ft_multi_mnist_test.ipynb

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   "source": [
    "#Choose GPUs and CPUs\n",
    "\n",
    "#If want to choose GPU to use\n",
    "#import os\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1,4\"\n",
    "#This will make it so that only the 0th GPU is visible to TensorFlow.\n",
    "#In your case, you can choose any in the range [0, 3].\n",
    "#If you wanted, for example, the 0th and 2nd GPUs to both be visible, replace \"0\" with \"0,2\"\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.datasets import mnist\n",
    "\n",
    "\n",
    "GPU=True\n",
    "CPU=False\n",
    "num_cores = 8\n",
    "\n",
    "if GPU:\n",
    "    num_GPU = 1\n",
    "    num_CPU = 2\n",
    "if CPU:\n",
    "    num_CPU = num_cores\n",
    "    num_GPU = 0\n",
    "\n",
    "config = tf.ConfigProto(intra_op_parallelism_threads=num_cores,\\\n",
    "        inter_op_parallelism_threads=num_cores, allow_soft_placement=True,\\\n",
    "        device_count = {'CPU' : num_CPU, 'GPU' : num_GPU},\\\n",
    "#        gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.5))\n",
    "                       )\n",
    "config.gpu_options.allow_growth = True\n",
    "\n",
    "session = tf.Session(config=config)\n",
    "K.set_session(session)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "\n",
    "from keras.layers import Conv2D, MaxPool2D\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "from tqdm import tqdm_notebook, tnrange\n",
    "\n",
    "import cv2\n",
    "from scipy.fftpack import fftn,fftshift\n",
    "\n",
    "\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load MNIST data from Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_dataset shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "num_classes=10\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "train_dataset = x_train.astype('float32')\n",
    "test_dataset = x_test.astype('float32')\n",
    "train_dataset /= 255\n",
    "test_dataset /= 255\n",
    "print('train_dataset shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "train_labels = keras.utils.to_categorical(y_train, num_classes)\n",
    "test_labels = keras.utils.to_categorical(y_test, num_classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pad numpy array \n",
    "Want get 36x36 which is how much the multiMNIST will be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 36, 36, 1)\n",
      "(10000, 36, 36, 1)\n"
     ]
    }
   ],
   "source": [
    "train_dataset=np.pad(train_dataset, ((0,0),(4,4),(4,4),(0,0)), 'constant', constant_values=(0,0))\n",
    "print (train_dataset.shape)\n",
    "\n",
    "test_dataset=np.pad(test_dataset, ((0,0),(4,4),(4,4),(0,0)), 'constant', constant_values=(0,0))\n",
    "print (test_dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Augment the data with rotations and translations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing rotations\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Doing displacements\n",
      "1.0000302009116304 5.9999829486631056\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Doing displacements and rotations\n",
      "1.000036701337498 5.999747238964373\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "aug_data=[]\n",
    "aug_labels=[]\n",
    "cols, rows=36,36\n",
    "\n",
    "dims=train_dataset.shape\n",
    "#Rotations, 1 each\n",
    "ang = (np.random.rand(dims[0]))*30+5\n",
    "print (\"Doing rotations\")\n",
    "for i in tqdm_notebook(range(dims[0])):\n",
    "    M = cv2.getRotationMatrix2D((cols/2,rows/2),ang[i],1)\n",
    "    tmp = cv2.warpAffine(train_dataset[i-1],M,(cols,rows))\n",
    "    aug_data.append(tmp)\n",
    "    aug_labels.append(train_labels[i])\n",
    "\n",
    "\n",
    "print (\"Doing displacements\")\n",
    "rows,cols = dims[1],dims[2]\n",
    "\n",
    "disps = (np.random.rand(dims[0],2))*5+1 # At least 1 pixel\n",
    "print (min(disps[:,0]), max(disps[:,0]))\n",
    "for i in tqdm_notebook(range(dims[0])):\n",
    "    M = np.float32([[1,0,disps[i,0]],[0,1,disps[i,1]]])\n",
    "    tmp = cv2.warpAffine(train_dataset[i],M,(cols,rows))\n",
    "    aug_data.append(tmp)\n",
    "    aug_labels.append(train_labels[i])\n",
    "   \n",
    "\n",
    "print (\"Doing displacements and rotations\")\n",
    "#Both displace and rotate\n",
    "ang = (np.random.rand(dims[0]))*30+5\n",
    "disps = (np.random.rand(dims[0],2))*5+1 # At least 1 pixel\n",
    "print (min(disps[:,0]), max(disps[:,0]))\n",
    "for i in tqdm_notebook(range(dims[0])):\n",
    "    M = np.float32([[1,0,disps[i,0]],[0,1,disps[i,1]]])\n",
    "    tmp = cv2.warpAffine(train_dataset[i],M,(cols,rows))\n",
    "    M = cv2.getRotationMatrix2D((cols/2,rows/2),ang[i],1)\n",
    "    tmp = cv2.warpAffine(tmp,M,(cols,rows))\n",
    "    aug_data.append(tmp)\n",
    "    aug_labels.append(train_labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(180000, 36, 36)\n"
     ]
    }
   ],
   "source": [
    "aug_data=np.asarray(aug_data)\n",
    "aug_labels=np.asarray(aug_labels)\n",
    "print (aug_data.shape)\n",
    "aug_data=np.reshape(aug_data,(aug_data.shape[0],36,36,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(240000, 36, 36, 1) (240000, 10)\n"
     ]
    }
   ],
   "source": [
    "all_data=np.concatenate([train_dataset,aug_data],axis=0)\n",
    "all_labels=np.concatenate([train_labels,aug_labels],axis=0)\n",
    "print (all_data.shape, all_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FT the training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(240000, 36, 36, 1)\n",
      "(10000, 36, 36, 1)\n"
     ]
    }
   ],
   "source": [
    "dims=all_data.shape\n",
    "ft_train=[]\n",
    "for i in tnrange(dims[0]):\n",
    "    ft_train.append(np.abs(fftshift(fftn(all_data[i,:,:]))))\n",
    "\n",
    "dims=test_dataset.shape\n",
    "ft_test=[]\n",
    "for i in tqdm_notebook(range(dims[0])):\n",
    "    ft_test.append(np.abs(fftshift(fftn(test_dataset[i,:,:])))) #Don't forget to take abs!\n",
    "\n",
    "ft_train=np.asarray(ft_train)\n",
    "ft_test=np.asarray(ft_test)\n",
    "print (ft_train.shape)\n",
    "print (ft_test.shape)"
   ]
  },
  {
   "cell_type": "code",
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       "<matplotlib.colorbar.Colorbar at 0x143708f90>"
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      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
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\n",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=200000\n",
    "plt.viridis()\n",
    "f,axarr=plt.subplots(1,2,figsize=(8, 6))\n",
    "axarr[0].imshow(all_data[i].squeeze())\n",
    "img=axarr[1].imshow(ft_train[i].squeeze())\n",
    "f.colorbar(img, fraction=0.046, pad=0.04)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# O.K now try CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(240000, 36, 36, 1)\n",
      "(240000, 36, 36, 1)\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_33 (Conv2D)           (None, 34, 34, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_34 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_35 (Conv2D)           (None, 14, 14, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_36 (Conv2D)           (None, 12, 12, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_18 (MaxPooling (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_9 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1200)              2766000   \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 1200)              0         \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 10)                12010     \n",
      "=================================================================\n",
      "Total params: 2,843,002\n",
      "Trainable params: 2,843,002\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 216000 samples, validate on 24000 samples\n",
      "Epoch 1/10\n",
      "215552/216000 [============================>.] - ETA: 0s - loss: 1.2064 - acc: 0.6603Epoch 00000: saving model to tmp_models/weights.00.hdf5\n",
      "216000/216000 [==============================] - 19s - loss: 1.2059 - acc: 0.6604 - val_loss: 0.3892 - val_acc: 0.9130\n",
      "Epoch 2/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.9283 - acc: 0.7041Epoch 00001: saving model to tmp_models/weights.01.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.9282 - acc: 0.7041 - val_loss: 0.4854 - val_acc: 0.9263\n",
      "Epoch 3/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.8775 - acc: 0.7122Epoch 00002: saving model to tmp_models/weights.02.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.8776 - acc: 0.7122 - val_loss: 0.3677 - val_acc: 0.9292\n",
      "Epoch 4/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.8509 - acc: 0.7176Epoch 00003: saving model to tmp_models/weights.03.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.8511 - acc: 0.7175 - val_loss: 0.2932 - val_acc: 0.9397\n",
      "Epoch 5/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.8312 - acc: 0.7209Epoch 00004: saving model to tmp_models/weights.04.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.8315 - acc: 0.7208 - val_loss: 0.3764 - val_acc: 0.9294\n",
      "Epoch 6/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.8152 - acc: 0.7237Epoch 00005: saving model to tmp_models/weights.05.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.8153 - acc: 0.7237 - val_loss: 0.2988 - val_acc: 0.9383\n",
      "Epoch 7/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.8021 - acc: 0.7269Epoch 00006: saving model to tmp_models/weights.06.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.8020 - acc: 0.7270 - val_loss: 0.3133 - val_acc: 0.9359\n",
      "Epoch 8/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.7904 - acc: 0.7292Epoch 00007: saving model to tmp_models/weights.07.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.7905 - acc: 0.7291 - val_loss: 0.3472 - val_acc: 0.9276\n",
      "Epoch 9/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.7732 - acc: 0.7330Epoch 00008: saving model to tmp_models/weights.08.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.7732 - acc: 0.7330 - val_loss: 0.2699 - val_acc: 0.9402\n",
      "Epoch 10/10\n",
      "215296/216000 [============================>.] - ETA: 0s - loss: 0.7619 - acc: 0.7361Epoch 00009: saving model to tmp_models/weights.09.hdf5\n",
      "216000/216000 [==============================] - 17s - loss: 0.7619 - acc: 0.7361 - val_loss: 0.2863 - val_acc: 0.9390\n"
     ]
    }
   ],
   "source": [
    "dims=ft_train.shape\n",
    "print (dims)\n",
    "trainee=ft_train.reshape(dims[0],dims[1],dims[2],1)\n",
    "dims=test_dataset.shape\n",
    "testee=ft_test.reshape(dims[0],dims[1],dims[2],1)\n",
    "print (trainee.shape)\n",
    "\n",
    "\n",
    "np.random.seed(123)\n",
    "model=Sequential()\n",
    "\n",
    "model.add(Conv2D(32,(3,3), activation='relu', input_shape=(36,36,1),data_format='channels_last'))\n",
    "model.add(Conv2D(32,(3,3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(64,(3,3), activation='relu'))\n",
    "model.add(Conv2D(64,(3,3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1200, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "#parallel_model = ModelMGPU(model, gpus=num_GPU)\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "print (model.summary())\n",
    "#print (parallel_model.summary())\n",
    "\n",
    "\n",
    "\n",
    "checkpoints=keras.callbacks.ModelCheckpoint('tmp_models/weights.{epoch:02d}.hdf5', monitor='val_loss', \n",
    "                                verbose=1, save_best_only=False, save_weights_only=False, mode='auto', period=1)\n",
    "\n",
    "history=model.fit(trainee, all_labels, batch_size=256, verbose=1, epochs=10, validation_split = 0.1, callbacks=[checkpoints])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=history\n",
    "epochs=np.asarray(history.epoch)+1\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "plt.style.use('seaborn-white')\n",
    "matplotlib.rc('font',family='Times New Roman')\n",
    "matplotlib.rcParams['font.size'] = 20\n",
    "\n",
    "f, axarr = plt.subplots(2, sharex=True, figsize=(8, 6))\n",
    "\n",
    "axarr[0].set(ylabel='Loss')\n",
    "axarr[0].plot(epochs,hist.history['loss'], 'C3o', label='Training')\n",
    "axarr[0].plot(epochs,hist.history['val_loss'], 'C3-', label='Validation')\n",
    "axarr[0].grid()\n",
    "axarr[0].legend(loc='center right', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "\n",
    "axarr[1].plot(epochs,hist.history['acc'], 'C0o', label='Training')\n",
    "axarr[1].plot(epochs,hist.history['val_acc'], 'C0-', label='Validation')\n",
    "plt.legend(loc='center right', bbox_to_anchor=(1.0, 0.5))\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "plt.tick_params(which='both', width=2)\n",
    "plt.tick_params(which='major', length=7)\n",
    "plt.tick_params(which='minor', length=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9600/10000 [===========================>..] - ETA: 0s[0.1481072497110581, 0.9609]\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, load_model\n",
    "model=load_model('tmp_models/weights.08.hdf5')\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "score=model.evaluate(testee, test_labels, verbose=1)\n",
    "print (score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds=(model.predict(testee))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": 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PkNWP/dhvJHcykglUUgIkZ6VeScraTFMPWklakoau54llR8phRlmexM1eOo22M+nW4+Pue/o/+CVD43aeNnYz/YQ0AAzU+yU/k1Keso5ybMN8aEmKz634UpJ6JqVIAcBVGClkckzTx3po32iYDRrIVfvPO93Pb/npuo3kNElZW6l6ltSOKoxyq1bZV9afjeEc18C6QhYREQmAFmQREZEAaEEWEREJgBZkERGRAGhBFhERCYBS1gFJzb3Ca1tb9zTt+z//4QZjKxOTso6NG5Lve2SB1/b2TX9P+/5zn1/bFQAODPp1cV/smE/7blz6JG3/z9n7vLaqJ35E+8oEGRwCktGf8dnN35HyE8gAEBn1qVmi2pXz5HR8wk/dJjU8oYuEx5DTh4/5jUbq1pUadag7/WTxqU820L4Dtfx4VLT7CfXK3bxWvSN1rzGtivbNTuftA/X+HC/95MdpX+z/NW2OSv257EjiHADivgE+vjqSLp/GX8Oo9xQfH6mXHpUY6X7yawyrb0TqbJ9+wDh386QrZBERkQBoQRYREQmAFmQREZEAaEEWEREJgEJdF6iKDl527gOLeQDjncfm0va3f3eL19b4yH+lfRse/iltT/r6SCsvGdr0lftp+1/8TbPXtu2HvIRnroOXA5QPxg30I8mODhfF06r9jkaYCsZN4V0ZCegMZfk2SOlMq9xhur2Lb4OUUhyaTcpKAkj18nKfvddc4bWdupTPrapW/lwyXYP+Nn6bB8Nypf5zTA2SYwGg4u1O2h6TYzpczV+TlOPbjsjxz9bxQFb6F+/wbddUem0uZYSm+vp5OwkOOlbGFUBc7ofIHAmFAbDLvg77247YeRdlAWPTgK6QRUREgqAFWUREJABakEVERAKgBVlERCQAWpBFREQCoJR1QPo+xm9UzlT/nwO0/YNmr9/+TiNt/6ff+ifavvie/+K1zXrqddo3ISXq8jX9qT20vf6vyE3Qq42SiUpZT4xUGtHZbykR+cw/zJPJkVGeFVk/uRv1GCUTSQnDaJCna3NHePnH5Npr/La0cWP6DH8LHaj2+2e6+flf3sZLSJ6a7aeNp/6E//rgyM2zvbbU53ia+tARvxQpAMz9rp+yTg3yd5RoKi+/6XL+cxyu5mVO03U8uZ6QVH2ujCfUy2qm8W30+CVUTQV4X0LWP3aOJb2jsd+hdYUsIiISAC3IIiIiAdCCLCIiEgAtyCIiIgFQqCsgfTMm9+VIX3m51/b3n95B+/63+/+Etk95+scFHdO5JCd5WOO7xz/ttbV99lLat/7tgwUdk+SH3rsXoMEYAAApeehOGaGuEj9AFOV4kCau4+GmoSo/VBQP8TGnTvnlLQGgvNMPqFUe6qV9rdKeEQkb9f67y2jf+bft89q+e+UPad+m2n9P23/93Ee9Nme8JcVDPNTFSpdmK/jzYyVRASAp8ftbpTOdcc7EU0j5zSw/D9wACdVNqaB9LeycjliJ2HjsAJmukEVERAKgBVlERCQAWpBFREQCoAVZREQkAFqQRUREAqCUdUBSQ+Mv4Zb96Edoe5RHWci3/9jfxmfKeHq16n+9RduNvGwQhquMm5rL5IlJKctUntcBJIVsldlkJRNZCUMAiI3yjxEp/xgP8zM9GuBlOdN9JHVrlKGM+/icK8v442YJZAD48Vt+Qvq3T/BSvLl/4e0z33rHa+tZyN9nrNRz1/yp/v545UyUV5aOe9vpk/w4J8dP0Pb4Y1d6bdHJPj6QjJ/2dqQNAKK0sWRavxDIk66QRUREAqAFWUREJABakEVERAIwru+QnXN44oknsGPHDhw+fBiXXHIJli9fjpUrVyJ63+3O+vr68OCDD2Lv3r1wzmHx4sVYvXo1UsZ3OCIyuTSXRcI1rivkRx99FG+++Sb++q//Glu2bMEVV1yB9evXY/369SN9hoaGsGrVKvT39+PJJ5/E448/jpaWFnzta1+bsMGLSH40l0XCdc4r5KGhIZw4cQLf+MY3RtquvfZafPGLX8R3vvMd3HHHHaivr8f27dvx5ptvYtOmTYiiCCUlJVi9ejVWrFiBpUuXYtGiRRP6RC4GNc//wmvb9Tf8JXr7T/mVysdeH//+Lv2Rn/qsWMUjkd1/2Ejbq5740fh3WAARqVUMAJeXHffa/rWrADcev4hM+FzOZeGyo8/LmKRSnVFbOi/R+BP0UUU5bU86u2h7SVfN+MdxvJNvo26K32jVYybJcADIXVHnb7eb1F0GcNVDPV5b30f588iV8uN/aoFf+73yHZ4A75vFaz0PVfvPsbLNSJcP8WSyy/jXiSVHeZoaM3lt76TUT0lHPfz9wE0lda/TxrUqqS9+eoDkfcmRZD5re59zXiH39vbijjvuGNWWSqVw4403IkkSHDlyBADw2GOP4aqrrkJt7W8Kti9cuBCZTAbNzc3n2o2ITDDNZZGwnfMK+f2T8v3Ky8sRxzFmz56N/fv34+jRo1i4cOGoPplMBrNmzUJLSwucc6O+oxKRyaW5LBK2805Zv/HGG1i8eDHq6urQ2toKAKivr/f6VVVVobu7Gz09/p9URKT4NJdFwnBelbqOHDmCl19+GU8//TQAoLu7GwBQRqrnpN/7DmlgYADV1dXnO04RmQCayyLhOK8F+YEHHsCaNWswd+5cAL+ZvIOD/s26B967+fO0adPOd4wfGjlSBu6FngW07//4vUdp+9+UfNprc8NDtG/Zu35AZNjxAEYSSJHVX/3Vb9P26ysf8dr+7z/PoX0LU+Tu4lDIuRyVlSNOzlrIS/wTJyI3sQcA19/Ptzvkl010xjYiUvIwKuMlGmGFy0jpzGw130aq2i8VCQDp471eW1LJy31GEf9DZbrPf94DM3iYKpPxj3PFL/ygIwCgi4fIWEnT/gWzaN+hSj7m6v3+mCt3H6Z9XQ0/dqlOEiQb5O9h2YZLaHvMSpqSEqwA4Mixi/r5/tDPQ3UoJ69tPxvD2O8+ef/JevPmzZg+fTpWrFgx0tbQ0AAA6OryU4tdXV2ora1FaakxKUSkKDSXRcKS14L83HPP4Wc/+xnWrVs3qn3evHmoq6vDgQMHRrUPDg6ira1NP3kSCYzmskh4xr0gv/DCC3jmmWfw0EMPjXyXBADHjh1DFEVoamrCW2+9NeqTdUtLC3K5HJqamgo7ahE5b5rLImEa14K8c+dOPPzww7jnnnvwzjvvYP/+/fjlL3+JF198Ed/61rcQRRHuvPNOzJkzB1u3bgVwuvTexo0b0dTUhGuvvXZCn4SIjI/mski4zhnVefbZZ7F27VokSYJbb73Ve3zDhg0ATodBtm/fjnXr1uG2225DkiRYsmQJVq5cWfBBi0j+NJdFwhY5Z9UCm1itra244YYb8JGD85HOGnewFnT86XW0/fW/+jvaftWLf+K1fWzFT8a9v4OPf4K2//3v7KDtX915h9eW6s8vK3jZa37ataeBf1b83/f/d9r++T9f47VVfO/HeY0jVNn0EI5euQ8vvfQSZs3iqddiOjOXZ/UsQkkyukxlVEnKVg74CW4ASE7xG8jHU6v8RiMhnZz00830/wNwxjiiar9/9lKeLI97eRo37vPTuNlLjFRxD0/uJvsP+Y2N82jfgel+ytcZpTphNDtSjTce5MtDWQdPxKeOvOs3lhrv79nxl1DNzvLLiI4l3eaHEl3auDEKS1938FKdLstT0hF5jq7PP0bDqQEcufSn5lzW7RdFREQCoAVZREQkAFqQRUREAqAFWUREJABakEVERAIQSIVisdT/w+u0/ff+8D/R9t03fNtr+61/+HPa9+r1bV7b3Dvepn3/4j/eSdsrp5PIppHizPllhgEA79zspy1//6qf077LVtxF2yt+eHEkqi9ow8NAMjrJ6k6SJO0wT6paKWuQutVRhid3HanBDfCUNUtTA4AjSe1UOS8XmpQZCWJSezn9q3be16i1zW5xGbd10r6ZzHSvLVvJ397jYV4HPNVPXpeEp6yTEp5Yjmb4t/iM3+2mfVmdcwDI1U7xtzvIE9mpY/x4sPMgKuFvQEkPqe1t/PgoqiC/GgAAI32dL10hi4iIBEALsoiISAC0IIuIiARAC7KIiEgAFOq6QNV+gd/0+7f+7m6vbe9/eIT2feEGP4CxZhe/m0/miDUSP/zw+3/wU9pz08xXafvtBz/ntbWu/Sjtm37536yBSJG5/gEk2bPOB+cHiOKKCvr/U1P9MA8A5HpIyCplhIpI2MsskWmUdKTbyPEgVFJuvIVe5s+tlBHIskpIRrM/4jcO8FKdqZ/+0m8zyovGl15C25Np/vG3ym+mungALzp5yt+G8bq6Mh6ySp3wX+/k1zwQx18V0HKYuX5eDjNVTUqapo3XdWiYNrOSmo4ef3PEAHSFLCIiEgQtyCIiIgHQgiwiIhIALcgiIiIB0IIsIiISAKWsL1C8RCAw709avLalf/BntO+vlvkJyts/xUtQ/tlnX6Ptd7x9m9f24mufoH2vf6WRtld+7w2vLU6O075yYXFG6UUq4tcHkVFikfYt9ctQ5rr8m9UDQCplXI+QBLc74peZBYCSQT9NDQDOKIfJZI/ybadqp/mNlTyhHtf4fR0rCQkge5j/ZCI+7m87LiujfaMpfBxuGilHaiTUozY+x1liOZ5Wzfv29dN2xP57W5ocI2t/rHwqAFrG1cRS1hFPvp+hK2QREZEAaEEWEREJgBZkERGRAGhBFhERCYAWZBERkQAoZf0hkHneTzEDwLzn/bZ/Mz6j/TE+Y2zdT2x+lLTJh0AU+enWrJ9KTfqMOsgkIX16s35iNtfr10wGgDhD6iMb6W0z/Vvu34Q+mpZfbWNU+Onk3Aye8k1V81rPOObXXk7e5fWY49oav/EyXrM6tlLrpG61c0ZKvp/X1I56SerZ2J9V4zoi9bqTrm7e16pHPqXSbxz209QA4Lp7/EZSCxsATeADQHLKPx+jEjI261w8s9sxHxUREZFJoQVZREQkAFqQRUREAqAFWUREJAAKdYlIQbhsFi47OvQSk6BWYgShIiNAxEJWsRG6cSS4ExtlLHOdnbQ9JqUUI6OEJEhfAECbX9o2xYJGAJDmQSGQ0p6Jsb/khP9con4+5riEBN8AO8jEWEGtKlJSk4TyACDq4cE8FrKKzWNnjKN/wG8zgoAsuBYZJTKtczeu4KVEPSnjfDmznfFtRURERCaSFmQREZEAaEEWEREJgBZkERGRAGhBFhERCYBS1iJSEFE6jSg66y2FJHojo2QlS0gDoOUKzb7GuJi4qor/B5awJTe8B4DIKP+IrH8jemeUDLWSwlGln9yNrXKTJP1rpYqz7R20naXRo3IrXW4sHaf80pluwE88A6ApcoA/b2eknsHKXgIASfdHVhL65El/f0biP1U9lW8j57/etERs2ii1+h5dIYuIiARAC7KIiEgAtCCLiIgEQAuyiIhIALQgi4iIBEApaxEpjChCdHbd4uGxU6Xv50hSFQDQ7yd3reQ0q8eckLrGAOCy4x9bNOjXpgbspHbE6mfHxs3tu7r5ttm4jdQzzwRzqdppefQ2WDW8CfO1MrgBcqyNlLt1POh2+/zz6PS2/XMmrvDrpwMAjF8IWOdYvnSFLCIiEgAtyCIiIgEY998Snn32WWzZsgWtra2YOXMmvvrVr+KP/uiPRvXp6+vDgw8+iL1798I5h8WLF2P16tVIkR/2i0jxaD6LhGdcV8jPPPMMfv7zn+Mb3/gGNmzYgGw2i7Vr1+Kll14a6TM0NIRVq1ahv78fTz75JB5//HG0tLTga1/72oQNXkTyp/ksEqZxXSFXVlZi7dq1I/+eMWMGvvCFL+C1117DDTfcAADYvn073nzzTWzatAlRFKGkpASrV6/GihUrsHTpUixatGhinoGI5GWi5rMbGkJydtYn8j/zR8bN7SPwK283POQ3Glfp9MbyjgdxrJvK03KRRilFJEY7CQpFlRnaNVXK290pv/SiM0pFRhl/G6wEJQCzVCd9jkbQzixlmUfYCxm/rCpghMCscRhlORNSwjMySnWy8q5ukJxzOH2O8weM45GncV0hf+5znxv177lz5wIAGhsbR9oee+wxXHXVVaitrR1pW7hwITKZDJqbmwsxVhEpAM1nkTCdV6jrtddew4033ojPf/7zAID9+/fj6NHcPp5+AAAJnklEQVSjuPLKK0f1y2QymDVrFlpaWsxi3SJSXJrPImHIe0F++eWX8cADD2Dp0qUjvzlsbW0FANTX13v9q6qq0N3djZ4e464cIlI0ms8i4Rj3gpwkCbZv345Nmzahra0Nd911F7Zu3QoA6O4+/cP2sjL/u5f0e98HDFi34BKRSaf5LBKecS/IcRxjxYoVeOKJJ7Bt2zaUl5dj48aN6OnpGZm4g6SazZmJO21aAarDiEhBaD6LhOe8Smd+5jOfwZe+9CVs3boVv/rVr9DQ0AAA6Orq8vp2dXWhtrYWpeSG0SJSfIWaz1E6jQg8OTuKlUy2kJKTzihl6c4u3QnYCWkrqT2UR7lPI3XrWLlPVk4TsFPPpD0q5yUd2f5yvaf4dg0ROR6RlYQmqW4AAEsyp43frRvnAUtOs8Q5ADPLQMdt9GWvoVXu0ywDSs47x8pskl8dvN95V+q69tprAZz+pDxv3jzU1dXhwIEDo/oMDg6ira1NP3kSCZzms0jxnfeCfOTIEXziE59AQ0MD4jhGU1MT3nrrrVGfqltaWpDL5dDU1FSQwYrIxNB8Fim+cy7Ivb29ePDBB/Hiiy+O/Hlg//79ePrpp/HNb35zpN+dd96JOXPmjARD+vr6sHHjRjQ1NY18+haR4tJ8FgnXOb9DHhoawk9+8hPs2LEDl112GRYsWIBZs2Zhy5YtqKurG+lXVlaG7du3Y926dbjtttuQJAmWLFmClStXTuT4RSQPms8i4TrnglxbW4vvfve749pYXV0dHn744Q88KBGZGJM9n2nZSxa8GgsLwljbYMEd4z7EbpiXeUxI2Cg27rsbTakc9zicERaLwMcRkZ+dodQo90nu3xtZZSyNdjY+K7Rm3f83IvctdkZ4i/W1WNuIjdAZZYT4YhLUsu7Nbb2G7H7NtFQnKak66uExHxUREZFJoQVZREQkAFqQRUREAqAFWUREJABakEVERAJwXqUzRUQ8UeSlTSNWMtdKsBrt7ObvrMzj6QfyuMbI46byzkgmm0lhNg7j+SWk7CXAS3hapSxpgthKohvtUalfDtMsFWkcO1YuMrJeV0tCtm0lxg1RyfiXNjfgl2FNrES89XqTp+jorwPGPud0hSwiIhIALcgiIiIB0IIsIiISAC3IIiIiAdCCLCIiEgClrEWkMFIxIoxO+7J60Waa2kpI55GGZjWFYdRBNmssk8CyWdu4n7dHLMls1DG2ksysjnRCEsEAr7UdZfzU9OnOedQSzyNVDBjP23p+1nnAjpNxbrBUNwCApaRZehs8UW2m+A3sucQZ1bIWERG5IGlBFhERCYAWZBERkQBoQRYREQmAQl0iUhjDw3DZ0Z/xacnJOL/ADBwPX31QVnCH3VjeWWOwymGy522WsjSuixKybWMbjgSTWNuYSIAr33ATHZ8VZDKOaV5BQHaMxuo/QVhJTRo4U+lMERGR8GlBFhERCYAWZBERkQBoQRYREQlA0UJdufe+dM+m/Wo0IvIbZ+ZIbpKDKuM1MpdT/lx2rKTTOaoVeUiFpShtVXkiIaQ4z1AYG58V6oqMUBF73nnen5hWlrLuZZzOo5qZhW2bBNzy3ob5elv3v/bbnXGcrepbeZ9jZ8k7zMaqyZGw3pk5Ys3loi3IHR0dAIBjsw8UawgiF5SOjg5cfvnlxR6G58xcbv/IL4s8EpELgzWXI2fm+SfWwMAA9uzZg/r6eqTy/TQi8iGSy+XQ0dGBBQsWoKzMr1lcbJrLIuNzrrlctAVZREREfkOhLhERkQBoQRYREQmAFmQREZEAFLWWdS6Xw8aNG7Fr1y7EcYzGxkbcd999KC8vL+awhHDO4YknnsCOHTtw+PBhXHLJJVi+fDlWrlw56qbkfX19ePDBB7F3714457B48WKsXr1aYZ8PAc3nC4PmcriKGuq6++670dPTgy1btiCTyeDee+9FZ2cntm3bNurEkOLbunUrDhw4gFtvvRXZbBaPPvoodu3ahZUrV+Iv//IvAQBDQ0P48pe/jMsvvxzr169HNpvFqlWr0NDQgK9//etFfgYy0TSfLwyaywFzRfKDH/zAzZs3z+3Zs2ek7fDhw27evHnuySefLNawhBgcHHTr168f1ZbNZt0tt9zi5s+f744dO+acc27Lli1u/vz57vjx4yP9Xn/9dTdv3jz36quvTuqYZXJpPl8YNJfDVrTvkJubm1FTU4Orr756pG327NmYOXMmmpubizUsIXp7e3HHHXeMakulUrjxxhuRJAmOHDkCAHjsscdw1VVXoba2dqTfwoULkclk9Jpe5DSfLwyay2EryoLc29uL3bt344orrvD+lDVnzhzs27cPPT09xRiaELW1tairq/Pay8vLEccxZs+ejf379+Po0aO48sorR/XJZDKYNWsWWlpa7HvKygVN8/nCobkctqIsyO3t7cjlcqivr/ceq6qqgnMOra2tRRiZ5OONN97A4sWLUVdXN/J6Wa9pd3e33pQvUprPFz7N5TAUJWXd1dUFALR0WDp9ekgDAwOTOibJz5EjR/Dyyy/j6aefBgB0d3cDOPdrWl1dPXmDlEmh+Xxh01wOR1GukM+80IODg95jZ9r0YoftgQcewJo1azB37lwAY7+mZ96Mp02bNnkDlEmj+Xxh01wOR1EW5IaGBgBAZ2en91hnZydSqRRmzJgx2cOScdq8eTOmT5+OFStWjLSdeU3PXC29X1dXF2pra1FaWjppY5TJo/l84dJcDktRFuSqqipcc801OHjwoPfYoUOH0NjYiClTphRhZHIuzz33HH72s59h3bp1o9rnzZuHuro6HDgw+naag4ODaGtrw6JFiyZzmDKJNJ8vTJrL4Snaz56WL1+Ojo4O7Nu3b6Tt4MGDaG9vR1NTU7GGJWN44YUX8Mwzz+Chhx4a+S4JAI4dO4YoitDU1IS33npr1CfrlpYW5HI5vaYXOc3nC4vmcpiKVqkrSRKsWrUKNTU12LBhA3K5HNasWYPBwUFs3rxZlX0Cs3PnTnz729/GN7/5zZFSiEmS4NChQ/jhD3+Iv/3bv8XAwACWLVuG66+/Hvfffz/6+vrwla98BfPnz8cDDzxQ5GcgE0nz+cKhuRyuopbO7Ovrw/r167F3717EcYxFixZh9erVyGQyxRqSEM8++yzWrl2LJEno4xs2bMDSpUsBAMePH8e6devQ1taGJEmwZMkSr0auXJw0n8OnuRy2oi7IIiIicppuvygiIhIALcgiIiIB0IIsIiISAC3IIiIiAdCCLCIiEgAtyCIiIgHQgiwiIhIALcgiIiIB0IIsIiISgP8PPqSLDnGg64wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=100\n",
    "f,axarr=plt.subplots(1,2,figsize=(8, 6))\n",
    "axarr[0].imshow(test_dataset[i].squeeze(),cmap='viridis')\n",
    "axarr[1].imshow(testee[i].squeeze(), cmap='viridis')\n",
    "print (np.argmax(preds[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}