keras/regularization/mlp-mnist-data_augment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP MNIST with data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 256)               200960    \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                2570      \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 269,322\n",
      "Trainable params: 269,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Using real-time data augmentation.\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/20, Batch 214/468\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/20, Batch 56/468\n",
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      "Epoch 17/20, Batch 111/468\n",
      "Epoch 17/20, Batch 112/468\n",
      "Epoch 17/20, Batch 113/468\n",
      "Epoch 17/20, Batch 114/468\n",
      "Epoch 17/20, Batch 115/468\n",
      "Epoch 17/20, Batch 116/468\n",
      "Epoch 17/20, Batch 117/468\n",
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      "Epoch 17/20, Batch 119/468\n",
      "Epoch 17/20, Batch 120/468\n",
      "Epoch 17/20, Batch 121/468\n",
      "Epoch 17/20, Batch 122/468\n",
      "Epoch 17/20, Batch 123/468\n",
      "Epoch 17/20, Batch 124/468\n",
      "Epoch 17/20, Batch 125/468\n",
      "Epoch 17/20, Batch 126/468\n",
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      "Epoch 17/20, Batch 128/468\n",
      "Epoch 17/20, Batch 129/468\n",
      "Epoch 17/20, Batch 130/468\n",
      "Epoch 17/20, Batch 131/468\n",
      "Epoch 17/20, Batch 132/468\n",
      "Epoch 17/20, Batch 133/468\n",
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      "Epoch 17/20, Batch 135/468\n",
      "Epoch 17/20, Batch 136/468\n",
      "Epoch 17/20, Batch 137/468\n",
      "Epoch 17/20, Batch 138/468\n",
      "Epoch 17/20, Batch 139/468\n",
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      "Epoch 17/20, Batch 142/468\n",
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      "Epoch 17/20, Batch 144/468\n",
      "Epoch 17/20, Batch 145/468\n",
      "Epoch 17/20, Batch 146/468\n",
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      "Epoch 17/20, Batch 156/468\n",
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      "Epoch 17/20, Batch 158/468\n",
      "Epoch 17/20, Batch 159/468\n",
      "Epoch 17/20, Batch 160/468\n",
      "Epoch 17/20, Batch 161/468\n",
      "Epoch 17/20, Batch 162/468\n",
      "Epoch 17/20, Batch 163/468\n",
      "Epoch 17/20, Batch 164/468\n",
      "Epoch 17/20, Batch 165/468\n",
      "Epoch 17/20, Batch 166/468\n",
      "Epoch 17/20, Batch 167/468\n",
      "Epoch 17/20, Batch 168/468\n",
      "Epoch 17/20, Batch 169/468\n",
      "Epoch 17/20, Batch 170/468\n",
      "Epoch 17/20, Batch 171/468\n",
      "Epoch 17/20, Batch 172/468\n",
      "Epoch 17/20, Batch 173/468\n",
      "Epoch 17/20, Batch 174/468\n",
      "Epoch 17/20, Batch 175/468\n",
      "Epoch 17/20, Batch 176/468\n",
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      "Epoch 17/20, Batch 178/468\n",
      "Epoch 17/20, Batch 179/468\n",
      "Epoch 17/20, Batch 180/468\n",
      "Epoch 17/20, Batch 181/468\n",
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      "Epoch 17/20, Batch 183/468\n",
      "Epoch 17/20, Batch 184/468\n",
      "Epoch 17/20, Batch 185/468\n",
      "Epoch 17/20, Batch 186/468\n",
      "Epoch 17/20, Batch 187/468\n",
      "Epoch 17/20, Batch 188/468\n",
      "Epoch 17/20, Batch 189/468\n",
      "Epoch 17/20, Batch 190/468\n",
      "Epoch 17/20, Batch 191/468\n",
      "Epoch 17/20, Batch 192/468\n",
      "Epoch 17/20, Batch 193/468\n",
      "Epoch 17/20, Batch 194/468\n",
      "Epoch 17/20, Batch 195/468\n",
      "Epoch 17/20, Batch 196/468\n",
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      "Epoch 17/20, Batch 201/468\n",
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      "Epoch 17/20, Batch 203/468\n",
      "Epoch 17/20, Batch 204/468\n",
      "Epoch 17/20, Batch 205/468\n",
      "Epoch 17/20, Batch 206/468\n",
      "Epoch 17/20, Batch 207/468\n",
      "Epoch 17/20, Batch 208/468\n",
      "Epoch 17/20, Batch 209/468\n",
      "Epoch 17/20, Batch 210/468\n",
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      "Epoch 17/20, Batch 212/468\n",
      "Epoch 17/20, Batch 213/468\n",
      "Epoch 17/20, Batch 214/468\n",
      "Epoch 17/20, Batch 215/468\n",
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      "Epoch 17/20, Batch 222/468\n",
      "Epoch 17/20, Batch 223/468\n",
      "Epoch 17/20, Batch 224/468\n",
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      "Epoch 17/20, Batch 235/468\n",
      "Epoch 17/20, Batch 236/468\n",
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      "Epoch 17/20, Batch 314/468\n",
      "Epoch 17/20, Batch 315/468\n",
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      "Epoch 17/20, Batch 317/468\n",
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      "Epoch 17/20, Batch 319/468\n",
      "Epoch 17/20, Batch 320/468\n",
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      "Epoch 17/20, Batch 324/468\n",
      "Epoch 17/20, Batch 325/468\n",
      "Epoch 17/20, Batch 326/468\n",
      "Epoch 17/20, Batch 327/468\n",
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      "Epoch 17/20, Batch 338/468\n",
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      "Epoch 17/20, Batch 353/468\n",
      "Epoch 17/20, Batch 354/468\n",
      "Epoch 17/20, Batch 355/468\n",
      "Epoch 17/20, Batch 356/468\n",
      "Epoch 17/20, Batch 357/468\n",
      "Epoch 17/20, Batch 358/468\n",
      "Epoch 17/20, Batch 359/468\n",
      "Epoch 17/20, Batch 360/468\n",
      "Epoch 17/20, Batch 361/468\n",
      "Epoch 17/20, Batch 362/468\n",
      "Epoch 17/20, Batch 363/468\n",
      "Epoch 17/20, Batch 364/468\n",
      "Epoch 17/20, Batch 365/468\n",
      "Epoch 17/20, Batch 366/468\n",
      "Epoch 17/20, Batch 367/468\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/20, Batch 368/468\n",
      "Epoch 17/20, Batch 369/468\n",
      "Epoch 17/20, Batch 370/468\n",
      "Epoch 17/20, Batch 371/468\n",
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      "Epoch 17/20, Batch 383/468\n",
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      "Epoch 17/20, Batch 385/468\n",
      "Epoch 17/20, Batch 386/468\n",
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      "Epoch 17/20, Batch 436/468\n",
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      "Epoch 17/20, Batch 438/468\n",
      "Epoch 17/20, Batch 439/468\n",
      "Epoch 17/20, Batch 440/468\n",
      "Epoch 17/20, Batch 441/468\n",
      "Epoch 17/20, Batch 442/468\n",
      "Epoch 17/20, Batch 443/468\n",
      "Epoch 17/20, Batch 444/468\n",
      "Epoch 17/20, Batch 445/468\n",
      "Epoch 17/20, Batch 446/468\n",
      "Epoch 17/20, Batch 447/468\n",
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      "Epoch 17/20, Batch 449/468\n",
      "Epoch 17/20, Batch 450/468\n",
      "Epoch 17/20, Batch 451/468\n",
      "Epoch 17/20, Batch 452/468\n",
      "Epoch 17/20, Batch 453/468\n",
      "Epoch 17/20, Batch 454/468\n",
      "Epoch 17/20, Batch 455/468\n",
      "Epoch 17/20, Batch 456/468\n",
      "Epoch 17/20, Batch 457/468\n",
      "Epoch 17/20, Batch 458/468\n",
      "Epoch 17/20, Batch 459/468\n",
      "Epoch 17/20, Batch 460/468\n",
      "Epoch 17/20, Batch 461/468\n",
      "Epoch 17/20, Batch 462/468\n",
      "Epoch 17/20, Batch 463/468\n",
      "Epoch 17/20, Batch 464/468\n",
      "Epoch 17/20, Batch 465/468\n",
      "Epoch 17/20, Batch 466/468\n",
      "Epoch 17/20, Batch 467/468\n",
      "Epoch 17/20, Batch 468/468\n",
      "Epoch 17/20, Batch 469/468\n",
      "Epoch 18/20, Batch 1/468\n",
      "Epoch 18/20, Batch 2/468\n",
      "Epoch 18/20, Batch 3/468\n",
      "Epoch 18/20, Batch 4/468\n",
      "Epoch 18/20, Batch 5/468\n",
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      "Epoch 18/20, Batch 17/468\n",
      "Epoch 18/20, Batch 18/468\n",
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      "Epoch 18/20, Batch 90/468\n",
      "Epoch 18/20, Batch 91/468\n",
      "Epoch 18/20, Batch 92/468\n",
      "Epoch 18/20, Batch 93/468\n",
      "Epoch 18/20, Batch 94/468\n",
      "Epoch 18/20, Batch 95/468\n",
      "Epoch 18/20, Batch 96/468\n",
      "Epoch 18/20, Batch 97/468\n",
      "Epoch 18/20, Batch 98/468\n",
      "Epoch 18/20, Batch 99/468\n",
      "Epoch 18/20, Batch 100/468\n",
      "Epoch 18/20, Batch 101/468\n",
      "Epoch 18/20, Batch 102/468\n",
      "Epoch 18/20, Batch 103/468\n",
      "Epoch 18/20, Batch 104/468\n",
      "Epoch 18/20, Batch 105/468\n",
      "Epoch 18/20, Batch 106/468\n",
      "Epoch 18/20, Batch 107/468\n",
      "Epoch 18/20, Batch 108/468\n",
      "Epoch 18/20, Batch 109/468\n",
      "Epoch 18/20, Batch 110/468\n",
      "Epoch 18/20, Batch 111/468\n",
      "Epoch 18/20, Batch 112/468\n",
      "Epoch 18/20, Batch 113/468\n",
      "Epoch 18/20, Batch 114/468\n",
      "Epoch 18/20, Batch 115/468\n",
      "Epoch 18/20, Batch 116/468\n",
      "Epoch 18/20, Batch 117/468\n",
      "Epoch 18/20, Batch 118/468\n",
      "Epoch 18/20, Batch 119/468\n",
      "Epoch 18/20, Batch 120/468\n",
      "Epoch 18/20, Batch 121/468\n",
      "Epoch 18/20, Batch 122/468\n",
      "Epoch 18/20, Batch 123/468\n",
      "Epoch 18/20, Batch 124/468\n",
      "Epoch 18/20, Batch 125/468\n",
      "Epoch 18/20, Batch 126/468\n",
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      "Epoch 18/20, Batch 128/468\n",
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      "Epoch 18/20, Batch 130/468\n",
      "Epoch 18/20, Batch 131/468\n",
      "Epoch 18/20, Batch 132/468\n",
      "Epoch 18/20, Batch 133/468\n",
      "Epoch 18/20, Batch 134/468\n",
      "Epoch 18/20, Batch 135/468\n",
      "Epoch 18/20, Batch 136/468\n",
      "Epoch 18/20, Batch 137/468\n",
      "Epoch 18/20, Batch 138/468\n",
      "Epoch 18/20, Batch 139/468\n",
      "Epoch 18/20, Batch 140/468\n",
      "Epoch 18/20, Batch 141/468\n",
      "Epoch 18/20, Batch 142/468\n",
      "Epoch 18/20, Batch 143/468\n",
      "Epoch 18/20, Batch 144/468\n",
      "Epoch 18/20, Batch 145/468\n",
      "Epoch 18/20, Batch 146/468\n",
      "Epoch 18/20, Batch 147/468\n",
      "Epoch 18/20, Batch 148/468\n",
      "Epoch 18/20, Batch 149/468\n",
      "Epoch 18/20, Batch 150/468\n",
      "Epoch 18/20, Batch 151/468\n",
      "Epoch 18/20, Batch 152/468\n",
      "Epoch 18/20, Batch 153/468\n",
      "Epoch 18/20, Batch 154/468\n",
      "Epoch 18/20, Batch 155/468\n",
      "Epoch 18/20, Batch 156/468\n",
      "Epoch 18/20, Batch 157/468\n",
      "Epoch 18/20, Batch 158/468\n",
      "Epoch 18/20, Batch 159/468\n",
      "Epoch 18/20, Batch 160/468\n",
      "Epoch 18/20, Batch 161/468\n",
      "Epoch 18/20, Batch 162/468\n",
      "Epoch 18/20, Batch 163/468\n",
      "Epoch 18/20, Batch 164/468\n",
      "Epoch 18/20, Batch 165/468\n",
      "Epoch 18/20, Batch 166/468\n",
      "Epoch 18/20, Batch 167/468\n",
      "Epoch 18/20, Batch 168/468\n",
      "Epoch 18/20, Batch 169/468\n",
      "Epoch 18/20, Batch 170/468\n",
      "Epoch 18/20, Batch 171/468\n",
      "Epoch 18/20, Batch 172/468\n",
      "Epoch 18/20, Batch 173/468\n",
      "Epoch 18/20, Batch 174/468\n",
      "Epoch 18/20, Batch 175/468\n",
      "Epoch 18/20, Batch 176/468\n",
      "Epoch 18/20, Batch 177/468\n",
      "Epoch 18/20, Batch 178/468\n",
      "Epoch 18/20, Batch 179/468\n",
      "Epoch 18/20, Batch 180/468\n",
      "Epoch 18/20, Batch 181/468\n",
      "Epoch 18/20, Batch 182/468\n",
      "Epoch 18/20, Batch 183/468\n",
      "Epoch 18/20, Batch 184/468\n",
      "Epoch 18/20, Batch 185/468\n",
      "Epoch 18/20, Batch 186/468\n",
      "Epoch 18/20, Batch 187/468\n",
      "Epoch 18/20, Batch 188/468\n",
      "Epoch 18/20, Batch 189/468\n",
      "Epoch 18/20, Batch 190/468\n",
      "Epoch 18/20, Batch 191/468\n",
      "Epoch 18/20, Batch 192/468\n",
      "Epoch 18/20, Batch 193/468\n",
      "Epoch 18/20, Batch 194/468\n",
      "Epoch 18/20, Batch 195/468\n",
      "Epoch 18/20, Batch 196/468\n",
      "Epoch 18/20, Batch 197/468\n",
      "Epoch 18/20, Batch 198/468\n",
      "Epoch 18/20, Batch 199/468\n",
      "Epoch 18/20, Batch 200/468\n",
      "Epoch 18/20, Batch 201/468\n",
      "Epoch 18/20, Batch 202/468\n",
      "Epoch 18/20, Batch 203/468\n",
      "Epoch 18/20, Batch 204/468\n",
      "Epoch 18/20, Batch 205/468\n",
      "Epoch 18/20, Batch 206/468\n",
      "Epoch 18/20, Batch 207/468\n",
      "Epoch 18/20, Batch 208/468\n",
      "Epoch 18/20, Batch 209/468\n",
      "Epoch 18/20, Batch 210/468\n",
      "Epoch 18/20, Batch 211/468\n",
      "Epoch 18/20, Batch 212/468\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/20, Batch 213/468\n",
      "Epoch 18/20, Batch 214/468\n",
      "Epoch 18/20, Batch 215/468\n",
      "Epoch 18/20, Batch 216/468\n",
      "Epoch 18/20, Batch 217/468\n",
      "Epoch 18/20, Batch 218/468\n",
      "Epoch 18/20, Batch 219/468\n",
      "Epoch 18/20, Batch 220/468\n",
      "Epoch 18/20, Batch 221/468\n",
      "Epoch 18/20, Batch 222/468\n",
      "Epoch 18/20, Batch 223/468\n",
      "Epoch 18/20, Batch 224/468\n",
      "Epoch 18/20, Batch 225/468\n",
      "Epoch 18/20, Batch 226/468\n",
      "Epoch 18/20, Batch 227/468\n",
      "Epoch 18/20, Batch 228/468\n",
      "Epoch 18/20, Batch 229/468\n",
      "Epoch 18/20, Batch 230/468\n",
      "Epoch 18/20, Batch 231/468\n",
      "Epoch 18/20, Batch 232/468\n",
      "Epoch 18/20, Batch 233/468\n",
      "Epoch 18/20, Batch 234/468\n",
      "Epoch 18/20, Batch 235/468\n",
      "Epoch 18/20, Batch 236/468\n",
      "Epoch 18/20, Batch 237/468\n",
      "Epoch 18/20, Batch 238/468\n",
      "Epoch 18/20, Batch 239/468\n",
      "Epoch 18/20, Batch 240/468\n",
      "Epoch 18/20, Batch 241/468\n",
      "Epoch 18/20, Batch 242/468\n",
      "Epoch 18/20, Batch 243/468\n",
      "Epoch 18/20, Batch 244/468\n",
      "Epoch 18/20, Batch 245/468\n",
      "Epoch 18/20, Batch 246/468\n",
      "Epoch 18/20, Batch 247/468\n",
      "Epoch 18/20, Batch 248/468\n",
      "Epoch 18/20, Batch 249/468\n",
      "Epoch 18/20, Batch 250/468\n",
      "Epoch 18/20, Batch 251/468\n",
      "Epoch 18/20, Batch 252/468\n",
      "Epoch 18/20, Batch 253/468\n",
      "Epoch 18/20, Batch 254/468\n",
      "Epoch 18/20, Batch 255/468\n",
      "Epoch 18/20, Batch 256/468\n",
      "Epoch 18/20, Batch 257/468\n",
      "Epoch 18/20, Batch 258/468\n",
      "Epoch 18/20, Batch 259/468\n",
      "Epoch 18/20, Batch 260/468\n",
      "Epoch 18/20, Batch 261/468\n",
      "Epoch 18/20, Batch 262/468\n",
      "Epoch 18/20, Batch 263/468\n",
      "Epoch 18/20, Batch 264/468\n",
      "Epoch 18/20, Batch 265/468\n",
      "Epoch 18/20, Batch 266/468\n",
      "Epoch 18/20, Batch 267/468\n",
      "Epoch 18/20, Batch 268/468\n",
      "Epoch 18/20, Batch 269/468\n",
      "Epoch 18/20, Batch 270/468\n",
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      "Epoch 18/20, Batch 273/468\n",
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      "Epoch 18/20, Batch 275/468\n",
      "Epoch 18/20, Batch 276/468\n",
      "Epoch 18/20, Batch 277/468\n",
      "Epoch 18/20, Batch 278/468\n",
      "Epoch 18/20, Batch 279/468\n",
      "Epoch 18/20, Batch 280/468\n",
      "Epoch 18/20, Batch 281/468\n",
      "Epoch 18/20, Batch 282/468\n",
      "Epoch 18/20, Batch 283/468\n",
      "Epoch 18/20, Batch 284/468\n",
      "Epoch 18/20, Batch 285/468\n",
      "Epoch 18/20, Batch 286/468\n",
      "Epoch 18/20, Batch 287/468\n",
      "Epoch 18/20, Batch 288/468\n",
      "Epoch 18/20, Batch 289/468\n",
      "Epoch 18/20, Batch 290/468\n",
      "Epoch 18/20, Batch 291/468\n",
      "Epoch 18/20, Batch 292/468\n",
      "Epoch 18/20, Batch 293/468\n",
      "Epoch 18/20, Batch 294/468\n",
      "Epoch 18/20, Batch 295/468\n",
      "Epoch 18/20, Batch 296/468\n",
      "Epoch 18/20, Batch 297/468\n",
      "Epoch 18/20, Batch 298/468\n",
      "Epoch 18/20, Batch 299/468\n",
      "Epoch 18/20, Batch 300/468\n",
      "Epoch 18/20, Batch 301/468\n",
      "Epoch 18/20, Batch 302/468\n",
      "Epoch 18/20, Batch 303/468\n",
      "Epoch 18/20, Batch 304/468\n",
      "Epoch 18/20, Batch 305/468\n",
      "Epoch 18/20, Batch 306/468\n",
      "Epoch 18/20, Batch 307/468\n",
      "Epoch 18/20, Batch 308/468\n",
      "Epoch 18/20, Batch 309/468\n",
      "Epoch 18/20, Batch 310/468\n",
      "Epoch 18/20, Batch 311/468\n",
      "Epoch 18/20, Batch 312/468\n",
      "Epoch 18/20, Batch 313/468\n",
      "Epoch 18/20, Batch 314/468\n",
      "Epoch 18/20, Batch 315/468\n",
      "Epoch 18/20, Batch 316/468\n",
      "Epoch 18/20, Batch 317/468\n",
      "Epoch 18/20, Batch 318/468\n",
      "Epoch 18/20, Batch 319/468\n",
      "Epoch 18/20, Batch 320/468\n",
      "Epoch 18/20, Batch 321/468\n",
      "Epoch 18/20, Batch 322/468\n",
      "Epoch 18/20, Batch 323/468\n",
      "Epoch 18/20, Batch 324/468\n",
      "Epoch 18/20, Batch 325/468\n",
      "Epoch 18/20, Batch 326/468\n",
      "Epoch 18/20, Batch 327/468\n",
      "Epoch 18/20, Batch 328/468\n",
      "Epoch 18/20, Batch 329/468\n",
      "Epoch 18/20, Batch 330/468\n",
      "Epoch 18/20, Batch 331/468\n",
      "Epoch 18/20, Batch 332/468\n",
      "Epoch 18/20, Batch 333/468\n",
      "Epoch 18/20, Batch 334/468\n",
      "Epoch 18/20, Batch 335/468\n",
      "Epoch 18/20, Batch 336/468\n",
      "Epoch 18/20, Batch 337/468\n",
      "Epoch 18/20, Batch 338/468\n",
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      "Epoch 18/20, Batch 340/468\n",
      "Epoch 18/20, Batch 341/468\n",
      "Epoch 18/20, Batch 342/468\n",
      "Epoch 18/20, Batch 343/468\n",
      "Epoch 18/20, Batch 344/468\n",
      "Epoch 18/20, Batch 345/468\n",
      "Epoch 18/20, Batch 346/468\n",
      "Epoch 18/20, Batch 347/468\n",
      "Epoch 18/20, Batch 348/468\n",
      "Epoch 18/20, Batch 349/468\n",
      "Epoch 18/20, Batch 350/468\n",
      "Epoch 18/20, Batch 351/468\n",
      "Epoch 18/20, Batch 352/468\n",
      "Epoch 18/20, Batch 353/468\n",
      "Epoch 18/20, Batch 354/468\n",
      "Epoch 18/20, Batch 355/468\n",
      "Epoch 18/20, Batch 356/468\n",
      "Epoch 18/20, Batch 357/468\n",
      "Epoch 18/20, Batch 358/468\n",
      "Epoch 18/20, Batch 359/468\n",
      "Epoch 18/20, Batch 360/468\n",
      "Epoch 18/20, Batch 361/468\n",
      "Epoch 18/20, Batch 362/468\n",
      "Epoch 18/20, Batch 363/468\n",
      "Epoch 18/20, Batch 364/468\n",
      "Epoch 18/20, Batch 365/468\n",
      "Epoch 18/20, Batch 366/468\n",
      "Epoch 18/20, Batch 367/468\n",
      "Epoch 18/20, Batch 368/468\n",
      "Epoch 18/20, Batch 369/468\n",
      "Epoch 18/20, Batch 370/468\n",
      "Epoch 18/20, Batch 371/468\n",
      "Epoch 18/20, Batch 372/468\n",
      "Epoch 18/20, Batch 373/468\n",
      "Epoch 18/20, Batch 374/468\n",
      "Epoch 18/20, Batch 375/468\n",
      "Epoch 18/20, Batch 376/468\n",
      "Epoch 18/20, Batch 377/468\n",
      "Epoch 18/20, Batch 378/468\n",
      "Epoch 18/20, Batch 379/468\n",
      "Epoch 18/20, Batch 380/468\n",
      "Epoch 18/20, Batch 381/468\n",
      "Epoch 18/20, Batch 382/468\n",
      "Epoch 18/20, Batch 383/468\n",
      "Epoch 18/20, Batch 384/468\n",
      "Epoch 18/20, Batch 385/468\n",
      "Epoch 18/20, Batch 386/468\n",
      "Epoch 18/20, Batch 387/468\n",
      "Epoch 18/20, Batch 388/468\n",
      "Epoch 18/20, Batch 389/468\n",
      "Epoch 18/20, Batch 390/468\n",
      "Epoch 18/20, Batch 391/468\n",
      "Epoch 18/20, Batch 392/468\n",
      "Epoch 18/20, Batch 393/468\n",
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      "Epoch 18/20, Batch 398/468\n",
      "Epoch 18/20, Batch 399/468\n",
      "Epoch 18/20, Batch 400/468\n",
      "Epoch 18/20, Batch 401/468\n",
      "Epoch 18/20, Batch 402/468\n",
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      "Epoch 18/20, Batch 404/468\n",
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      "Epoch 18/20, Batch 406/468\n",
      "Epoch 18/20, Batch 407/468\n",
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      "Epoch 18/20, Batch 409/468\n",
      "Epoch 18/20, Batch 410/468\n",
      "Epoch 18/20, Batch 411/468\n",
      "Epoch 18/20, Batch 412/468\n",
      "Epoch 18/20, Batch 413/468\n",
      "Epoch 18/20, Batch 414/468\n",
      "Epoch 18/20, Batch 415/468\n",
      "Epoch 18/20, Batch 416/468\n",
      "Epoch 18/20, Batch 417/468\n",
      "Epoch 18/20, Batch 418/468\n",
      "Epoch 18/20, Batch 419/468\n",
      "Epoch 18/20, Batch 420/468\n",
      "Epoch 18/20, Batch 421/468\n",
      "Epoch 18/20, Batch 422/468\n",
      "Epoch 18/20, Batch 423/468\n",
      "Epoch 18/20, Batch 424/468\n",
      "Epoch 18/20, Batch 425/468\n",
      "Epoch 18/20, Batch 426/468\n",
      "Epoch 18/20, Batch 427/468\n",
      "Epoch 18/20, Batch 428/468\n",
      "Epoch 18/20, Batch 429/468\n",
      "Epoch 18/20, Batch 430/468\n",
      "Epoch 18/20, Batch 431/468\n",
      "Epoch 18/20, Batch 432/468\n",
      "Epoch 18/20, Batch 433/468\n",
      "Epoch 18/20, Batch 434/468\n",
      "Epoch 18/20, Batch 435/468\n",
      "Epoch 18/20, Batch 436/468\n",
      "Epoch 18/20, Batch 437/468\n",
      "Epoch 18/20, Batch 438/468\n",
      "Epoch 18/20, Batch 439/468\n",
      "Epoch 18/20, Batch 440/468\n",
      "Epoch 18/20, Batch 441/468\n",
      "Epoch 18/20, Batch 442/468\n",
      "Epoch 18/20, Batch 443/468\n",
      "Epoch 18/20, Batch 444/468\n",
      "Epoch 18/20, Batch 445/468\n",
      "Epoch 18/20, Batch 446/468\n",
      "Epoch 18/20, Batch 447/468\n",
      "Epoch 18/20, Batch 448/468\n",
      "Epoch 18/20, Batch 449/468\n",
      "Epoch 18/20, Batch 450/468\n",
      "Epoch 18/20, Batch 451/468\n",
      "Epoch 18/20, Batch 452/468\n",
      "Epoch 18/20, Batch 453/468\n",
      "Epoch 18/20, Batch 454/468\n",
      "Epoch 18/20, Batch 455/468\n",
      "Epoch 18/20, Batch 456/468\n",
      "Epoch 18/20, Batch 457/468\n",
      "Epoch 18/20, Batch 458/468\n",
      "Epoch 18/20, Batch 459/468\n",
      "Epoch 18/20, Batch 460/468\n",
      "Epoch 18/20, Batch 461/468\n",
      "Epoch 18/20, Batch 462/468\n",
      "Epoch 18/20, Batch 463/468\n",
      "Epoch 18/20, Batch 464/468\n",
      "Epoch 18/20, Batch 465/468\n",
      "Epoch 18/20, Batch 466/468\n",
      "Epoch 18/20, Batch 467/468\n",
      "Epoch 18/20, Batch 468/468\n",
      "Epoch 18/20, Batch 469/468\n",
      "Epoch 19/20, Batch 1/468\n",
      "Epoch 19/20, Batch 2/468\n",
      "Epoch 19/20, Batch 3/468\n",
      "Epoch 19/20, Batch 4/468\n",
      "Epoch 19/20, Batch 5/468\n",
      "Epoch 19/20, Batch 6/468\n",
      "Epoch 19/20, Batch 7/468\n",
      "Epoch 19/20, Batch 8/468\n",
      "Epoch 19/20, Batch 9/468\n",
      "Epoch 19/20, Batch 10/468\n",
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      "Epoch 19/20, Batch 12/468\n",
      "Epoch 19/20, Batch 13/468\n",
      "Epoch 19/20, Batch 14/468\n",
      "Epoch 19/20, Batch 15/468\n",
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      "Epoch 19/20, Batch 35/468\n",
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      "Epoch 19/20, Batch 40/468\n",
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      "Epoch 19/20, Batch 44/468\n",
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      "Epoch 19/20, Batch 46/468\n",
      "Epoch 19/20, Batch 47/468\n",
      "Epoch 19/20, Batch 48/468\n",
      "Epoch 19/20, Batch 49/468\n",
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      "Epoch 19/20, Batch 52/468\n",
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      "Epoch 19/20, Batch 54/468\n",
      "Epoch 19/20, Batch 55/468\n",
      "Epoch 19/20, Batch 56/468\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/20, Batch 57/468\n",
      "Epoch 19/20, Batch 58/468\n",
      "Epoch 19/20, Batch 59/468\n",
      "Epoch 19/20, Batch 60/468\n",
      "Epoch 19/20, Batch 61/468\n",
      "Epoch 19/20, Batch 62/468\n",
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      "Epoch 19/20, Batch 67/468\n",
      "Epoch 19/20, Batch 68/468\n",
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      "10000/10000 [==============================] - 0s 21us/step\n",
      "Test loss: 0.07337765944781713\n",
      "Test accuracy: 0.977\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "MLP network for MNIST digits classification w/ data augment\n",
    "Test accuracy: 97.7\n",
    "'''\n",
    "\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "# numpy package\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# load mnist dataset\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "# compute the number of labels\n",
    "num_labels = np.amax(y_train) + 1\n",
    "# convert to one-hot vector\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n",
    "\n",
    "# image dimensions (assumed square)\n",
    "image_size = x_train.shape[1]\n",
    "input_size = image_size * image_size\n",
    "# we train our network using float data\n",
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = x_test.astype('float32') / 255\n",
    "\n",
    "# network parameters\n",
    "batch_size = 128\n",
    "hidden_units = 256\n",
    "data_augmentation = True\n",
    "epochs = 20\n",
    "max_batches = len(x_train) / batch_size\n",
    "\n",
    "# this is 3-layer MLP with ReLU after each layer\n",
    "model = Sequential()\n",
    "model.add(Dense(hidden_units, input_dim=input_size))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(hidden_units))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(num_labels))\n",
    "# this is the output for one-hot vector\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()\n",
    "\n",
    "# loss function for one-hot vector\n",
    "# use of sgd optimizer\n",
    "# accuracy is good metric for classification tasks\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# validate the model on test dataset to determine generalization\n",
    "# score = model.evaluate(x_test, y_test, batch_size=batch_size)\n",
    "# print(\"\\nTest accuracy: %.1f%%\" % (100.0 * score[1]))\n",
    "\n",
    "# Run training, with or without data augmentation.\n",
    "if not data_augmentation:\n",
    "    print('Not using data augmentation.')\n",
    "    # train the network no data augmentation\n",
    "    x_train = np.reshape(x_train, [-1, input_size])\n",
    "    model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size)\n",
    "else:\n",
    "    print('Using real-time data augmentation.')\n",
    "    # This will do preprocessing and realtime data augmentation:\n",
    "    # we need [width, height, channel] dim for data aug\n",
    "    x_train = np.reshape(x_train, [-1, image_size, image_size, 1])\n",
    "    datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=5.0,  # randomly rotate images in the range (deg 0 to 180)\n",
    "        width_shift_range=0.0,  # randomly shift images horizontally\n",
    "        height_shift_range=0.0,  # randomly shift images vertically\n",
    "        horizontal_flip=False,  # randomly flip images\n",
    "        vertical_flip=False)  # randomly flip images\n",
    "\n",
    "    # Compute quantities required for featurewise normalization\n",
    "    # (std, mean, and principal components if ZCA whitening is applied).\n",
    "    datagen.fit(x_train)\n",
    "    for e in range(epochs):\n",
    "        batches = 0\n",
    "        for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=batch_size):\n",
    "            x_batch = np.reshape(x_batch, [-1, input_size])\n",
    "            model.fit(x_batch, y_batch, verbose=0)\n",
    "            batches += 1\n",
    "            print(\"Epoch %d/%d, Batch %d/%d\" % (e+1, epochs, batches, max_batches))\n",
    "            if batches >= max_batches:\n",
    "                # we need to break the loop by hand because\n",
    "                # the generator loops indefinitely\n",
    "                break\n",
    "\n",
    "# Score trained model.\n",
    "x_test = np.reshape(x_test, [-1, input_size])\n",
    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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