secsilm/simple-autoencoder.ipynb

## simple-autoencoder.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 编码表示的大小\n",
    "encoding_dim = 32\n",
    "# 输入\n",
    "input_img = Input(shape=(784,))\n",
    "# 输入的编码表示\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "# 输入的重建\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A separate encoder model\n",
    "encoder = Model(input_img, encoded)\n",
    "# A decoder model\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "decoder = Model(encoded_input, decoder_layer(encoded_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 载入 mnist\n",
    "(x_train, _), (x_test, _) = mnist.load_data(path='/home/alan/文档/Paper/DATA/mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 5s - loss: 0.6640 - val_loss: 0.6103\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 5s - loss: 0.3349 - val_loss: 0.0840\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 5s - loss: 0.0407 - val_loss: 0.0210\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 5s - loss: 0.0154 - val_loss: 0.0117\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 5s - loss: 0.0098 - val_loss: 0.0085\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 5s - loss: 0.0076 - val_loss: 0.0070\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 6s - loss: 0.0065 - val_loss: 0.0061\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 6s - loss: 0.0058 - val_loss: 0.0056\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 6s - loss: 0.0054 - val_loss: 0.0052\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 6s - loss: 0.0051 - val_loss: 0.0050\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.0049 - val_loss: 0.0049\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0048 - val_loss: 0.0047\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0046 - val_loss: 0.0046\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.0046 - val_loss: 0.0046\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 6s - loss: 0.0045 - val_loss: 0.0045\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 6s - loss: 0.0045 - val_loss: 0.0045\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0044 - val_loss: 0.0044\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0044 - val_loss: 0.0044\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.0043 - val_loss: 0.0044\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0043 - val_loss: 0.0044\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0042 - val_loss: 0.0043\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.0042 - val_loss: 0.0043\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0042 - val_loss: 0.0043\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 10s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 10s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 9s - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 11s - loss: 0.0041 - val_loss: 0.0041\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa40162b1d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'dense_1/kernel:0' shape=(784, 32) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_1/bias:0' shape=(32,) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_2/kernel:0' shape=(32, 784) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_2/bias:0' shape=(784,) dtype=float32_ref>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'dense_1/kernel:0' shape=(784, 32) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_1/bias:0' shape=(32,) dtype=float32_ref>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'dense_2/kernel:0' shape=(32, 784) dtype=float32_ref>,\n",
       " <tf.Variable 'dense_2/bias:0' shape=(784,) dtype=float32_ref>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 要显示的图片数量\n",
    "n = 10\n",
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_imgs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3e45f7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # 原始图像\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    # 重建的图像\n",
    "    ax = plt.subplot(2, n, i+1+n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+X5fHyXC6defY8u/FzFx8p6UMD2ou6rgBAAAAqJQbNwAAAACVcuMGAAAAoFJL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LAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3f3026390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "for i in range(5000, 5100):\n",
    "    # 重建的图像\n",
    "    ax = plt.subplot(10, 10, (i+1)%5000)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6808656"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_imgs.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00051966513"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00024039,  0.00023481,  0.00024137,  0.00024558,  0.00024049,\n",
       "         0.00023667,  0.00024427,  0.00024408,  0.0002375 ,  0.00023203,\n",
       "         0.00023487,  0.00023932,  0.00023752,  0.00024236,  0.00024637,\n",
       "         0.00023967,  0.00023459,  0.00024124,  0.00023601,  0.0002436 ,\n",
       "         0.00023875,  0.00023812,  0.00023995,  0.00023822,  0.00024435,\n",
       "         0.00023833,  0.00023803,  0.00023875],\n",
       "       [ 0.00023891,  0.00023591,  0.00023079,  0.0002347 ,  0.0002391 ,\n",
       "         0.00022964,  0.00023515,  0.00023816,  0.00024489,  0.00024024,\n",
       "         0.00024509,  0.00024064,  0.00023044,  0.00023825,  0.00024286,\n",
       "         0.0002425 ,  0.00022821,  0.00024786,  0.00023531,  0.00023916,\n",
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       "         0.00024416,  0.00023918,  0.00024347],\n",
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       "         0.00024293,  0.00023858,  0.00024269],\n",
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       "         0.00023232,  0.00023967,  0.00023994],\n",
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       "         0.00074903,  0.00053709,  0.00041185,  0.00033109,  0.0002728 ,\n",
       "         0.00024052,  0.00024287,  0.00024157],\n",
       "       [ 0.00023998,  0.00023615,  0.00024354,  0.00024007,  0.00025924,\n",
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       "         0.00078842,  0.00058515,  0.00044563,  0.00034274,  0.0002761 ,\n",
       "         0.00024049,  0.00022726,  0.00023501],\n",
       "       [ 0.00023474,  0.00024397,  0.00024218,  0.00023926,  0.00026785,\n",
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       "         0.00080215,  0.00058239,  0.00043859,  0.00033784,  0.00027837,\n",
       "         0.00025117,  0.00024258,  0.00024385],\n",
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       "         0.00037039,  0.00050268,  0.00065713,  0.00081379,  0.00092946,\n",
       "         0.0010035 ,  0.00109349,  0.00121601,  0.00140039,  0.00156811,\n",
       "         0.00160903,  0.00156038,  0.00144305,  0.00125231,  0.00104048,\n",
       "         0.0007882 ,  0.00057075,  0.00041528,  0.00032013,  0.00027357,\n",
       "         0.00024863,  0.00023772,  0.0002429 ],\n",
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       "         0.00038074,  0.00051207,  0.00065532,  0.00078277,  0.00087702,\n",
       "         0.0009566 ,  0.00103137,  0.00111857,  0.00129789,  0.0014725 ,\n",
       "         0.00155738,  0.00154651,  0.00144851,  0.00126986,  0.0010328 ,\n",
       "         0.00076623,  0.0005464 ,  0.00040619,  0.00031383,  0.00026236,\n",
       "         0.00024702,  0.00023946,  0.00023776],\n",
       "       [ 0.00023582,  0.00024156,  0.00024456,  0.00025276,  0.00029933,\n",
       "         0.00040227,  0.00054131,  0.00068928,  0.00083465,  0.00095407,\n",
       "         0.0010488 ,  0.00111105,  0.00122802,  0.00139288,  0.00156373,\n",
       "         0.00164536,  0.00161522,  0.0014829 ,  0.0012466 ,  0.00095763,\n",
       "         0.00069528,  0.00049605,  0.00036709,  0.0002958 ,  0.00026029,\n",
       "         0.000246  ,  0.00024109,  0.00023094],\n",
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       "         0.00038568,  0.00053328,  0.00072009,  0.00092354,  0.00109131,\n",
       "         0.00123124,  0.00134005,  0.00147838,  0.00164172,  0.00175974,\n",
       "         0.00176998,  0.00165675,  0.00142539,  0.00112662,  0.00081807,\n",
       "         0.00056839,  0.0004246 ,  0.00033899,  0.00027396,  0.00026304,\n",
       "         0.00024943,  0.00023594,  0.00024575],\n",
       "       [ 0.00024105,  0.00023707,  0.00024292,  0.00025802,  0.00027815,\n",
       "         0.00035972,  0.0004965 ,  0.00070413,  0.00094682,  0.0011797 ,\n",
       "         0.00139067,  0.0015626 ,  0.00172268,  0.0018465 ,  0.0018672 ,\n",
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       "         0.0004568 ,  0.00034448,  0.00029849,  0.00025793,  0.00025203,\n",
       "         0.00024733,  0.00024219,  0.00024543],\n",
       "       [ 0.00024272,  0.00024569,  0.00024533,  0.00024801,  0.00026544,\n",
       "         0.0002974 ,  0.00040891,  0.00057558,  0.000813  ,  0.00107368,\n",
       "         0.00135819,  0.00157395,  0.00172018,  0.00176741,  0.00169415,\n",
       "         0.00148489,  0.00117885,  0.00087855,  0.00063181,  0.00045888,\n",
       "         0.0003526 ,  0.00030161,  0.00026461,  0.00025317,  0.0002486 ,\n",
       "         0.00023579,  0.00024012,  0.00024814],\n",
       "       [ 0.00023258,  0.0002394 ,  0.00024349,  0.000234  ,  0.0002507 ,\n",
       "         0.00027256,  0.00031764,  0.00042215,  0.00057962,  0.00079886,\n",
       "         0.00104072,  0.0012497 ,  0.0013636 ,  0.00136307,  0.00123228,\n",
       "         0.00101754,  0.00076446,  0.00056198,  0.00042951,  0.00033282,\n",
       "         0.00028913,  0.00026535,  0.00025406,  0.00024585,  0.00024437,\n",
       "         0.00024099,  0.00023647,  0.00023943],\n",
       "       [ 0.00023973,  0.0002414 ,  0.00024271,  0.00024363,  0.00024836,\n",
       "         0.00024732,  0.00026328,  0.00029745,  0.00035657,  0.00044722,\n",
       "         0.00055708,  0.00064915,  0.00070239,  0.00069102,  0.00062666,\n",
       "         0.00053822,  0.00044539,  0.0003745 ,  0.00031179,  0.00028686,\n",
       "         0.00025476,  0.00024651,  0.00025042,  0.00024895,  0.00024792,\n",
       "         0.00023955,  0.00023911,  0.00024375],\n",
       "       [ 0.00023709,  0.00023864,  0.00023081,  0.00024406,  0.00023584,\n",
       "         0.00024419,  0.00025194,  0.00026444,  0.00028002,  0.00030468,\n",
       "         0.00033503,  0.00034903,  0.00036472,  0.00037328,  0.0003524 ,\n",
       "         0.00032018,  0.0003123 ,  0.00029561,  0.00027335,  0.00026578,\n",
       "         0.00024462,  0.00023999,  0.00024512,  0.00024482,  0.00024132,\n",
       "         0.00023712,  0.00023186,  0.00024133],\n",
       "       [ 0.00023064,  0.00024115,  0.00023884,  0.00024126,  0.00023614,\n",
       "         0.00023157,  0.00024051,  0.00025319,  0.00025824,  0.00025171,\n",
       "         0.00027211,  0.00028033,  0.00027826,  0.00027339,  0.00026889,\n",
       "         0.00026674,  0.00026029,  0.00025507,  0.00024392,  0.00024697,\n",
       "         0.00024425,  0.0002376 ,  0.00024468,  0.00023844,  0.00024222,\n",
       "         0.00024773,  0.00024084,  0.00023033],\n",
       "       [ 0.00023831,  0.00024142,  0.00023693,  0.00024519,  0.0002366 ,\n",
       "         0.00024047,  0.0002458 ,  0.00024264,  0.00024746,  0.00024095,\n",
       "         0.00024016,  0.00023785,  0.00024369,  0.00024515,  0.00024796,\n",
       "         0.00024403,  0.00024483,  0.00023967,  0.00024407,  0.00023346,\n",
       "         0.00023399,  0.00023934,  0.00023843,  0.00024481,  0.00024551,\n",
       "         0.00024614,  0.00023915,  0.00023523]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded_imgs[0].reshape(28, 28)"
   ]
  }
 ],
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