHJjzXGmHZjzDbn\nb5ONMW8bY/akf0/Kto9r6JtiFO+bYhTvm2I0DEVLeMaYMgB/APAQgIUAHjXGLCzW8YkXATxIf1sF\n4B1r7XwA76TtoqIYxaMYxaMYZaaYV3h3A9hrrd1vre0F8BcAy4t4/Ahr7UYAnfTn5QDWprfXAlhR\nVKeuoBjFoxjFoxhloJgJbwaAI47dlv5bqdBorT0GAOnfU2Mefy1QjOJRjOJRjDJQzIQ3XJMv1cT4\nKEbxKEbxKEYZKGbCawMwy7FnAjhaxOPHccIYMx0A0r/bYx5/LVCM4lGM4lGMMlDMhNcCYL4xZo4x\nphLANwE0F/H4cTQDWJneXgngzVHwQTGKRzGKRzHKhLW2aD8AlgHYDWAfgGeLeWzy488AjgHow5X/\nht8FcAOufGO0J/178ij5phgpRorRNfrR0jIhRDBopYUQIhiU8IQQwaCEJ4QIBiU8IUQwKOEJIYJB\nCU8IEQxKeEKIYPh/d/PC65s+Mc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15abfa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16893278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3Ozs7UVtbi0mTJqGtrS25DeW5c+fQ3d0N51xzMT6qqakJ3ju/r0up21WK119/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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x157d15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LrqPisy8GFxSM3kcjZbga3hzv/REAKP5fdrVB59zDzrk251xbTAXN+Xw++aWz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\nJ4SIBgU8IUQ0KOAJIaJBAU8IEQ3/D/6jpMjZdzxmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16cdcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157bed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fMsZsM8Zs661vg96grq4uuoLNxkfFGl8p0NzcHF2FxfnI9c/p06eLOcReJ3U1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158194a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLvoYXXrppXk/kOV6+g3+ihZ9jCqlXA1vqve+HQBy/59f6InOududc5ucc5vK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x157cd7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting conv_3\n",
    "for i in range(4):\n",
    "    #i = 3\n",
    "    x = x_test[i:i+1]\n",
    "    check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)\n",
    "\n",
    "    temp = x[0,:,:,:]\n",
    "    fig, axes = plt.subplots(1, 1, figsize=(3, 3))\n",
    "    plt.imshow(temp)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    k = 0\n",
    "    while k < check.shape[2]:\n",
    "        #plt.figure()\n",
    "        #plt.subplot(231 + i)\n",
    "        fig, axes = plt.subplots(2, 4, figsize=(5, 5))\n",
    "        for i in range(2):\n",
    "            for j in range(4):\n",
    "                axes[i,j].imshow(check[:,:,k])\n",
    "                k += 1\n",
    "        #axes[0, 0].imshow(R, cmap='jet')\n",
    "        #plt.imshow(check[:,:,i])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the fourth convnet/decoded/output layer_4 with sample test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 8)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 3\n",
    "x = x_test[i:i+1]\n",
    "check = np.squeeze(np.squeeze(np.array(convout4_f(x)),0),0)\n",
    "check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15109908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AnQVctsmGGHDIBYel8IrzXC7+1FNPTdrsJ0pbGnDFihWBzCEwNlSES33btCf2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\nkMETQkSDDJ4QIhr+P1DaxIrPjY/4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1546bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLu3DvjWbLsWhIewP49Ae9nfZsKC0fbF/a/369YHMyzieffbZSZtLaNm0NA6t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\nDJ4QIhpk8IQQ0SCDJ4SIBhk8IUQ0/D+ECnB2hNOHzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a83ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16cebc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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63SXaXD0aAO6++27PZt3z2Wef9Wx3CTK3zhvIfM9c211MjilvsZkrhd7hrQOw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\npwuIiZgdkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e22f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WT7fddltGXwaTVGVlJYwx0fpjd71trjHi21jyrGHevHkZx/h2nbzG121bBfht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\nCU8IEQxKeEKIYPh/d/PC65s+Mc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x158080b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157beb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3Ozs7UVtbi0mTJqGtrS25DeW5c+fQ3d0N51xzMT6qqakJ3ju/r0up21WK119/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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x153d39b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LrqPisy8GFxSM3kcjZbga3hzv/REAKP5fdrVB59zDzrk251xbTAXN+Xw++aWz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\nJ4SIBgU8IUQ0KOAJIaJBAU8IEQ3/D/6jpMjZdzxmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x156e2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x165f7940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fMsZsM8Zs661vg96grq4uuoLNxkfFGl8p0NzcHF2FxfnI9c/p06eLOcReJ3U1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16dcec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLvoYXXrppXk/kOV6+g3+ihZ9jCqlXA1vqve+HQBy/59f6InOududc5ucc5vK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15400588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting conv_4\n",
    "for i in range(4):\n",
    "    #i = 3\n",
    "    x = x_test[i:i+1]\n",
    "    check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)\n",
    "\n",
    "    temp = x[0,:,:,:]\n",
    "    fig, axes = plt.subplots(1, 1, figsize=(3, 3))\n",
    "    plt.imshow(temp)\n",
    "    plt.show()\n",
    "\n",
    "    k = 0\n",
    "    while k < check.shape[2]:\n",
    "        #plt.figure()\n",
    "        #plt.subplot(231 + i)\n",
    "        fig, axes = plt.subplots(2, 4, figsize=(5, 5))\n",
    "        for i in range(2):\n",
    "            for j in range(4):\n",
    "                axes[i,j].imshow(check[:,:,k])\n",
    "                k += 1\n",
    "        #axes[0, 0].imshow(R, cmap='jet')\n",
    "        #plt.imshow(check[:,:,i])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the fifth convnet/decoded/output layer_5 with sample test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 8, 8)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 3\n",
    "x = x_test[i:i+1]\n",
    "check = np.squeeze(np.squeeze(np.array(convout5_f(x)),0),0)\n",
    "check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15fbc438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AnQVctsmGGHDIBYel8IrzXC7+1FNPTdrsJ0pbGnDFihWBzCEwNlSES33btCf2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\nkMETQkSDDJ4QIhr+P1DaxIrPjY/4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15109e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLu3DvjWbLsWhIewP49Ae9nfZsKC0fbF/a/369YHMyzieffbZSZtLaNm0NA6t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\nDJ4QIhpk8IQQ0SCDJ4SIBhk8IUQ0/D+ECnB2hNOHzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16db3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f441d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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63SXaXD0aAO6++27PZt3z2Wef9Wx3CTK3zhvIfM9c211MjilvsZkrhd7hrQOw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\npwuIiZgdkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x145bf668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WT7fddltGXwaTVGVlJYwx0fpjd71trjHi21jyrGHevHkZx/h2nbzG121bBfht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\nCU8IEQxKeEKIYPh/d/PC65s+Mc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16804c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x165f48d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3Ozs7UVtbi0mTJqGtrS25DeW5c+fQ3d0N51xzMT6qqakJ3ju/r0up21WK119/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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e75c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LrqPisy8GFxSM3kcjZbga3hzv/REAKP5fdrVB59zDzrk251xbTAXN+Xw++aWz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\nJ4SIBgU8IUQ0KOAJIaJBAU8IEQ3/D/6jpMjZdzxmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1685f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e357f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fMsZsM8Zs661vg96grq4uuoLNxkfFGl8p0NzcHF2FxfnI9c/p06eLOcReJ3U1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f4af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLvoYXXrppXk/kOV6+g3+ihZ9jCqlXA1vqve+HQBy/59f6InOududc5ucc5vK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ad46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting conv_5\n",
    "for i in range(4):\n",
    "    #i = 3\n",
    "    x = x_test[i:i+1]\n",
    "    check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)\n",
    "\n",
    "    temp = x[0,:,:,:]\n",
    "    fig, axes = plt.subplots(1, 1, figsize=(3, 3))\n",
    "    plt.imshow(temp)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    k = 0\n",
    "    while k < check.shape[2]:\n",
    "        #plt.figure()\n",
    "        #plt.subplot(231 + i)\n",
    "        fig, axes = plt.subplots(2, 4, figsize=(5, 5))\n",
    "        for i in range(2):\n",
    "            for j in range(4):\n",
    "                axes[i,j].imshow(check[:,:,k])\n",
    "                k += 1\n",
    "        #axes[0, 0].imshow(R, cmap='jet')\n",
    "        #plt.imshow(check[:,:,i])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the sixth convnet/decoded/output layer_6 with sample test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16, 16)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 3\n",
    "x = x_test[i:i+1]\n",
    "check = np.squeeze(np.squeeze(np.array(convout6_f(x)),0),0)\n",
    "check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x170977f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+sCorK1ttA/vdKV5//fVAzkZHUj8jRozwDzzwQNAn7xOu4MZV0/785z8rWVak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\nxsY023Nnx97oDMNIPDbRGYaReGyiMww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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15713cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1630fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cddddPW7j9D3uK1/DUg/jMuv/8z//49hcFkuGyvB7g75D2S/+bn7605967Q8/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\nHh3oFEWJPTrQKYoSe/4f5ChwunKX0MgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d755c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1513fa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EgCNHjgTlqOuv2JSXl3thIzrl1KRJk0K/y1mZ9XQrnmp5/fXXe/Yrr7zi2boN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f162b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x165dbeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C8vGfH+/58Z/t/v37h54nzJbDEF6xiVcjk2EEADB69OigzENiORUqbFjekck3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\nMGKPdXSGYcSe/x8Y5fJZjE65hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x170a0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting conv_6\n",
    "for i in range(4):\n",
    "    #i = 3\n",
    "    x = x_test[i:i+1]\n",
    "    check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)\n",
    "\n",
    "    temp = x[0,:,:,:]\n",
    "    fig, axes = plt.subplots(1, 1, figsize=(3, 3))\n",
    "    plt.imshow(temp)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    k = 0\n",
    "    while k < check.shape[2]:\n",
    "        #plt.figure()\n",
    "        #plt.subplot(231 + i)\n",
    "        fig, axes = plt.subplots(4, 4, figsize=(5, 5))\n",
    "        for i in range(4):\n",
    "            for j in range(4):\n",
    "                axes[i,j].imshow(check[:,:,k])\n",
    "                k += 1\n",
    "        #axes[0, 0].imshow(R, cmap='jet')\n",
    "        #plt.imshow(check[:,:,i])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the final decoded/output layer with sample test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 1\n",
    "x = x_test[i:i+1]\n",
    "check = np.squeeze(np.squeeze(np.array(convout7_f(x)),0),0)\n",
    "check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JW2sosxHivPOxtDshmXRN0u+jI5TDPHqEEtf9PcwpM7N33/jTJn779T9q4uee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\nLoQQQgghhBBC3In/BaPhf4nHAhGsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f8a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot final decoded layer\n",
    "\n",
    "decoded_imgs = autoencoder.predict(x_test)\n",
    "\n",
    "n = 4\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(32, 32, 3))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + n + 1)\n",
    "    plt.imshow(decoded_imgs[i].reshape(32, 32, 3))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}