5/ds_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fashion MNIST: classification of fashion images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We develop a model that classifies fashion images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset and explore some of its statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://firebasestorage.googleapis.com/v0/b/z2tma61d2a74hya815w9x621uszb3a.appspot.com/o/FashionMNIST_dataset.zip?alt=media&token=e013aa17-558d-4e8b-9a30-d51532b6c0d1'\n",
    "local_zip = 'FashionMNIST_dataset.zip'\n",
    "urllib.request.urlretrieve(url, local_zip)\n",
    "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
    "zip_ref.extractall()\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
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       "\n",
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       "3       0  ...         3         0         0         0         0         1   \n",
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       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
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       "\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_table('fashion-mnist_train.csv', sep=',')\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Columns: 785 entries, label to pixel784\n",
      "dtypes: int64(785)\n",
      "memory usage: 359.3 MB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      0       0       0       0       0       0       0       0       9   \n",
       "1      1       0       0       0       0       0       0       0       0   \n",
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       "3      2       0       0       0       0       0       0       0       0   \n",
       "4      3       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0       8  ...       103        87        56         0         0         0   \n",
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       "3       0  ...       137       126       140         0       133       224   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_table('fashion-mnist_test.csv', sep=',')\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Columns: 785 entries, label to pixel784\n",
      "dtypes: int64(785)\n",
      "memory usage: 59.9 MB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    1000\n",
       "6    1000\n",
       "5    1000\n",
       "4    1000\n",
       "3    1000\n",
       "2    1000\n",
       "9    1000\n",
       "1    1000\n",
       "8    1000\n",
       "0    1000\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.loc[:,'label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is well balanced in each class so we procede to the other steps in preparation for training a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show some training and testing examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n",
    "\n",
    "data_train.loc[:,'class_name'] = data_train.loc[:,'label'].apply(lambda x:class_names[x])\n",
    "data_test.loc[:,'class_name'] = data_test.loc[:,'label'].apply(lambda x:class_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_examples(data):\n",
    "    i,j,k = np.random.randint(0,784,3)\n",
    "    fig = plt.figure(figsize=(15, 15))\n",
    "    ax1 = fig.add_subplot(1, 3, 1)\n",
    "    ax1.set_title(data.iloc[i,-1])\n",
    "    ax1.set_xticklabels([])\n",
    "    ax1.set_yticklabels([])\n",
    "    ax1.imshow(np.reshape(np.array(data.iloc[i,1:-1].values, dtype='float32'), newshape=(28,28)))\n",
    "    ax2 = fig.add_subplot(1, 3, 2)\n",
    "    ax2.set_title(data.iloc[j,-1])\n",
    "    ax2.set_xticklabels([])\n",
    "    ax2.set_yticklabels([])\n",
    "    ax2.imshow(np.reshape(np.array(data.iloc[j,1:-1].values, dtype='float32'), newshape=(28,28)))\n",
    "    ax3 = fig.add_subplot(1, 3, 3)\n",
    "    ax3.set_title(data.iloc[k,-1])\n",
    "    ax3.set_xticklabels([])\n",
    "    ax3.set_yticklabels([])\n",
    "    ax3.imshow(np.reshape(np.array(data.iloc[k,1:-1].values, dtype='float32'), newshape=(28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_examples(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_examples(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the dataset for training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(data_train.iloc[:,1:-1].values, dtype='float32')\n",
    "Y_train = np.array(np.reshape(np.eye(10)[data_train.iloc[:,[0]]],(-1,10)), dtype='float32')\n",
    "\n",
    "X_test = np.array(data_test.iloc[:,1:-1].values, dtype='float32')\n",
    "Y_test = np.array(np.reshape(np.eye(10)[data_test.iloc[:,[0]]],(-1,10)), dtype='float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 784), (60000, 10), (10000, 784), (10000, 10))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape, X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function shows the training history of the model to see how is performing in every step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training(model):\n",
    "    loss = model.history.history['loss']\n",
    "    val_loss = model.history.history['val_loss']\n",
    "    accuracy = model.history.history['categorical_accuracy']\n",
    "    val_accuracy = model.history.history['val_categorical_accuracy']\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax1 = fig.add_subplot(2, 1, 1)\n",
    "    ax1.set_title('LOSS')\n",
    "    ax1.set_xlabel('iterations')\n",
    "    ax1.set_ylabel('loss')\n",
    "    ax1.plot(loss, marker='.', color='#FF00AE', markersize=1, linestyle='-', label='train')\n",
    "    ax1.plot(val_loss, marker='.', color='#0F00FF', markersize=1, linestyle='-', label='val')\n",
    "    ax1.legend()\n",
    "    ax2 = fig.add_subplot(2, 1, 2)\n",
    "    ax2.set_title('ACCURACY')\n",
    "    ax2.set_xlabel('iterations')\n",
    "    ax2.set_ylabel('accuracy')\n",
    "    ax2.plot(accuracy, marker='.', color='#FF00AE', markersize=1, linestyle='-', label='train')\n",
    "    ax2.plot(val_accuracy, marker='.', color='#0F00FF', markersize=1, linestyle='-', label='val')\n",
    "    ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our first model and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_1():\n",
    "    model = tf.keras.Sequential([   \n",
    "        tf.keras.layers.Lambda(function=lambda x: x/255.,\n",
    "                               input_shape=[X_train.shape[1]]),\n",
    "        tf.keras.layers.Dense(units=64,\n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1], \n",
    "                              activation='softmax',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='categorical_crossentropy', metrics=['categorical_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=1000, callbacks=[callback1, callback2], validation_data=(X_test, Y_test))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda (Lambda)              (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                50240     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 50,890\n",
      "Trainable params: 50,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "60000/60000 [==============================] - 2s 41us/sample - loss: 0.7262 - categorical_accuracy: 0.7416 - val_loss: 0.4562 - val_categorical_accuracy: 0.8360\n",
      "Epoch 2/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.5561 - categorical_accuracy: 0.7998 - val_loss: 0.4160 - val_categorical_accuracy: 0.8531\n",
      "Epoch 3/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.5197 - categorical_accuracy: 0.8131 - val_loss: 0.4021 - val_categorical_accuracy: 0.8543\n",
      "Epoch 4/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.4956 - categorical_accuracy: 0.8205 - val_loss: 0.3928 - val_categorical_accuracy: 0.8595\n",
      "Epoch 5/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.4808 - categorical_accuracy: 0.8247 - val_loss: 0.3805 - val_categorical_accuracy: 0.8646\n",
      "Epoch 6/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4726 - categorical_accuracy: 0.8278 - val_loss: 0.3714 - val_categorical_accuracy: 0.8658\n",
      "Epoch 7/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.4609 - categorical_accuracy: 0.8313 - val_loss: 0.3756 - val_categorical_accuracy: 0.8648\n",
      "Epoch 8/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4592 - categorical_accuracy: 0.8304 - val_loss: 0.3743 - val_categorical_accuracy: 0.8674\n",
      "Epoch 9/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4496 - categorical_accuracy: 0.8340 - val_loss: 0.3647 - val_categorical_accuracy: 0.8697\n",
      "Epoch 10/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4458 - categorical_accuracy: 0.8336 - val_loss: 0.3621 - val_categorical_accuracy: 0.8696\n",
      "Epoch 11/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4391 - categorical_accuracy: 0.8376 - val_loss: 0.3576 - val_categorical_accuracy: 0.8707\n",
      "Epoch 12/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4379 - categorical_accuracy: 0.8391 - val_loss: 0.3655 - val_categorical_accuracy: 0.8703\n",
      "Epoch 13/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4383 - categorical_accuracy: 0.8379 - val_loss: 0.3613 - val_categorical_accuracy: 0.8703\n",
      "Epoch 14/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4284 - categorical_accuracy: 0.8397 - val_loss: 0.3684 - val_categorical_accuracy: 0.8695\n",
      "Epoch 15/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4275 - categorical_accuracy: 0.8412 - val_loss: 0.3570 - val_categorical_accuracy: 0.8707\n",
      "Epoch 16/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4268 - categorical_accuracy: 0.8420 - val_loss: 0.3649 - val_categorical_accuracy: 0.8722\n",
      "Epoch 17/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.4213 - categorical_accuracy: 0.8451 - val_loss: 0.3544 - val_categorical_accuracy: 0.8734\n",
      "Epoch 18/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.4171 - categorical_accuracy: 0.8435 - val_loss: 0.3566 - val_categorical_accuracy: 0.8729\n",
      "Epoch 19/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4203 - categorical_accuracy: 0.8445 - val_loss: 0.3585 - val_categorical_accuracy: 0.8715\n",
      "Epoch 20/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.4140 - categorical_accuracy: 0.8468 - val_loss: 0.3516 - val_categorical_accuracy: 0.8751\n",
      "Epoch 21/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4117 - categorical_accuracy: 0.8475 - val_loss: 0.3550 - val_categorical_accuracy: 0.8728\n",
      "Epoch 22/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4115 - categorical_accuracy: 0.8466 - val_loss: 0.3549 - val_categorical_accuracy: 0.8737\n",
      "Epoch 23/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4131 - categorical_accuracy: 0.8473 - val_loss: 0.3532 - val_categorical_accuracy: 0.8770\n",
      "Epoch 24/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.4083 - categorical_accuracy: 0.8471 - val_loss: 0.3516 - val_categorical_accuracy: 0.8751\n",
      "Epoch 25/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4070 - categorical_accuracy: 0.8477 - val_loss: 0.3520 - val_categorical_accuracy: 0.8746\n",
      "Epoch 26/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.4048 - categorical_accuracy: 0.8485 - val_loss: 0.3685 - val_categorical_accuracy: 0.8708\n",
      "Epoch 27/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3997 - categorical_accuracy: 0.8498 - val_loss: 0.3475 - val_categorical_accuracy: 0.8787\n",
      "Epoch 28/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3980 - categorical_accuracy: 0.8507 - val_loss: 0.3410 - val_categorical_accuracy: 0.8799\n",
      "Epoch 29/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3991 - categorical_accuracy: 0.8505 - val_loss: 0.3516 - val_categorical_accuracy: 0.8751\n",
      "Epoch 30/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3977 - categorical_accuracy: 0.8517 - val_loss: 0.3536 - val_categorical_accuracy: 0.8739\n",
      "Epoch 31/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3980 - categorical_accuracy: 0.8502 - val_loss: 0.3475 - val_categorical_accuracy: 0.8780\n",
      "Epoch 32/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3919 - categorical_accuracy: 0.8521 - val_loss: 0.3541 - val_categorical_accuracy: 0.8770\n",
      "Epoch 33/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3908 - categorical_accuracy: 0.8539 - val_loss: 0.3538 - val_categorical_accuracy: 0.8753\n",
      "Epoch 34/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3881 - categorical_accuracy: 0.8535 - val_loss: 0.3557 - val_categorical_accuracy: 0.8755\n",
      "Epoch 35/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3917 - categorical_accuracy: 0.8526 - val_loss: 0.3569 - val_categorical_accuracy: 0.8736\n",
      "Epoch 36/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.3883 - categorical_accuracy: 0.8530 - val_loss: 0.3567 - val_categorical_accuracy: 0.8737\n",
      "Epoch 37/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3836 - categorical_accuracy: 0.8551 - val_loss: 0.3481 - val_categorical_accuracy: 0.8796\n",
      "Epoch 38/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3874 - categorical_accuracy: 0.8558 - val_loss: 0.3531 - val_categorical_accuracy: 0.8765\n",
      "Epoch 39/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3882 - categorical_accuracy: 0.8558 - val_loss: 0.3498 - val_categorical_accuracy: 0.8783\n",
      "Epoch 40/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3796 - categorical_accuracy: 0.8570 - val_loss: 0.3526 - val_categorical_accuracy: 0.8761\n",
      "Epoch 41/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3835 - categorical_accuracy: 0.8555 - val_loss: 0.3559 - val_categorical_accuracy: 0.8770\n",
      "Epoch 42/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3872 - categorical_accuracy: 0.8542 - val_loss: 0.3560 - val_categorical_accuracy: 0.8734\n",
      "Epoch 43/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3815 - categorical_accuracy: 0.8564 - val_loss: 0.3583 - val_categorical_accuracy: 0.8807\n",
      "Epoch 44/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3797 - categorical_accuracy: 0.8568 - val_loss: 0.3583 - val_categorical_accuracy: 0.8800\n",
      "Epoch 45/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3811 - categorical_accuracy: 0.8567 - val_loss: 0.3486 - val_categorical_accuracy: 0.8808\n",
      "Epoch 46/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3798 - categorical_accuracy: 0.8561 - val_loss: 0.3510 - val_categorical_accuracy: 0.8798\n",
      "Epoch 47/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3790 - categorical_accuracy: 0.8566 - val_loss: 0.3577 - val_categorical_accuracy: 0.8775\n",
      "Epoch 48/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3794 - categorical_accuracy: 0.8572 - val_loss: 0.3532 - val_categorical_accuracy: 0.8819\n",
      "Epoch 49/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3771 - categorical_accuracy: 0.8571 - val_loss: 0.3498 - val_categorical_accuracy: 0.8778\n",
      "Epoch 50/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.3802 - categorical_accuracy: 0.8551 - val_loss: 0.3566 - val_categorical_accuracy: 0.8783\n",
      "Epoch 51/1000\n",
      "60000/60000 [==============================] - 2s 41us/sample - loss: 0.3759 - categorical_accuracy: 0.8595 - val_loss: 0.3517 - val_categorical_accuracy: 0.8804\n",
      "Epoch 52/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3738 - categorical_accuracy: 0.8579 - val_loss: 0.3502 - val_categorical_accuracy: 0.8777\n",
      "Epoch 53/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.3744 - categorical_accuracy: 0.8595 - val_loss: 0.3534 - val_categorical_accuracy: 0.8784\n",
      "Epoch 54/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3701 - categorical_accuracy: 0.8608 - val_loss: 0.3681 - val_categorical_accuracy: 0.8776\n",
      "Epoch 55/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3721 - categorical_accuracy: 0.8595 - val_loss: 0.3586 - val_categorical_accuracy: 0.8764\n",
      "Epoch 56/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3728 - categorical_accuracy: 0.8584 - val_loss: 0.3513 - val_categorical_accuracy: 0.8812\n",
      "Epoch 57/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3720 - categorical_accuracy: 0.8605 - val_loss: 0.3604 - val_categorical_accuracy: 0.8747\n",
      "Epoch 58/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3741 - categorical_accuracy: 0.8578 - val_loss: 0.3564 - val_categorical_accuracy: 0.8809\n",
      "Epoch 59/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3674 - categorical_accuracy: 0.8609 - val_loss: 0.3622 - val_categorical_accuracy: 0.8804\n",
      "Epoch 60/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3727 - categorical_accuracy: 0.8587 - val_loss: 0.3603 - val_categorical_accuracy: 0.8802\n",
      "Epoch 61/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3665 - categorical_accuracy: 0.8618 - val_loss: 0.3652 - val_categorical_accuracy: 0.8783\n",
      "Epoch 62/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3692 - categorical_accuracy: 0.8604 - val_loss: 0.3633 - val_categorical_accuracy: 0.8786\n",
      "Epoch 63/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3693 - categorical_accuracy: 0.8596 - val_loss: 0.3670 - val_categorical_accuracy: 0.8737\n",
      "Epoch 64/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3652 - categorical_accuracy: 0.8624 - val_loss: 0.3645 - val_categorical_accuracy: 0.8787\n",
      "Epoch 65/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3681 - categorical_accuracy: 0.8620 - val_loss: 0.3639 - val_categorical_accuracy: 0.8789\n",
      "Epoch 66/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3641 - categorical_accuracy: 0.8619 - val_loss: 0.3758 - val_categorical_accuracy: 0.8748\n",
      "Epoch 67/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3626 - categorical_accuracy: 0.8613 - val_loss: 0.3745 - val_categorical_accuracy: 0.8793\n",
      "Epoch 68/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3660 - categorical_accuracy: 0.8616 - val_loss: 0.3648 - val_categorical_accuracy: 0.8794\n",
      "Epoch 69/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3662 - categorical_accuracy: 0.8602 - val_loss: 0.3689 - val_categorical_accuracy: 0.8792\n",
      "Epoch 70/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3634 - categorical_accuracy: 0.8624 - val_loss: 0.3727 - val_categorical_accuracy: 0.8781\n",
      "Epoch 71/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3613 - categorical_accuracy: 0.8607 - val_loss: 0.3714 - val_categorical_accuracy: 0.8795\n",
      "Epoch 72/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3605 - categorical_accuracy: 0.8626 - val_loss: 0.3589 - val_categorical_accuracy: 0.8806\n",
      "Epoch 73/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3592 - categorical_accuracy: 0.8635 - val_loss: 0.3663 - val_categorical_accuracy: 0.8816\n",
      "Epoch 74/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3613 - categorical_accuracy: 0.8626 - val_loss: 0.3738 - val_categorical_accuracy: 0.8768\n",
      "Epoch 75/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3592 - categorical_accuracy: 0.8632 - val_loss: 0.3756 - val_categorical_accuracy: 0.8798\n",
      "Epoch 76/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3586 - categorical_accuracy: 0.8644 - val_loss: 0.3674 - val_categorical_accuracy: 0.8815\n",
      "Epoch 77/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3592 - categorical_accuracy: 0.8638 - val_loss: 0.3749 - val_categorical_accuracy: 0.8778\n",
      "Epoch 78/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3601 - categorical_accuracy: 0.8636 - val_loss: 0.3669 - val_categorical_accuracy: 0.8779\n",
      "Epoch 79/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3569 - categorical_accuracy: 0.8641 - val_loss: 0.3738 - val_categorical_accuracy: 0.8782\n",
      "Epoch 80/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3566 - categorical_accuracy: 0.8627 - val_loss: 0.3658 - val_categorical_accuracy: 0.8781\n",
      "Epoch 81/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3577 - categorical_accuracy: 0.8633 - val_loss: 0.3745 - val_categorical_accuracy: 0.8712\n",
      "Epoch 82/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3585 - categorical_accuracy: 0.8627 - val_loss: 0.3822 - val_categorical_accuracy: 0.8783\n",
      "Epoch 83/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3548 - categorical_accuracy: 0.8671 - val_loss: 0.3675 - val_categorical_accuracy: 0.8799\n",
      "Epoch 84/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.3571 - categorical_accuracy: 0.8644 - val_loss: 0.3652 - val_categorical_accuracy: 0.8799\n",
      "Epoch 85/1000\n",
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      "Epoch 86/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3531 - categorical_accuracy: 0.8646 - val_loss: 0.3709 - val_categorical_accuracy: 0.8796\n",
      "Epoch 87/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3559 - categorical_accuracy: 0.8648 - val_loss: 0.3689 - val_categorical_accuracy: 0.8804\n",
      "Epoch 88/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3542 - categorical_accuracy: 0.8648 - val_loss: 0.3664 - val_categorical_accuracy: 0.8795\n",
      "Epoch 89/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3579 - categorical_accuracy: 0.8632 - val_loss: 0.3694 - val_categorical_accuracy: 0.8818\n",
      "Epoch 90/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3506 - categorical_accuracy: 0.8654 - val_loss: 0.3772 - val_categorical_accuracy: 0.8813\n",
      "Epoch 91/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3545 - categorical_accuracy: 0.8647 - val_loss: 0.3788 - val_categorical_accuracy: 0.8814\n",
      "Epoch 92/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3543 - categorical_accuracy: 0.8652 - val_loss: 0.3772 - val_categorical_accuracy: 0.8771\n",
      "Epoch 93/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3525 - categorical_accuracy: 0.8662 - val_loss: 0.3708 - val_categorical_accuracy: 0.8770\n",
      "Epoch 94/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3534 - categorical_accuracy: 0.8646 - val_loss: 0.3714 - val_categorical_accuracy: 0.8788\n",
      "Epoch 95/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3500 - categorical_accuracy: 0.8655 - val_loss: 0.3918 - val_categorical_accuracy: 0.8750\n",
      "Epoch 96/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3502 - categorical_accuracy: 0.8654 - val_loss: 0.3862 - val_categorical_accuracy: 0.8789\n",
      "Epoch 97/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3501 - categorical_accuracy: 0.8657 - val_loss: 0.3775 - val_categorical_accuracy: 0.8786\n",
      "Epoch 98/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3499 - categorical_accuracy: 0.8667 - val_loss: 0.3786 - val_categorical_accuracy: 0.8787\n",
      "Epoch 99/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3496 - categorical_accuracy: 0.8648 - val_loss: 0.3802 - val_categorical_accuracy: 0.8794\n",
      "Epoch 100/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3503 - categorical_accuracy: 0.8653 - val_loss: 0.3767 - val_categorical_accuracy: 0.8769\n",
      "Epoch 101/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3520 - categorical_accuracy: 0.8663 - val_loss: 0.3822 - val_categorical_accuracy: 0.8804\n",
      "Epoch 102/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3485 - categorical_accuracy: 0.8663 - val_loss: 0.3832 - val_categorical_accuracy: 0.8785\n",
      "Epoch 103/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3464 - categorical_accuracy: 0.8682 - val_loss: 0.3818 - val_categorical_accuracy: 0.8797\n",
      "Epoch 104/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3511 - categorical_accuracy: 0.8662 - val_loss: 0.3904 - val_categorical_accuracy: 0.8797\n",
      "Epoch 105/1000\n",
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      "Epoch 106/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3494 - categorical_accuracy: 0.8661 - val_loss: 0.3825 - val_categorical_accuracy: 0.8791\n",
      "Epoch 107/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3478 - categorical_accuracy: 0.8669 - val_loss: 0.3758 - val_categorical_accuracy: 0.8803\n",
      "Epoch 108/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3451 - categorical_accuracy: 0.8679 - val_loss: 0.3749 - val_categorical_accuracy: 0.8828\n",
      "Epoch 109/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3482 - categorical_accuracy: 0.8659 - val_loss: 0.3844 - val_categorical_accuracy: 0.8764\n",
      "Epoch 110/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3486 - categorical_accuracy: 0.8654 - val_loss: 0.3756 - val_categorical_accuracy: 0.8767\n",
      "Epoch 111/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3434 - categorical_accuracy: 0.8680 - val_loss: 0.3949 - val_categorical_accuracy: 0.8764\n",
      "Epoch 112/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3465 - categorical_accuracy: 0.8669 - val_loss: 0.3807 - val_categorical_accuracy: 0.8802\n",
      "Epoch 113/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3482 - categorical_accuracy: 0.8684 - val_loss: 0.3783 - val_categorical_accuracy: 0.8798\n",
      "Epoch 114/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3473 - categorical_accuracy: 0.8664 - val_loss: 0.3866 - val_categorical_accuracy: 0.8779\n",
      "Epoch 115/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3463 - categorical_accuracy: 0.8691 - val_loss: 0.3871 - val_categorical_accuracy: 0.8774\n",
      "Epoch 116/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3445 - categorical_accuracy: 0.8674 - val_loss: 0.3928 - val_categorical_accuracy: 0.8785\n",
      "Epoch 117/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3440 - categorical_accuracy: 0.8682 - val_loss: 0.3771 - val_categorical_accuracy: 0.8832\n",
      "Epoch 118/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3444 - categorical_accuracy: 0.8682 - val_loss: 0.3820 - val_categorical_accuracy: 0.8798\n",
      "Epoch 119/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3427 - categorical_accuracy: 0.8684 - val_loss: 0.3949 - val_categorical_accuracy: 0.8777\n",
      "Epoch 120/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3431 - categorical_accuracy: 0.8675 - val_loss: 0.3836 - val_categorical_accuracy: 0.8786\n",
      "Epoch 121/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3413 - categorical_accuracy: 0.8674 - val_loss: 0.3940 - val_categorical_accuracy: 0.8790\n",
      "Epoch 122/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3408 - categorical_accuracy: 0.8664 - val_loss: 0.3839 - val_categorical_accuracy: 0.8778\n",
      "Epoch 123/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.3406 - categorical_accuracy: 0.8685 - val_loss: 0.3987 - val_categorical_accuracy: 0.8786\n",
      "Epoch 124/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3455 - categorical_accuracy: 0.8678 - val_loss: 0.4301 - val_categorical_accuracy: 0.8774\n",
      "Epoch 125/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3409 - categorical_accuracy: 0.8689 - val_loss: 0.3906 - val_categorical_accuracy: 0.8753\n",
      "Epoch 126/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3401 - categorical_accuracy: 0.8708 - val_loss: 0.3940 - val_categorical_accuracy: 0.8803\n",
      "Epoch 127/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3423 - categorical_accuracy: 0.8690 - val_loss: 0.4021 - val_categorical_accuracy: 0.8788\n",
      "Epoch 128/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3420 - categorical_accuracy: 0.8677 - val_loss: 0.3892 - val_categorical_accuracy: 0.8762\n",
      "Epoch 129/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.3406 - categorical_accuracy: 0.8699 - val_loss: 0.3911 - val_categorical_accuracy: 0.8786\n",
      "Epoch 130/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3436 - categorical_accuracy: 0.8666 - val_loss: 0.3838 - val_categorical_accuracy: 0.8781\n",
      "Epoch 131/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3419 - categorical_accuracy: 0.8687 - val_loss: 0.3899 - val_categorical_accuracy: 0.8765\n",
      "Epoch 132/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3426 - categorical_accuracy: 0.8687 - val_loss: 0.3852 - val_categorical_accuracy: 0.8798\n",
      "Epoch 133/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3381 - categorical_accuracy: 0.8700 - val_loss: 0.3974 - val_categorical_accuracy: 0.8769\n",
      "Epoch 134/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3396 - categorical_accuracy: 0.8695 - val_loss: 0.4079 - val_categorical_accuracy: 0.8779\n",
      "Epoch 135/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3408 - categorical_accuracy: 0.8698 - val_loss: 0.3892 - val_categorical_accuracy: 0.8810\n",
      "Epoch 136/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3394 - categorical_accuracy: 0.8686 - val_loss: 0.3943 - val_categorical_accuracy: 0.8824\n",
      "Epoch 137/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3374 - categorical_accuracy: 0.8691 - val_loss: 0.3900 - val_categorical_accuracy: 0.8785\n",
      "Epoch 138/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3369 - categorical_accuracy: 0.8695 - val_loss: 0.4014 - val_categorical_accuracy: 0.8780\n",
      "Epoch 139/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3390 - categorical_accuracy: 0.8696 - val_loss: 0.3892 - val_categorical_accuracy: 0.8785\n",
      "Epoch 140/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3363 - categorical_accuracy: 0.8713 - val_loss: 0.3984 - val_categorical_accuracy: 0.8766\n",
      "Epoch 141/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3381 - categorical_accuracy: 0.8701 - val_loss: 0.4101 - val_categorical_accuracy: 0.8766\n",
      "Epoch 142/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3375 - categorical_accuracy: 0.8686 - val_loss: 0.3987 - val_categorical_accuracy: 0.8765\n",
      "Epoch 143/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3365 - categorical_accuracy: 0.8709 - val_loss: 0.4090 - val_categorical_accuracy: 0.8787\n",
      "Epoch 144/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3344 - categorical_accuracy: 0.8713 - val_loss: 0.3936 - val_categorical_accuracy: 0.8783\n",
      "Epoch 145/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3396 - categorical_accuracy: 0.8707 - val_loss: 0.3935 - val_categorical_accuracy: 0.8756\n",
      "Epoch 146/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3404 - categorical_accuracy: 0.8686 - val_loss: 0.4351 - val_categorical_accuracy: 0.8750\n",
      "Epoch 147/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3338 - categorical_accuracy: 0.8693 - val_loss: 0.3964 - val_categorical_accuracy: 0.8813\n",
      "Epoch 148/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3386 - categorical_accuracy: 0.8689 - val_loss: 0.3901 - val_categorical_accuracy: 0.8759\n",
      "Epoch 149/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3374 - categorical_accuracy: 0.8700 - val_loss: 0.4035 - val_categorical_accuracy: 0.8784\n",
      "Epoch 150/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3386 - categorical_accuracy: 0.8683 - val_loss: 0.4044 - val_categorical_accuracy: 0.8781\n",
      "Epoch 151/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3342 - categorical_accuracy: 0.8722 - val_loss: 0.4003 - val_categorical_accuracy: 0.8784\n",
      "Epoch 152/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3356 - categorical_accuracy: 0.8713 - val_loss: 0.3927 - val_categorical_accuracy: 0.8767\n",
      "Epoch 153/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3354 - categorical_accuracy: 0.8706 - val_loss: 0.4085 - val_categorical_accuracy: 0.8806\n",
      "Epoch 154/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3317 - categorical_accuracy: 0.8710 - val_loss: 0.3946 - val_categorical_accuracy: 0.8810\n",
      "Epoch 155/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3352 - categorical_accuracy: 0.8715 - val_loss: 0.3951 - val_categorical_accuracy: 0.8785\n",
      "Epoch 156/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3334 - categorical_accuracy: 0.8709 - val_loss: 0.4051 - val_categorical_accuracy: 0.8803\n",
      "Epoch 157/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3357 - categorical_accuracy: 0.8700 - val_loss: 0.4120 - val_categorical_accuracy: 0.8775\n",
      "Epoch 158/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3342 - categorical_accuracy: 0.8711 - val_loss: 0.4053 - val_categorical_accuracy: 0.8774\n",
      "Epoch 159/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3318 - categorical_accuracy: 0.8714 - val_loss: 0.4153 - val_categorical_accuracy: 0.8785\n",
      "Epoch 160/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3348 - categorical_accuracy: 0.8702 - val_loss: 0.4123 - val_categorical_accuracy: 0.8754\n",
      "Epoch 161/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3338 - categorical_accuracy: 0.8708 - val_loss: 0.4015 - val_categorical_accuracy: 0.8814\n",
      "Epoch 162/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3330 - categorical_accuracy: 0.8708 - val_loss: 0.4047 - val_categorical_accuracy: 0.8785\n",
      "Epoch 163/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3347 - categorical_accuracy: 0.8707 - val_loss: 0.4022 - val_categorical_accuracy: 0.8775\n",
      "Epoch 164/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3317 - categorical_accuracy: 0.8718 - val_loss: 0.4068 - val_categorical_accuracy: 0.8760\n",
      "Epoch 165/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3069 - categorical_accuracy: 0.8814 - val_loss: 0.4100 - val_categorical_accuracy: 0.8830\n",
      "Epoch 166/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3035 - categorical_accuracy: 0.8841 - val_loss: 0.4148 - val_categorical_accuracy: 0.8816\n",
      "Epoch 167/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.3022 - categorical_accuracy: 0.8835 - val_loss: 0.4134 - val_categorical_accuracy: 0.8829\n",
      "Epoch 168/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3025 - categorical_accuracy: 0.8838 - val_loss: 0.4171 - val_categorical_accuracy: 0.8818\n",
      "Epoch 169/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3000 - categorical_accuracy: 0.8842 - val_loss: 0.4192 - val_categorical_accuracy: 0.8819\n",
      "Epoch 170/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2980 - categorical_accuracy: 0.8850 - val_loss: 0.4150 - val_categorical_accuracy: 0.8795\n",
      "Epoch 171/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3008 - categorical_accuracy: 0.8842 - val_loss: 0.4183 - val_categorical_accuracy: 0.8810\n",
      "Epoch 172/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2994 - categorical_accuracy: 0.8836 - val_loss: 0.4116 - val_categorical_accuracy: 0.8810\n",
      "Epoch 173/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2975 - categorical_accuracy: 0.8847 - val_loss: 0.4184 - val_categorical_accuracy: 0.8816\n",
      "Epoch 174/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2983 - categorical_accuracy: 0.8833 - val_loss: 0.4167 - val_categorical_accuracy: 0.8818\n",
      "Epoch 175/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.3011 - categorical_accuracy: 0.8832 - val_loss: 0.4161 - val_categorical_accuracy: 0.8821\n",
      "Epoch 176/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2976 - categorical_accuracy: 0.8848 - val_loss: 0.4171 - val_categorical_accuracy: 0.8809\n",
      "Epoch 177/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2984 - categorical_accuracy: 0.8847 - val_loss: 0.4159 - val_categorical_accuracy: 0.8813\n",
      "Epoch 178/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2974 - categorical_accuracy: 0.8849 - val_loss: 0.4227 - val_categorical_accuracy: 0.8820\n",
      "Epoch 179/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2952 - categorical_accuracy: 0.8859 - val_loss: 0.4219 - val_categorical_accuracy: 0.8819\n",
      "Epoch 180/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2951 - categorical_accuracy: 0.8855 - val_loss: 0.4230 - val_categorical_accuracy: 0.8813\n",
      "Epoch 181/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2962 - categorical_accuracy: 0.8840 - val_loss: 0.4233 - val_categorical_accuracy: 0.8816\n",
      "Epoch 182/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2976 - categorical_accuracy: 0.8843 - val_loss: 0.4241 - val_categorical_accuracy: 0.8817\n",
      "Epoch 183/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.3002 - categorical_accuracy: 0.8848 - val_loss: 0.4185 - val_categorical_accuracy: 0.8836\n",
      "Epoch 184/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2967 - categorical_accuracy: 0.8845 - val_loss: 0.4225 - val_categorical_accuracy: 0.8826\n",
      "Epoch 185/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2963 - categorical_accuracy: 0.8844 - val_loss: 0.4227 - val_categorical_accuracy: 0.8826\n",
      "Epoch 186/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2973 - categorical_accuracy: 0.8853 - val_loss: 0.4249 - val_categorical_accuracy: 0.8820\n",
      "Epoch 187/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2929 - categorical_accuracy: 0.8865 - val_loss: 0.4252 - val_categorical_accuracy: 0.8823\n",
      "Epoch 188/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2983 - categorical_accuracy: 0.8845 - val_loss: 0.4236 - val_categorical_accuracy: 0.8814\n",
      "Epoch 189/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2947 - categorical_accuracy: 0.8854 - val_loss: 0.4238 - val_categorical_accuracy: 0.8817\n",
      "Epoch 190/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2996 - categorical_accuracy: 0.8842 - val_loss: 0.4292 - val_categorical_accuracy: 0.8818\n",
      "Epoch 191/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2941 - categorical_accuracy: 0.8860 - val_loss: 0.4256 - val_categorical_accuracy: 0.8817\n",
      "Epoch 192/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2947 - categorical_accuracy: 0.8874 - val_loss: 0.4248 - val_categorical_accuracy: 0.8820\n",
      "Epoch 193/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2951 - categorical_accuracy: 0.8855 - val_loss: 0.4268 - val_categorical_accuracy: 0.8823\n",
      "Epoch 194/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2944 - categorical_accuracy: 0.8853 - val_loss: 0.4253 - val_categorical_accuracy: 0.8806\n",
      "Epoch 195/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2956 - categorical_accuracy: 0.8853 - val_loss: 0.4247 - val_categorical_accuracy: 0.8814\n",
      "Epoch 196/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2944 - categorical_accuracy: 0.8854 - val_loss: 0.4269 - val_categorical_accuracy: 0.8814\n",
      "Epoch 197/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2979 - categorical_accuracy: 0.8845 - val_loss: 0.4289 - val_categorical_accuracy: 0.8819\n",
      "Epoch 198/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2940 - categorical_accuracy: 0.8859 - val_loss: 0.4275 - val_categorical_accuracy: 0.8827\n",
      "Epoch 199/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2916 - categorical_accuracy: 0.8868 - val_loss: 0.4278 - val_categorical_accuracy: 0.8811\n",
      "Epoch 200/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2982 - categorical_accuracy: 0.8836 - val_loss: 0.4255 - val_categorical_accuracy: 0.8810\n",
      "Epoch 201/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2933 - categorical_accuracy: 0.8868 - val_loss: 0.4344 - val_categorical_accuracy: 0.8829\n",
      "Epoch 202/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2941 - categorical_accuracy: 0.8874 - val_loss: 0.4270 - val_categorical_accuracy: 0.8824\n",
      "Epoch 203/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2927 - categorical_accuracy: 0.8871 - val_loss: 0.4308 - val_categorical_accuracy: 0.8820\n",
      "Epoch 204/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2946 - categorical_accuracy: 0.8852 - val_loss: 0.4285 - val_categorical_accuracy: 0.8814\n",
      "Epoch 205/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2967 - categorical_accuracy: 0.8859 - val_loss: 0.4295 - val_categorical_accuracy: 0.8815\n",
      "Epoch 206/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2962 - categorical_accuracy: 0.8838 - val_loss: 0.4276 - val_categorical_accuracy: 0.8828\n",
      "Epoch 207/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2943 - categorical_accuracy: 0.8852 - val_loss: 0.4312 - val_categorical_accuracy: 0.8800\n",
      "Epoch 208/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2944 - categorical_accuracy: 0.8861 - val_loss: 0.4298 - val_categorical_accuracy: 0.8800\n",
      "Epoch 209/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2915 - categorical_accuracy: 0.8870 - val_loss: 0.4361 - val_categorical_accuracy: 0.8821\n",
      "Epoch 210/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2946 - categorical_accuracy: 0.8860 - val_loss: 0.4346 - val_categorical_accuracy: 0.8810\n",
      "Epoch 211/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2949 - categorical_accuracy: 0.8867 - val_loss: 0.4321 - val_categorical_accuracy: 0.8810\n",
      "Epoch 212/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2926 - categorical_accuracy: 0.8871 - val_loss: 0.4319 - val_categorical_accuracy: 0.8818\n",
      "Epoch 213/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2935 - categorical_accuracy: 0.8868 - val_loss: 0.4388 - val_categorical_accuracy: 0.8812\n",
      "Epoch 214/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2935 - categorical_accuracy: 0.8864 - val_loss: 0.4364 - val_categorical_accuracy: 0.8819\n",
      "Epoch 215/1000\n",
      "60000/60000 [==============================] - 2s 35us/sample - loss: 0.2947 - categorical_accuracy: 0.8842 - val_loss: 0.4339 - val_categorical_accuracy: 0.8808\n",
      "Epoch 216/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2916 - categorical_accuracy: 0.8852 - val_loss: 0.4334 - val_categorical_accuracy: 0.8813\n",
      "Epoch 217/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2945 - categorical_accuracy: 0.8866 - val_loss: 0.4323 - val_categorical_accuracy: 0.8813\n",
      "Epoch 218/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2955 - categorical_accuracy: 0.8853 - val_loss: 0.4340 - val_categorical_accuracy: 0.8813\n",
      "Epoch 219/1000\n",
      "60000/60000 [==============================] - 2s 34us/sample - loss: 0.2949 - categorical_accuracy: 0.8864 - val_loss: 0.4360 - val_categorical_accuracy: 0.8802\n"
     ]
    }
   ],
   "source": [
    "model_1 = Model_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 1s 20us/sample - loss: 0.1885 - categorical_accuracy: 0.9308\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1885366970151663, 0.93081665]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 20us/sample - loss: 0.4360 - categorical_accuracy: 0.8802\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.4359746217131615, 0.8802]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing well but it is overfitting so we keep trying with other architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our second model and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_2():\n",
    "    model = tf.keras.Sequential([   \n",
    "        tf.keras.layers.Lambda(function=lambda x: x/255.,\n",
    "                               input_shape=[X_train.shape[1]]),\n",
    "        tf.keras.layers.Dense(units=64,\n",
    "                              activation='relu',\n",
    "                              kernel_initializer='he_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dense(units=64,\n",
    "                              activation='relu',\n",
    "                               kernel_initializer='he_uniform',\n",
    "                           bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1], \n",
    "                              activation='softmax',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='categorical_crossentropy', metrics=['categorical_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=1000, callbacks=[callback1, callback2], validation_data=(X_test, Y_test))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_1 (Lambda)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                50240     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 55,050\n",
      "Trainable params: 55,050\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "60000/60000 [==============================] - 3s 43us/sample - loss: 0.6540 - categorical_accuracy: 0.7741 - val_loss: 0.4314 - val_categorical_accuracy: 0.8488\n",
      "Epoch 2/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.4658 - categorical_accuracy: 0.8368 - val_loss: 0.4115 - val_categorical_accuracy: 0.8539\n",
      "Epoch 3/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.4275 - categorical_accuracy: 0.8521 - val_loss: 0.3787 - val_categorical_accuracy: 0.8604\n",
      "Epoch 4/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.4027 - categorical_accuracy: 0.8573 - val_loss: 0.3721 - val_categorical_accuracy: 0.8630\n",
      "Epoch 5/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3853 - categorical_accuracy: 0.8635 - val_loss: 0.3470 - val_categorical_accuracy: 0.8723\n",
      "Epoch 6/1000\n",
      "60000/60000 [==============================] - 2s 41us/sample - loss: 0.3658 - categorical_accuracy: 0.8702 - val_loss: 0.3683 - val_categorical_accuracy: 0.8684\n",
      "Epoch 7/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3559 - categorical_accuracy: 0.8736 - val_loss: 0.3481 - val_categorical_accuracy: 0.8760\n",
      "Epoch 8/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3457 - categorical_accuracy: 0.8768 - val_loss: 0.3443 - val_categorical_accuracy: 0.8746\n",
      "Epoch 9/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3384 - categorical_accuracy: 0.8790 - val_loss: 0.3415 - val_categorical_accuracy: 0.8751\n",
      "Epoch 10/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3291 - categorical_accuracy: 0.8823 - val_loss: 0.3456 - val_categorical_accuracy: 0.8752\n",
      "Epoch 11/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3231 - categorical_accuracy: 0.8832 - val_loss: 0.3339 - val_categorical_accuracy: 0.8815\n",
      "Epoch 12/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.3154 - categorical_accuracy: 0.8864 - val_loss: 0.3418 - val_categorical_accuracy: 0.8767\n",
      "Epoch 13/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3114 - categorical_accuracy: 0.8881 - val_loss: 0.3394 - val_categorical_accuracy: 0.8782\n",
      "Epoch 14/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.3035 - categorical_accuracy: 0.8903 - val_loss: 0.3521 - val_categorical_accuracy: 0.8765\n",
      "Epoch 15/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.2994 - categorical_accuracy: 0.8922 - val_loss: 0.3368 - val_categorical_accuracy: 0.8840\n",
      "Epoch 16/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2951 - categorical_accuracy: 0.8922 - val_loss: 0.3252 - val_categorical_accuracy: 0.8861\n",
      "Epoch 17/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2901 - categorical_accuracy: 0.8939 - val_loss: 0.3332 - val_categorical_accuracy: 0.8820\n",
      "Epoch 18/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2867 - categorical_accuracy: 0.8950 - val_loss: 0.3493 - val_categorical_accuracy: 0.8815\n",
      "Epoch 19/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2819 - categorical_accuracy: 0.8969 - val_loss: 0.3422 - val_categorical_accuracy: 0.8821\n",
      "Epoch 20/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2779 - categorical_accuracy: 0.8975 - val_loss: 0.3523 - val_categorical_accuracy: 0.8810\n",
      "Epoch 21/1000\n",
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      "Epoch 22/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.2732 - categorical_accuracy: 0.9003 - val_loss: 0.3397 - val_categorical_accuracy: 0.8866\n",
      "Epoch 23/1000\n",
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      "Epoch 24/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.2654 - categorical_accuracy: 0.9015 - val_loss: 0.3356 - val_categorical_accuracy: 0.8896\n",
      "Epoch 25/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.2638 - categorical_accuracy: 0.9025 - val_loss: 0.3413 - val_categorical_accuracy: 0.8854\n",
      "Epoch 26/1000\n",
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      "Epoch 27/1000\n",
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      "Epoch 28/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2541 - categorical_accuracy: 0.9065 - val_loss: 0.3506 - val_categorical_accuracy: 0.8900\n",
      "Epoch 29/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2510 - categorical_accuracy: 0.9071 - val_loss: 0.3591 - val_categorical_accuracy: 0.8858\n",
      "Epoch 30/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2527 - categorical_accuracy: 0.9067 - val_loss: 0.3447 - val_categorical_accuracy: 0.8875\n",
      "Epoch 31/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2478 - categorical_accuracy: 0.9077 - val_loss: 0.3715 - val_categorical_accuracy: 0.8853\n",
      "Epoch 32/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2472 - categorical_accuracy: 0.9080 - val_loss: 0.3727 - val_categorical_accuracy: 0.8857\n",
      "Epoch 33/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2426 - categorical_accuracy: 0.9093 - val_loss: 0.3963 - val_categorical_accuracy: 0.8849\n",
      "Epoch 34/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2424 - categorical_accuracy: 0.9098 - val_loss: 0.3703 - val_categorical_accuracy: 0.8911\n",
      "Epoch 35/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2392 - categorical_accuracy: 0.9115 - val_loss: 0.3740 - val_categorical_accuracy: 0.8891\n",
      "Epoch 36/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2406 - categorical_accuracy: 0.9099 - val_loss: 0.3727 - val_categorical_accuracy: 0.8876\n",
      "Epoch 37/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2360 - categorical_accuracy: 0.9117 - val_loss: 0.3923 - val_categorical_accuracy: 0.8832\n",
      "Epoch 38/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2346 - categorical_accuracy: 0.9131 - val_loss: 0.3769 - val_categorical_accuracy: 0.8900\n",
      "Epoch 39/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2324 - categorical_accuracy: 0.9131 - val_loss: 0.3902 - val_categorical_accuracy: 0.8870\n",
      "Epoch 40/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2335 - categorical_accuracy: 0.9131 - val_loss: 0.4160 - val_categorical_accuracy: 0.8771\n",
      "Epoch 41/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2281 - categorical_accuracy: 0.9144 - val_loss: 0.3836 - val_categorical_accuracy: 0.8877\n",
      "Epoch 42/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2316 - categorical_accuracy: 0.9143 - val_loss: 0.3859 - val_categorical_accuracy: 0.8880\n",
      "Epoch 43/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2265 - categorical_accuracy: 0.9149 - val_loss: 0.3904 - val_categorical_accuracy: 0.8880\n",
      "Epoch 44/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2280 - categorical_accuracy: 0.9152 - val_loss: 0.3921 - val_categorical_accuracy: 0.8873\n",
      "Epoch 45/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2256 - categorical_accuracy: 0.9144 - val_loss: 0.3742 - val_categorical_accuracy: 0.8916\n",
      "Epoch 46/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2246 - categorical_accuracy: 0.9168 - val_loss: 0.4017 - val_categorical_accuracy: 0.8886\n",
      "Epoch 47/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2209 - categorical_accuracy: 0.9181 - val_loss: 0.4168 - val_categorical_accuracy: 0.8842\n",
      "Epoch 48/1000\n",
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      "Epoch 49/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2168 - categorical_accuracy: 0.9184 - val_loss: 0.4076 - val_categorical_accuracy: 0.8899\n",
      "Epoch 50/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2212 - categorical_accuracy: 0.9166 - val_loss: 0.4328 - val_categorical_accuracy: 0.8879\n",
      "Epoch 51/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2165 - categorical_accuracy: 0.9188 - val_loss: 0.4399 - val_categorical_accuracy: 0.8830\n",
      "Epoch 52/1000\n",
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      "Epoch 53/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.2175 - categorical_accuracy: 0.9183 - val_loss: 0.4161 - val_categorical_accuracy: 0.8902\n",
      "Epoch 54/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2136 - categorical_accuracy: 0.9195 - val_loss: 0.4104 - val_categorical_accuracy: 0.8874\n",
      "Epoch 55/1000\n",
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      "Epoch 56/1000\n",
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      "Epoch 57/1000\n",
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      "Epoch 58/1000\n",
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      "Epoch 59/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2110 - categorical_accuracy: 0.9210 - val_loss: 0.4229 - val_categorical_accuracy: 0.8887\n",
      "Epoch 60/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.2086 - categorical_accuracy: 0.9207 - val_loss: 0.4236 - val_categorical_accuracy: 0.8890\n",
      "Epoch 61/1000\n",
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      "Epoch 62/1000\n",
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      "Epoch 63/1000\n",
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      "Epoch 64/1000\n",
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      "Epoch 65/1000\n",
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      "Epoch 66/1000\n",
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      "Epoch 67/1000\n",
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      "Epoch 68/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.2017 - categorical_accuracy: 0.9248 - val_loss: 0.4385 - val_categorical_accuracy: 0.8891\n",
      "Epoch 69/1000\n",
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      "Epoch 70/1000\n",
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      "Epoch 71/1000\n",
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      "Epoch 72/1000\n",
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      "Epoch 73/1000\n",
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      "Epoch 74/1000\n",
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      "Epoch 75/1000\n",
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      "Epoch 76/1000\n",
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      "Epoch 77/1000\n",
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      "Epoch 78/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1898 - categorical_accuracy: 0.9285 - val_loss: 0.4799 - val_categorical_accuracy: 0.8886\n",
      "Epoch 79/1000\n",
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      "Epoch 80/1000\n",
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      "Epoch 81/1000\n",
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      "Epoch 82/1000\n",
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      "Epoch 83/1000\n",
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      "Epoch 84/1000\n",
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      "Epoch 85/1000\n",
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      "Epoch 86/1000\n",
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      "Epoch 87/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.1855 - categorical_accuracy: 0.9297 - val_loss: 0.5185 - val_categorical_accuracy: 0.8861\n",
      "Epoch 88/1000\n",
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      "Epoch 89/1000\n",
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      "Epoch 90/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1805 - categorical_accuracy: 0.9294 - val_loss: 0.5208 - val_categorical_accuracy: 0.8874\n",
      "Epoch 91/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1826 - categorical_accuracy: 0.9313 - val_loss: 0.5561 - val_categorical_accuracy: 0.8860\n",
      "Epoch 92/1000\n",
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.1828 - categorical_accuracy: 0.9286 - val_loss: 0.5437 - val_categorical_accuracy: 0.8860\n",
      "Epoch 93/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.1806 - categorical_accuracy: 0.9299 - val_loss: 0.5667 - val_categorical_accuracy: 0.8811\n",
      "Epoch 94/1000\n",
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      "Epoch 95/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.1827 - categorical_accuracy: 0.9298 - val_loss: 0.5252 - val_categorical_accuracy: 0.8733\n",
      "Epoch 96/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.1807 - categorical_accuracy: 0.9303 - val_loss: 0.5607 - val_categorical_accuracy: 0.8823\n",
      "Epoch 97/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.1780 - categorical_accuracy: 0.9326 - val_loss: 0.5322 - val_categorical_accuracy: 0.8847\n",
      "Epoch 98/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1777 - categorical_accuracy: 0.9318 - val_loss: 0.5794 - val_categorical_accuracy: 0.8836\n",
      "Epoch 99/1000\n",
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      "Epoch 100/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1779 - categorical_accuracy: 0.9310 - val_loss: 0.5436 - val_categorical_accuracy: 0.8877\n",
      "Epoch 101/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1793 - categorical_accuracy: 0.9317 - val_loss: 0.5646 - val_categorical_accuracy: 0.8850\n",
      "Epoch 102/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1769 - categorical_accuracy: 0.9317 - val_loss: 0.5382 - val_categorical_accuracy: 0.8863\n",
      "Epoch 103/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.1783 - categorical_accuracy: 0.9311 - val_loss: 0.5683 - val_categorical_accuracy: 0.8794\n",
      "Epoch 104/1000\n",
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      "Epoch 105/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.1764 - categorical_accuracy: 0.9325 - val_loss: 0.5752 - val_categorical_accuracy: 0.8834\n",
      "Epoch 106/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.1745 - categorical_accuracy: 0.9327 - val_loss: 0.5707 - val_categorical_accuracy: 0.8825\n",
      "Epoch 107/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1743 - categorical_accuracy: 0.9330 - val_loss: 0.5908 - val_categorical_accuracy: 0.8848\n",
      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
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      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
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      "Epoch 112/1000\n",
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      "Epoch 113/1000\n",
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      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
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      "Epoch 116/1000\n",
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      "Epoch 117/1000\n",
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      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
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      "Epoch 120/1000\n",
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      "Epoch 121/1000\n",
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      "Epoch 122/1000\n",
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      "Epoch 123/1000\n",
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      "Epoch 124/1000\n",
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      "Epoch 125/1000\n",
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      "Epoch 126/1000\n",
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      "Epoch 127/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.1662 - categorical_accuracy: 0.9357 - val_loss: 0.6123 - val_categorical_accuracy: 0.8818\n",
      "Epoch 128/1000\n",
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      "Epoch 129/1000\n",
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      "Epoch 130/1000\n",
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      "Epoch 131/1000\n",
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      "Epoch 132/1000\n",
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      "Epoch 133/1000\n"
     ]
    },
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      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.1620 - categorical_accuracy: 0.9383 - val_loss: 0.6466 - val_categorical_accuracy: 0.8820\n",
      "Epoch 134/1000\n",
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      "Epoch 135/1000\n",
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      "Epoch 136/1000\n",
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      "Epoch 137/1000\n",
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      "Epoch 138/1000\n",
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      "Epoch 141/1000\n",
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      "Epoch 142/1000\n",
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      "Epoch 143/1000\n",
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      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
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      "Epoch 151/1000\n",
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      "Epoch 152/1000\n",
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      "Epoch 153/1000\n",
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      "Epoch 156/1000\n",
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      "Epoch 157/1000\n",
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      "Epoch 173/1000\n",
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      "Epoch 175/1000\n",
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      "Epoch 178/1000\n",
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      "Epoch 179/1000\n"
     ]
    },
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     "text": [
      "60000/60000 [==============================] - 2s 40us/sample - loss: 0.1082 - categorical_accuracy: 0.9565 - val_loss: 0.8409 - val_categorical_accuracy: 0.8853\n",
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      "Epoch 182/1000\n",
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      "Epoch 183/1000\n",
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      "Epoch 184/1000\n",
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      "Epoch 185/1000\n",
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      "Epoch 187/1000\n",
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      "Epoch 188/1000\n",
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      "Epoch 189/1000\n",
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      "Epoch 191/1000\n",
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      "Epoch 192/1000\n",
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      "Epoch 193/1000\n",
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      "Epoch 194/1000\n",
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      "Epoch 197/1000\n",
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      "Epoch 198/1000\n",
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      "Epoch 199/1000\n",
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      "Epoch 200/1000\n",
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      "Epoch 202/1000\n",
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      "Epoch 203/1000\n",
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      "Epoch 204/1000\n",
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      "Epoch 206/1000\n",
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      "Epoch 207/1000\n",
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      "Epoch 208/1000\n",
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      "Epoch 209/1000\n",
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      "Epoch 210/1000\n",
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      "Epoch 211/1000\n",
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      "Epoch 213/1000\n",
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      "Epoch 214/1000\n",
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      "Epoch 215/1000\n",
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      "Epoch 216/1000\n",
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      "Epoch 217/1000\n",
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      "Epoch 218/1000\n",
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      "Epoch 219/1000\n",
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      "Epoch 220/1000\n",
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      "Epoch 221/1000\n",
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      "Epoch 222/1000\n",
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      "Epoch 223/1000\n",
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      "Epoch 224/1000\n",
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      "Epoch 225/1000\n"
     ]
    },
    {
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     "text": [
      "60000/60000 [==============================] - 3s 42us/sample - loss: 0.0982 - categorical_accuracy: 0.9603 - val_loss: 0.9691 - val_categorical_accuracy: 0.8858\n",
      "Epoch 226/1000\n",
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      "Epoch 227/1000\n",
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      "Epoch 228/1000\n",
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      "Epoch 229/1000\n",
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      "Epoch 230/1000\n",
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      "Epoch 231/1000\n",
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      "Epoch 232/1000\n",
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      "Epoch 233/1000\n",
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      "Epoch 234/1000\n",
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      "Epoch 235/1000\n",
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      "Epoch 236/1000\n",
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      "Epoch 237/1000\n",
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      "Epoch 238/1000\n",
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      "Epoch 239/1000\n",
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      "Epoch 240/1000\n",
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      "Epoch 241/1000\n",
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      "Epoch 242/1000\n",
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      "Epoch 243/1000\n",
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      "Epoch 244/1000\n",
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      "Epoch 245/1000\n",
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      "Epoch 246/1000\n",
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      "Epoch 247/1000\n",
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      "Epoch 248/1000\n",
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      "Epoch 249/1000\n",
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      "Epoch 250/1000\n",
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      "Epoch 251/1000\n",
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      "Epoch 252/1000\n",
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      "Epoch 253/1000\n",
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      "Epoch 254/1000\n",
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      "Epoch 255/1000\n",
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      "Epoch 256/1000\n",
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      "Epoch 257/1000\n",
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      "Epoch 258/1000\n",
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      "Epoch 259/1000\n",
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      "Epoch 260/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0947 - categorical_accuracy: 0.9604 - val_loss: 1.0634 - val_categorical_accuracy: 0.8849\n",
      "Epoch 261/1000\n",
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      "Epoch 262/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0933 - categorical_accuracy: 0.9614 - val_loss: 1.0883 - val_categorical_accuracy: 0.8833\n",
      "Epoch 263/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0935 - categorical_accuracy: 0.9618 - val_loss: 1.0543 - val_categorical_accuracy: 0.8841\n",
      "Epoch 264/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0940 - categorical_accuracy: 0.9611 - val_loss: 1.0895 - val_categorical_accuracy: 0.8850\n",
      "Epoch 265/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0949 - categorical_accuracy: 0.9612 - val_loss: 1.0753 - val_categorical_accuracy: 0.8833\n",
      "Epoch 266/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0939 - categorical_accuracy: 0.9619 - val_loss: 1.0724 - val_categorical_accuracy: 0.8843\n",
      "Epoch 267/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0943 - categorical_accuracy: 0.9615 - val_loss: 1.1135 - val_categorical_accuracy: 0.8850\n",
      "Epoch 268/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0929 - categorical_accuracy: 0.9619 - val_loss: 1.0926 - val_categorical_accuracy: 0.8831\n",
      "Epoch 269/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0938 - categorical_accuracy: 0.9610 - val_loss: 1.0652 - val_categorical_accuracy: 0.8832\n",
      "Epoch 270/1000\n",
      "60000/60000 [==============================] - 3s 43us/sample - loss: 0.0926 - categorical_accuracy: 0.9623 - val_loss: 1.0824 - val_categorical_accuracy: 0.8835\n",
      "Epoch 271/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.0941 - categorical_accuracy: 0.9609 - val_loss: 1.0693 - val_categorical_accuracy: 0.8831\n",
      "Epoch 272/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0936 - categorical_accuracy: 0.9613 - val_loss: 1.0894 - val_categorical_accuracy: 0.8853\n",
      "Epoch 273/1000\n",
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      "Epoch 274/1000\n",
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      "Epoch 275/1000\n",
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      "Epoch 276/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.0940 - categorical_accuracy: 0.9615 - val_loss: 1.1147 - val_categorical_accuracy: 0.8840\n",
      "Epoch 277/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.0935 - categorical_accuracy: 0.9617 - val_loss: 1.1061 - val_categorical_accuracy: 0.8834\n",
      "Epoch 278/1000\n",
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      "Epoch 279/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0930 - categorical_accuracy: 0.9619 - val_loss: 1.0979 - val_categorical_accuracy: 0.8835\n",
      "Epoch 280/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0915 - categorical_accuracy: 0.9618 - val_loss: 1.1019 - val_categorical_accuracy: 0.8836\n",
      "Epoch 281/1000\n",
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      "Epoch 282/1000\n",
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      "Epoch 283/1000\n",
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      "Epoch 284/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0935 - categorical_accuracy: 0.9616 - val_loss: 1.1275 - val_categorical_accuracy: 0.8837\n",
      "Epoch 285/1000\n",
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      "Epoch 286/1000\n",
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      "Epoch 287/1000\n",
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      "Epoch 288/1000\n",
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      "Epoch 289/1000\n",
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      "Epoch 290/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0904 - categorical_accuracy: 0.9626 - val_loss: 1.1151 - val_categorical_accuracy: 0.8842\n",
      "Epoch 291/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0909 - categorical_accuracy: 0.9628 - val_loss: 1.1471 - val_categorical_accuracy: 0.8848\n",
      "Epoch 292/1000\n",
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      "Epoch 293/1000\n",
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      "Epoch 294/1000\n",
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      "Epoch 295/1000\n",
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      "Epoch 296/1000\n",
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      "Epoch 297/1000\n",
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      "Epoch 298/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0908 - categorical_accuracy: 0.9622 - val_loss: 1.1577 - val_categorical_accuracy: 0.8847\n",
      "Epoch 299/1000\n",
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      "Epoch 300/1000\n",
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      "Epoch 301/1000\n",
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      "Epoch 302/1000\n",
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      "Epoch 303/1000\n",
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      "Epoch 304/1000\n",
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      "Epoch 305/1000\n",
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      "Epoch 306/1000\n",
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      "Epoch 307/1000\n",
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      "Epoch 308/1000\n",
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      "Epoch 309/1000\n",
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      "Epoch 310/1000\n",
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      "Epoch 311/1000\n",
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      "Epoch 312/1000\n",
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      "Epoch 313/1000\n",
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      "Epoch 314/1000\n",
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      "Epoch 315/1000\n",
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      "Epoch 316/1000\n",
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      "Epoch 317/1000\n"
     ]
    },
    {
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     "text": [
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0891 - categorical_accuracy: 0.9630 - val_loss: 1.2109 - val_categorical_accuracy: 0.8842\n",
      "Epoch 318/1000\n",
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      "Epoch 319/1000\n",
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      "Epoch 320/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0886 - categorical_accuracy: 0.9636 - val_loss: 1.1893 - val_categorical_accuracy: 0.8831\n",
      "Epoch 321/1000\n",
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      "Epoch 322/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0888 - categorical_accuracy: 0.9628 - val_loss: 1.2040 - val_categorical_accuracy: 0.8834\n",
      "Epoch 323/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0889 - categorical_accuracy: 0.9629 - val_loss: 1.2186 - val_categorical_accuracy: 0.8836\n",
      "Epoch 324/1000\n",
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      "Epoch 325/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0891 - categorical_accuracy: 0.9636 - val_loss: 1.2163 - val_categorical_accuracy: 0.8838\n",
      "Epoch 326/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0888 - categorical_accuracy: 0.9627 - val_loss: 1.2205 - val_categorical_accuracy: 0.8831\n",
      "Epoch 327/1000\n",
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      "Epoch 328/1000\n",
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      "Epoch 329/1000\n",
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      "Epoch 330/1000\n",
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      "Epoch 331/1000\n",
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      "Epoch 332/1000\n",
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      "Epoch 333/1000\n",
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      "Epoch 334/1000\n",
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      "Epoch 335/1000\n",
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      "Epoch 336/1000\n",
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      "Epoch 337/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0862 - categorical_accuracy: 0.9631 - val_loss: 1.2432 - val_categorical_accuracy: 0.8831\n",
      "Epoch 338/1000\n",
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      "Epoch 339/1000\n",
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      "Epoch 340/1000\n",
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      "Epoch 341/1000\n",
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      "Epoch 342/1000\n",
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      "Epoch 343/1000\n",
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      "Epoch 344/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0870 - categorical_accuracy: 0.9633 - val_loss: 1.2456 - val_categorical_accuracy: 0.8824\n",
      "Epoch 345/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0873 - categorical_accuracy: 0.9640 - val_loss: 1.2762 - val_categorical_accuracy: 0.8851\n",
      "Epoch 346/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0862 - categorical_accuracy: 0.9646 - val_loss: 1.2873 - val_categorical_accuracy: 0.8841\n",
      "Epoch 347/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0858 - categorical_accuracy: 0.9646 - val_loss: 1.2819 - val_categorical_accuracy: 0.8844\n",
      "Epoch 348/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0867 - categorical_accuracy: 0.9638 - val_loss: 1.3083 - val_categorical_accuracy: 0.8835\n",
      "Epoch 349/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0855 - categorical_accuracy: 0.9637 - val_loss: 1.2858 - val_categorical_accuracy: 0.8838\n",
      "Epoch 350/1000\n",
      "60000/60000 [==============================] - 2s 36us/sample - loss: 0.0875 - categorical_accuracy: 0.9636 - val_loss: 1.2792 - val_categorical_accuracy: 0.8828\n",
      "Epoch 351/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0871 - categorical_accuracy: 0.9634 - val_loss: 1.2807 - val_categorical_accuracy: 0.8840\n",
      "Epoch 352/1000\n",
      "60000/60000 [==============================] - 2s 39us/sample - loss: 0.0858 - categorical_accuracy: 0.9635 - val_loss: 1.2607 - val_categorical_accuracy: 0.8853\n",
      "Epoch 353/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0859 - categorical_accuracy: 0.9653 - val_loss: 1.2687 - val_categorical_accuracy: 0.8832\n",
      "Epoch 354/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0857 - categorical_accuracy: 0.9640 - val_loss: 1.2854 - val_categorical_accuracy: 0.8827\n",
      "Epoch 355/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0864 - categorical_accuracy: 0.9640 - val_loss: 1.2802 - val_categorical_accuracy: 0.8813\n",
      "Epoch 356/1000\n",
      "60000/60000 [==============================] - 2s 38us/sample - loss: 0.0857 - categorical_accuracy: 0.9640 - val_loss: 1.3103 - val_categorical_accuracy: 0.8842\n",
      "Epoch 357/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0861 - categorical_accuracy: 0.9645 - val_loss: 1.2993 - val_categorical_accuracy: 0.8823\n",
      "Epoch 358/1000\n",
      "60000/60000 [==============================] - 2s 37us/sample - loss: 0.0852 - categorical_accuracy: 0.9641 - val_loss: 1.3176 - val_categorical_accuracy: 0.8834\n",
      "Epoch 359/1000\n",
      "60000/60000 [==============================] - 2s 42us/sample - loss: 0.0848 - categorical_accuracy: 0.9644 - val_loss: 1.3090 - val_categorical_accuracy: 0.8839\n",
      "Epoch 360/1000\n",
      "60000/60000 [==============================] - 3s 44us/sample - loss: 0.0842 - categorical_accuracy: 0.9644 - val_loss: 1.2990 - val_categorical_accuracy: 0.8845\n",
      "Epoch 361/1000\n",
      "60000/60000 [==============================] - 2s 41us/sample - loss: 0.0849 - categorical_accuracy: 0.9646 - val_loss: 1.3215 - val_categorical_accuracy: 0.8834\n",
      "Epoch 362/1000\n",
      "60000/60000 [==============================] - 3s 42us/sample - loss: 0.0861 - categorical_accuracy: 0.9638 - val_loss: 1.3159 - val_categorical_accuracy: 0.8830\n"
     ]
    }
   ],
   "source": [
    "model_2 = Model_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 1s 21us/sample - loss: 0.0661 - categorical_accuracy: 0.9714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.06608635652473556, 0.9714]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 27us/sample - loss: 1.3159 - categorical_accuracy: 0.8830\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.315931296958751, 0.883]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing worse and it is also overfitting so we discard it. We keep trying with other architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our third model and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_3():\n",
    "    model = tf.keras.Sequential([   \n",
    "        tf.keras.layers.Lambda(function=lambda x: x/255.,\n",
    "                               input_shape=[X_train.shape[1]]),\n",
    "        tf.keras.layers.Reshape(target_shape=(28, 28, 1)),\n",
    "        tf.keras.layers.Conv2D(filters=64,\n",
    "                               kernel_size=(3, 3),\n",
    "                               strides=(1, 1),\n",
    "                               padding='valid',\n",
    "                               activation='relu',\n",
    "                               kernel_initializer='glorot_uniform',\n",
    "                               bias_initializer='zeros'),\n",
    "        tf.keras.layers.MaxPooling2D(pool_size=(2, 2),\n",
    "                                     strides=(2, 2),\n",
    "                                     padding='valid'),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=64,\n",
    "                              activation='relu',\n",
    "                               kernel_initializer='he_uniform',\n",
    "                           bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1], \n",
    "                              activation='softmax',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='categorical_crossentropy', metrics=['categorical_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=1000, callbacks=[callback1, callback2], validation_data=(X_test, Y_test))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_2 (Lambda)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 26, 26, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 10816)             0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 10816)             0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 64)                692288    \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 693,578\n",
      "Trainable params: 693,578\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/1000\n",
      "60000/60000 [==============================] - 19s 319us/sample - loss: 0.4251 - categorical_accuracy: 0.8479 - val_loss: 0.2955 - val_categorical_accuracy: 0.8954\n",
      "Epoch 2/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.3049 - categorical_accuracy: 0.8894 - val_loss: 0.2631 - val_categorical_accuracy: 0.9045\n",
      "Epoch 3/1000\n",
      "60000/60000 [==============================] - 19s 315us/sample - loss: 0.2717 - categorical_accuracy: 0.9005 - val_loss: 0.2640 - val_categorical_accuracy: 0.9050\n",
      "Epoch 4/1000\n",
      "60000/60000 [==============================] - 19s 313us/sample - loss: 0.2451 - categorical_accuracy: 0.9100 - val_loss: 0.2397 - val_categorical_accuracy: 0.9115\n",
      "Epoch 5/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.2253 - categorical_accuracy: 0.9155 - val_loss: 0.2341 - val_categorical_accuracy: 0.9174\n",
      "Epoch 6/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.2115 - categorical_accuracy: 0.9207 - val_loss: 0.2294 - val_categorical_accuracy: 0.9163\n",
      "Epoch 7/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.1960 - categorical_accuracy: 0.9263 - val_loss: 0.2265 - val_categorical_accuracy: 0.9182\n",
      "Epoch 8/1000\n",
      "60000/60000 [==============================] - 18s 305us/sample - loss: 0.1856 - categorical_accuracy: 0.9308 - val_loss: 0.2206 - val_categorical_accuracy: 0.9196\n",
      "Epoch 9/1000\n",
      "60000/60000 [==============================] - 18s 305us/sample - loss: 0.1746 - categorical_accuracy: 0.9340 - val_loss: 0.2264 - val_categorical_accuracy: 0.9193\n",
      "Epoch 10/1000\n",
      "60000/60000 [==============================] - 20s 338us/sample - loss: 0.1650 - categorical_accuracy: 0.9369 - val_loss: 0.2349 - val_categorical_accuracy: 0.9187\n",
      "Epoch 11/1000\n",
      "60000/60000 [==============================] - 19s 316us/sample - loss: 0.1574 - categorical_accuracy: 0.9410 - val_loss: 0.2311 - val_categorical_accuracy: 0.9195\n",
      "Epoch 12/1000\n",
      "60000/60000 [==============================] - 19s 318us/sample - loss: 0.1508 - categorical_accuracy: 0.9427 - val_loss: 0.2349 - val_categorical_accuracy: 0.9208\n",
      "Epoch 13/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.1425 - categorical_accuracy: 0.9459 - val_loss: 0.2338 - val_categorical_accuracy: 0.9227\n",
      "Epoch 14/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.1379 - categorical_accuracy: 0.9474 - val_loss: 0.2408 - val_categorical_accuracy: 0.9211\n",
      "Epoch 15/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.1300 - categorical_accuracy: 0.9502 - val_loss: 0.2458 - val_categorical_accuracy: 0.9188\n",
      "Epoch 16/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.1261 - categorical_accuracy: 0.9520 - val_loss: 0.2557 - val_categorical_accuracy: 0.9225\n",
      "Epoch 17/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.1210 - categorical_accuracy: 0.9532 - val_loss: 0.2668 - val_categorical_accuracy: 0.9229\n",
      "Epoch 18/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.1180 - categorical_accuracy: 0.9549 - val_loss: 0.2446 - val_categorical_accuracy: 0.9235\n",
      "Epoch 19/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.1142 - categorical_accuracy: 0.9573 - val_loss: 0.2635 - val_categorical_accuracy: 0.9227\n",
      "Epoch 20/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.1084 - categorical_accuracy: 0.9592 - val_loss: 0.2706 - val_categorical_accuracy: 0.9222\n",
      "Epoch 21/1000\n",
      "60000/60000 [==============================] - 19s 313us/sample - loss: 0.1091 - categorical_accuracy: 0.9590 - val_loss: 0.2618 - val_categorical_accuracy: 0.9235\n",
      "Epoch 22/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.1016 - categorical_accuracy: 0.9614 - val_loss: 0.2765 - val_categorical_accuracy: 0.9194\n",
      "Epoch 23/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.1027 - categorical_accuracy: 0.9612 - val_loss: 0.2795 - val_categorical_accuracy: 0.9209\n",
      "Epoch 24/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0980 - categorical_accuracy: 0.9631 - val_loss: 0.2778 - val_categorical_accuracy: 0.9250\n",
      "Epoch 25/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0963 - categorical_accuracy: 0.9636 - val_loss: 0.2768 - val_categorical_accuracy: 0.9229\n",
      "Epoch 26/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0935 - categorical_accuracy: 0.9652 - val_loss: 0.2906 - val_categorical_accuracy: 0.9224\n",
      "Epoch 27/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0917 - categorical_accuracy: 0.9658 - val_loss: 0.2917 - val_categorical_accuracy: 0.9227\n",
      "Epoch 28/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0891 - categorical_accuracy: 0.9665 - val_loss: 0.2843 - val_categorical_accuracy: 0.9226\n",
      "Epoch 29/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0879 - categorical_accuracy: 0.9669 - val_loss: 0.2955 - val_categorical_accuracy: 0.9240\n",
      "Epoch 30/1000\n",
      "60000/60000 [==============================] - 19s 313us/sample - loss: 0.0857 - categorical_accuracy: 0.9685 - val_loss: 0.2993 - val_categorical_accuracy: 0.9234\n",
      "Epoch 31/1000\n",
      "60000/60000 [==============================] - 19s 313us/sample - loss: 0.0809 - categorical_accuracy: 0.9693 - val_loss: 0.3213 - val_categorical_accuracy: 0.9209\n",
      "Epoch 32/1000\n",
      "60000/60000 [==============================] - 19s 314us/sample - loss: 0.0839 - categorical_accuracy: 0.9691 - val_loss: 0.3031 - val_categorical_accuracy: 0.9233\n",
      "Epoch 33/1000\n",
      "60000/60000 [==============================] - 19s 318us/sample - loss: 0.0802 - categorical_accuracy: 0.9697 - val_loss: 0.3068 - val_categorical_accuracy: 0.9213\n",
      "Epoch 34/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0787 - categorical_accuracy: 0.9702 - val_loss: 0.3137 - val_categorical_accuracy: 0.9214\n",
      "Epoch 35/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0771 - categorical_accuracy: 0.9707 - val_loss: 0.3258 - val_categorical_accuracy: 0.9236\n",
      "Epoch 36/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0746 - categorical_accuracy: 0.9721 - val_loss: 0.3323 - val_categorical_accuracy: 0.9191\n",
      "Epoch 37/1000\n",
      "60000/60000 [==============================] - 19s 314us/sample - loss: 0.0736 - categorical_accuracy: 0.9726 - val_loss: 0.3206 - val_categorical_accuracy: 0.9206\n",
      "Epoch 38/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0751 - categorical_accuracy: 0.9720 - val_loss: 0.3286 - val_categorical_accuracy: 0.9222\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39/1000\n",
      "60000/60000 [==============================] - 18s 306us/sample - loss: 0.0734 - categorical_accuracy: 0.9725 - val_loss: 0.3365 - val_categorical_accuracy: 0.9200\n",
      "Epoch 40/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0715 - categorical_accuracy: 0.9735 - val_loss: 0.3456 - val_categorical_accuracy: 0.9228\n",
      "Epoch 41/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0715 - categorical_accuracy: 0.9728 - val_loss: 0.3175 - val_categorical_accuracy: 0.9245\n",
      "Epoch 42/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0675 - categorical_accuracy: 0.9746 - val_loss: 0.3354 - val_categorical_accuracy: 0.9238\n",
      "Epoch 43/1000\n",
      "60000/60000 [==============================] - 18s 306us/sample - loss: 0.0689 - categorical_accuracy: 0.9746 - val_loss: 0.3444 - val_categorical_accuracy: 0.9223\n",
      "Epoch 44/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0680 - categorical_accuracy: 0.9749 - val_loss: 0.3409 - val_categorical_accuracy: 0.9222\n",
      "Epoch 45/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0672 - categorical_accuracy: 0.9750 - val_loss: 0.3347 - val_categorical_accuracy: 0.9228\n",
      "Epoch 46/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0661 - categorical_accuracy: 0.9757 - val_loss: 0.3520 - val_categorical_accuracy: 0.9211\n",
      "Epoch 47/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0643 - categorical_accuracy: 0.9761 - val_loss: 0.3575 - val_categorical_accuracy: 0.9220\n",
      "Epoch 48/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0631 - categorical_accuracy: 0.9761 - val_loss: 0.3615 - val_categorical_accuracy: 0.9235\n",
      "Epoch 49/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0643 - categorical_accuracy: 0.9763 - val_loss: 0.3420 - val_categorical_accuracy: 0.9220\n",
      "Epoch 50/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0630 - categorical_accuracy: 0.9765 - val_loss: 0.3549 - val_categorical_accuracy: 0.9208\n",
      "Epoch 51/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0609 - categorical_accuracy: 0.9781 - val_loss: 0.3775 - val_categorical_accuracy: 0.9213\n",
      "Epoch 52/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0610 - categorical_accuracy: 0.9773 - val_loss: 0.3656 - val_categorical_accuracy: 0.9210\n",
      "Epoch 53/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0605 - categorical_accuracy: 0.9775 - val_loss: 0.3711 - val_categorical_accuracy: 0.9207\n",
      "Epoch 54/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0609 - categorical_accuracy: 0.9772 - val_loss: 0.3761 - val_categorical_accuracy: 0.9209\n",
      "Epoch 55/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0584 - categorical_accuracy: 0.9789 - val_loss: 0.3876 - val_categorical_accuracy: 0.9229\n",
      "Epoch 56/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0587 - categorical_accuracy: 0.9785 - val_loss: 0.4040 - val_categorical_accuracy: 0.9193\n",
      "Epoch 57/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0582 - categorical_accuracy: 0.9788 - val_loss: 0.3873 - val_categorical_accuracy: 0.9191\n",
      "Epoch 58/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0556 - categorical_accuracy: 0.9791 - val_loss: 0.3947 - val_categorical_accuracy: 0.9182\n",
      "Epoch 59/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0565 - categorical_accuracy: 0.9791 - val_loss: 0.3970 - val_categorical_accuracy: 0.9201\n",
      "Epoch 60/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0558 - categorical_accuracy: 0.9793 - val_loss: 0.3869 - val_categorical_accuracy: 0.9227\n",
      "Epoch 61/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0572 - categorical_accuracy: 0.9797 - val_loss: 0.3873 - val_categorical_accuracy: 0.9211\n",
      "Epoch 62/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0568 - categorical_accuracy: 0.9789 - val_loss: 0.4086 - val_categorical_accuracy: 0.9188\n",
      "Epoch 63/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0544 - categorical_accuracy: 0.9800 - val_loss: 0.4109 - val_categorical_accuracy: 0.9184\n",
      "Epoch 64/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0534 - categorical_accuracy: 0.9804 - val_loss: 0.4091 - val_categorical_accuracy: 0.9204\n",
      "Epoch 65/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0544 - categorical_accuracy: 0.9806 - val_loss: 0.4308 - val_categorical_accuracy: 0.9215\n",
      "Epoch 66/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0511 - categorical_accuracy: 0.9811 - val_loss: 0.4035 - val_categorical_accuracy: 0.9199\n",
      "Epoch 67/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0536 - categorical_accuracy: 0.9808 - val_loss: 0.4214 - val_categorical_accuracy: 0.9216\n",
      "Epoch 68/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0519 - categorical_accuracy: 0.9816 - val_loss: 0.4118 - val_categorical_accuracy: 0.9206\n",
      "Epoch 69/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0530 - categorical_accuracy: 0.9811 - val_loss: 0.4191 - val_categorical_accuracy: 0.9193\n",
      "Epoch 70/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0546 - categorical_accuracy: 0.9810 - val_loss: 0.4221 - val_categorical_accuracy: 0.9217\n",
      "Epoch 71/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0508 - categorical_accuracy: 0.9815 - val_loss: 0.4394 - val_categorical_accuracy: 0.9204\n",
      "Epoch 72/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0524 - categorical_accuracy: 0.9816 - val_loss: 0.4142 - val_categorical_accuracy: 0.9160\n",
      "Epoch 73/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0497 - categorical_accuracy: 0.9826 - val_loss: 0.4246 - val_categorical_accuracy: 0.9230\n",
      "Epoch 74/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0521 - categorical_accuracy: 0.9810 - val_loss: 0.4279 - val_categorical_accuracy: 0.9223\n",
      "Epoch 75/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0522 - categorical_accuracy: 0.9816 - val_loss: 0.4453 - val_categorical_accuracy: 0.9182\n",
      "Epoch 76/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0485 - categorical_accuracy: 0.9824 - val_loss: 0.4269 - val_categorical_accuracy: 0.9211\n",
      "Epoch 77/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0497 - categorical_accuracy: 0.9821 - val_loss: 0.4218 - val_categorical_accuracy: 0.9189\n",
      "Epoch 78/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0486 - categorical_accuracy: 0.9822 - val_loss: 0.4434 - val_categorical_accuracy: 0.9196\n",
      "Epoch 79/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0510 - categorical_accuracy: 0.9826 - val_loss: 0.4304 - val_categorical_accuracy: 0.9210\n",
      "Epoch 80/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0472 - categorical_accuracy: 0.9829 - val_loss: 0.4366 - val_categorical_accuracy: 0.9227\n",
      "Epoch 81/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0478 - categorical_accuracy: 0.9824 - val_loss: 0.4500 - val_categorical_accuracy: 0.9175\n",
      "Epoch 82/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0467 - categorical_accuracy: 0.9835 - val_loss: 0.4520 - val_categorical_accuracy: 0.9216\n",
      "Epoch 83/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0483 - categorical_accuracy: 0.9828 - val_loss: 0.4550 - val_categorical_accuracy: 0.9192\n",
      "Epoch 84/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0468 - categorical_accuracy: 0.9832 - val_loss: 0.4565 - val_categorical_accuracy: 0.9186\n",
      "Epoch 85/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0471 - categorical_accuracy: 0.9828 - val_loss: 0.4319 - val_categorical_accuracy: 0.9228\n",
      "Epoch 86/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0461 - categorical_accuracy: 0.9840 - val_loss: 0.4456 - val_categorical_accuracy: 0.9208\n",
      "Epoch 87/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0448 - categorical_accuracy: 0.9848 - val_loss: 0.4556 - val_categorical_accuracy: 0.9209\n",
      "Epoch 88/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0447 - categorical_accuracy: 0.9840 - val_loss: 0.4436 - val_categorical_accuracy: 0.9224\n",
      "Epoch 89/1000\n",
      "60000/60000 [==============================] - 19s 312us/sample - loss: 0.0453 - categorical_accuracy: 0.9836 - val_loss: 0.4523 - val_categorical_accuracy: 0.9174\n",
      "Epoch 90/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0456 - categorical_accuracy: 0.9837 - val_loss: 0.4500 - val_categorical_accuracy: 0.9227\n",
      "Epoch 91/1000\n",
      "60000/60000 [==============================] - 18s 306us/sample - loss: 0.0442 - categorical_accuracy: 0.9837 - val_loss: 0.4517 - val_categorical_accuracy: 0.9221\n",
      "Epoch 92/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0446 - categorical_accuracy: 0.9845 - val_loss: 0.4550 - val_categorical_accuracy: 0.9238\n",
      "Epoch 93/1000\n",
      "60000/60000 [==============================] - 19s 308us/sample - loss: 0.0444 - categorical_accuracy: 0.9845 - val_loss: 0.4587 - val_categorical_accuracy: 0.9221\n",
      "Epoch 94/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0440 - categorical_accuracy: 0.9848 - val_loss: 0.4672 - val_categorical_accuracy: 0.9196\n",
      "Epoch 95/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0469 - categorical_accuracy: 0.9845 - val_loss: 0.4694 - val_categorical_accuracy: 0.9183\n",
      "Epoch 96/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0440 - categorical_accuracy: 0.9844 - val_loss: 0.4658 - val_categorical_accuracy: 0.9216\n",
      "Epoch 97/1000\n",
      "60000/60000 [==============================] - 18s 306us/sample - loss: 0.0450 - categorical_accuracy: 0.9841 - val_loss: 0.4643 - val_categorical_accuracy: 0.9230\n",
      "Epoch 98/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0311 - categorical_accuracy: 0.9894 - val_loss: 0.4682 - val_categorical_accuracy: 0.9256\n",
      "Epoch 99/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0272 - categorical_accuracy: 0.9905 - val_loss: 0.4649 - val_categorical_accuracy: 0.9261\n",
      "Epoch 100/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0262 - categorical_accuracy: 0.9911 - val_loss: 0.4676 - val_categorical_accuracy: 0.9260\n",
      "Epoch 101/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0252 - categorical_accuracy: 0.9914 - val_loss: 0.4709 - val_categorical_accuracy: 0.9264\n",
      "Epoch 102/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0252 - categorical_accuracy: 0.9916 - val_loss: 0.4696 - val_categorical_accuracy: 0.9272\n",
      "Epoch 103/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0238 - categorical_accuracy: 0.9916 - val_loss: 0.4758 - val_categorical_accuracy: 0.9268\n",
      "Epoch 104/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0252 - categorical_accuracy: 0.9911 - val_loss: 0.4753 - val_categorical_accuracy: 0.9261\n",
      "Epoch 105/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0220 - categorical_accuracy: 0.9923 - val_loss: 0.4728 - val_categorical_accuracy: 0.9268\n",
      "Epoch 106/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0226 - categorical_accuracy: 0.9923 - val_loss: 0.4774 - val_categorical_accuracy: 0.9264\n",
      "Epoch 107/1000\n",
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      "Epoch 108/1000\n",
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      "Epoch 109/1000\n",
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      "Epoch 110/1000\n",
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      "Epoch 111/1000\n",
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      "Epoch 112/1000\n",
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      "Epoch 113/1000\n",
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      "Epoch 114/1000\n",
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      "Epoch 115/1000\n",
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      "Epoch 116/1000\n",
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      "Epoch 117/1000\n",
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      "Epoch 118/1000\n",
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      "Epoch 119/1000\n",
      "60000/60000 [==============================] - 19s 308us/sample - loss: 0.0193 - categorical_accuracy: 0.9932 - val_loss: 0.4850 - val_categorical_accuracy: 0.9250\n",
      "Epoch 120/1000\n",
      "60000/60000 [==============================] - 19s 308us/sample - loss: 0.0205 - categorical_accuracy: 0.9930 - val_loss: 0.4785 - val_categorical_accuracy: 0.9249\n",
      "Epoch 121/1000\n",
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      "Epoch 122/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0213 - categorical_accuracy: 0.9923 - val_loss: 0.4832 - val_categorical_accuracy: 0.9258\n",
      "Epoch 123/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0204 - categorical_accuracy: 0.9931 - val_loss: 0.4879 - val_categorical_accuracy: 0.9260\n",
      "Epoch 124/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0190 - categorical_accuracy: 0.9931 - val_loss: 0.4831 - val_categorical_accuracy: 0.9271\n",
      "Epoch 125/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0198 - categorical_accuracy: 0.9932 - val_loss: 0.4849 - val_categorical_accuracy: 0.9257\n",
      "Epoch 126/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0199 - categorical_accuracy: 0.9930 - val_loss: 0.4862 - val_categorical_accuracy: 0.9257\n",
      "Epoch 127/1000\n",
      "60000/60000 [==============================] - 19s 308us/sample - loss: 0.0201 - categorical_accuracy: 0.9929 - val_loss: 0.4860 - val_categorical_accuracy: 0.9264\n",
      "Epoch 128/1000\n",
      "60000/60000 [==============================] - 19s 310us/sample - loss: 0.0200 - categorical_accuracy: 0.9930 - val_loss: 0.4901 - val_categorical_accuracy: 0.9260\n",
      "Epoch 129/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0182 - categorical_accuracy: 0.9934 - val_loss: 0.4842 - val_categorical_accuracy: 0.9262\n",
      "Epoch 130/1000\n",
      "60000/60000 [==============================] - 19s 311us/sample - loss: 0.0188 - categorical_accuracy: 0.9933 - val_loss: 0.4877 - val_categorical_accuracy: 0.9262\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 131/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0193 - categorical_accuracy: 0.9931 - val_loss: 0.4860 - val_categorical_accuracy: 0.9262\n",
      "Epoch 132/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0189 - categorical_accuracy: 0.9934 - val_loss: 0.4908 - val_categorical_accuracy: 0.9264\n",
      "Epoch 133/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0193 - categorical_accuracy: 0.9938 - val_loss: 0.4875 - val_categorical_accuracy: 0.9255\n",
      "Epoch 134/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0176 - categorical_accuracy: 0.9939 - val_loss: 0.4910 - val_categorical_accuracy: 0.9257\n",
      "Epoch 135/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0184 - categorical_accuracy: 0.9937 - val_loss: 0.4954 - val_categorical_accuracy: 0.9258\n",
      "Epoch 136/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0204 - categorical_accuracy: 0.9931 - val_loss: 0.4969 - val_categorical_accuracy: 0.9259\n",
      "Epoch 137/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0184 - categorical_accuracy: 0.9937 - val_loss: 0.4934 - val_categorical_accuracy: 0.9259\n",
      "Epoch 138/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0184 - categorical_accuracy: 0.9936 - val_loss: 0.4947 - val_categorical_accuracy: 0.9264\n",
      "Epoch 139/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0178 - categorical_accuracy: 0.9941 - val_loss: 0.4953 - val_categorical_accuracy: 0.9264\n",
      "Epoch 140/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0183 - categorical_accuracy: 0.9934 - val_loss: 0.4942 - val_categorical_accuracy: 0.9273\n",
      "Epoch 141/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0196 - categorical_accuracy: 0.9937 - val_loss: 0.4895 - val_categorical_accuracy: 0.9257\n",
      "Epoch 142/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0179 - categorical_accuracy: 0.9936 - val_loss: 0.4951 - val_categorical_accuracy: 0.9267\n",
      "Epoch 143/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0177 - categorical_accuracy: 0.9936 - val_loss: 0.4939 - val_categorical_accuracy: 0.9263\n",
      "Epoch 144/1000\n",
      "60000/60000 [==============================] - 18s 306us/sample - loss: 0.0180 - categorical_accuracy: 0.9941 - val_loss: 0.4969 - val_categorical_accuracy: 0.9264\n",
      "Epoch 145/1000\n",
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      "Epoch 146/1000\n",
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      "Epoch 147/1000\n",
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      "Epoch 148/1000\n",
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      "Epoch 149/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0180 - categorical_accuracy: 0.9939 - val_loss: 0.4976 - val_categorical_accuracy: 0.9260\n",
      "Epoch 150/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0168 - categorical_accuracy: 0.9942 - val_loss: 0.4979 - val_categorical_accuracy: 0.9254\n",
      "Epoch 151/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0165 - categorical_accuracy: 0.9942 - val_loss: 0.4987 - val_categorical_accuracy: 0.9267\n",
      "Epoch 152/1000\n",
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      "Epoch 153/1000\n",
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      "Epoch 154/1000\n",
      "60000/60000 [==============================] - 18s 306us/sample - loss: 0.0181 - categorical_accuracy: 0.9936 - val_loss: 0.5004 - val_categorical_accuracy: 0.9266\n",
      "Epoch 155/1000\n",
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      "Epoch 156/1000\n",
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      "Epoch 157/1000\n",
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      "Epoch 158/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0166 - categorical_accuracy: 0.9940 - val_loss: 0.5017 - val_categorical_accuracy: 0.9262\n",
      "Epoch 159/1000\n",
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      "Epoch 160/1000\n",
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      "Epoch 161/1000\n",
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      "Epoch 162/1000\n",
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      "Epoch 163/1000\n",
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      "Epoch 164/1000\n",
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      "Epoch 165/1000\n",
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      "Epoch 166/1000\n",
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      "Epoch 167/1000\n",
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      "Epoch 168/1000\n",
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      "Epoch 169/1000\n",
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      "Epoch 170/1000\n",
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      "Epoch 171/1000\n",
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      "Epoch 172/1000\n",
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      "Epoch 173/1000\n",
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      "Epoch 174/1000\n",
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      "Epoch 175/1000\n",
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      "Epoch 176/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 177/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0170 - categorical_accuracy: 0.9941 - val_loss: 0.5103 - val_categorical_accuracy: 0.9254\n",
      "Epoch 178/1000\n",
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      "Epoch 179/1000\n",
      "60000/60000 [==============================] - 18s 307us/sample - loss: 0.0145 - categorical_accuracy: 0.9954 - val_loss: 0.5128 - val_categorical_accuracy: 0.9260\n",
      "Epoch 180/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0154 - categorical_accuracy: 0.9946 - val_loss: 0.5195 - val_categorical_accuracy: 0.9266\n",
      "Epoch 181/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0150 - categorical_accuracy: 0.9949 - val_loss: 0.5161 - val_categorical_accuracy: 0.9254\n",
      "Epoch 182/1000\n",
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      "Epoch 183/1000\n",
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      "Epoch 184/1000\n",
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      "Epoch 185/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0153 - categorical_accuracy: 0.9948 - val_loss: 0.5209 - val_categorical_accuracy: 0.9259\n",
      "Epoch 186/1000\n",
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      "Epoch 187/1000\n",
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      "Epoch 188/1000\n",
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      "Epoch 189/1000\n",
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      "Epoch 190/1000\n",
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      "Epoch 191/1000\n",
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      "Epoch 192/1000\n",
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      "Epoch 193/1000\n",
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      "Epoch 194/1000\n",
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      "Epoch 195/1000\n",
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      "Epoch 196/1000\n",
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      "Epoch 197/1000\n",
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      "Epoch 198/1000\n",
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      "Epoch 199/1000\n",
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      "Epoch 200/1000\n",
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      "Epoch 201/1000\n",
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      "Epoch 202/1000\n",
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      "Epoch 203/1000\n",
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      "Epoch 204/1000\n",
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      "Epoch 205/1000\n",
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      "Epoch 207/1000\n",
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      "Epoch 208/1000\n",
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      "Epoch 209/1000\n",
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      "Epoch 210/1000\n",
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      "Epoch 211/1000\n",
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      "Epoch 212/1000\n",
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      "Epoch 213/1000\n",
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      "Epoch 214/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0145 - categorical_accuracy: 0.9951 - val_loss: 0.5264 - val_categorical_accuracy: 0.9264\n",
      "Epoch 215/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0136 - categorical_accuracy: 0.9952 - val_loss: 0.5231 - val_categorical_accuracy: 0.9275\n",
      "Epoch 216/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0136 - categorical_accuracy: 0.9951 - val_loss: 0.5268 - val_categorical_accuracy: 0.9278\n",
      "Epoch 217/1000\n",
      "60000/60000 [==============================] - 18s 308us/sample - loss: 0.0128 - categorical_accuracy: 0.9956 - val_loss: 0.5302 - val_categorical_accuracy: 0.9255\n",
      "Epoch 218/1000\n",
      "60000/60000 [==============================] - 19s 309us/sample - loss: 0.0148 - categorical_accuracy: 0.9950 - val_loss: 0.5360 - val_categorical_accuracy: 0.9259\n"
     ]
    }
   ],
   "source": [
    "model_3 = Model_3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 4s 65us/sample - loss: 8.0364e-04 - categorical_accuracy: 0.9999\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0008036359940859863, 0.99993336]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 63us/sample - loss: 0.5360 - categorical_accuracy: 0.9259\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5359596041304351, 0.9259]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that the last model is the best one, but overfitting is present so additional exploration of hyperparameters would be necessary to make a final conclusion."
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}