dice_cnn_API_MACTEST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D convolution network with Adam optimizer (Functional model)\n",
    "\n",
    "## This is very first functional model instead of sequential one. Due to that facts there are few issues related with this model:\n",
    "* model cannot use big batch_size, there is an error related with OOM (Out of memory)\n",
    "* model got troubles with uninitialization, probable because of using tensorflow backend instead of theano\n",
    "* model is NOT learning. After whole 5 epochs I decided to interupt because there is no sense in running it it accuracy and loss hadn't changed since beginning\n",
    "\n",
    "## My previous assumption was wrong, actually the model is learning and it is performing very well\n",
    "In comparison to the sequential model with same data set and optimizer (Adam), same layers and layers structure the difference is pretty distinctive. However, there are little differences in the count of params to be learned (25M compare to 17M in sequential one) which presumably made that difference in assumptions. I have to dive into documentation to work on this.\n",
    "##### Nonetheless here are results:\n",
    "Functional API model: \n",
    "* accuracy: 0.9517\n",
    "* loss: 0.1445\n",
    "\n",
    "Sequential model:\n",
    "* accuracy: 0.5565\n",
    "* loss: 1.0111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/oziomek/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['navyOnWhite', 'greenOnGreen', 'navyOnBlue', 'blackOnBlack', 'whiteOnBlue', 'blackOnRed', 'redOnRed', 'stainOnWhite', 'redOnRed_white', 'whiteOnBlack', 'hardLight', 'whiteOnRed', 'greenOnWhite', 'woodOnRed', 'whiteOnRed_distance']\n",
      "100800\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.utils import to_categorical\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from keras import backend as K\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from PIL import Image\n",
    "\n",
    "import numpy as np\n",
    "from numpy.testing import assert_allclose, assert_equal\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# initialization and check if dataset got proper size\n",
    "\n",
    "# path = '/home/ubuntu/zdjecia/'\n",
    "# path = '/home/oziomek/licencjat/kostki/zdjecia/'\n",
    "path = '/Users/oziomek/licencjat/kostki/zdjecia/'\n",
    "listing = os.listdir(path)\n",
    "print(listing)\n",
    "num_samples = 0\n",
    "for folder in os.listdir(path):\n",
    "    for i in range(1, 7):\n",
    "        num_samples += len(os.listdir(path + folder + '/' + str(i)))\n",
    "assert_equal(100800, num_samples)\n",
    "print(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load all data into numpy array\n",
    "immatrix = np.array(\n",
    "    [np.array(Image.open(path + folder + '/' + str(i) + '/' + file)).flatten() \n",
    "         for folder in os.listdir(path)\n",
    "             for i in range(1, 7) \n",
    "                 for file in os.listdir(path + folder + '/' + str(i))], 'f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100800, 4096)\n"
     ]
    }
   ],
   "source": [
    "print(immatrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.ones((num_samples, ), dtype=int)\n",
    "counter = 0\n",
    "for folder in os.listdir(path):\n",
    "    for i in range(1, 7):\n",
    "        samples = len(os.listdir(path + folder + '/' + str(i)))\n",
    "        labels[counter:counter+samples] = i-1\n",
    "        counter += samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# randomize data with const random_state\n",
    "data, label = shuffle(immatrix, labels, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d202128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 dots, number is 58120\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d2466a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 dots, number is 28982\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d3d5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 dots, number is 14438\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d448588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 dots, number is 96533\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d3d5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 dots, number is 93042\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d3bd588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 dots, number is 64498\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d467b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 dots, number is 98801\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d4c87f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 6 dots, number is 90250\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d4d4c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 dots, number is 31258\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d28fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 dots, number is 92660\n"
     ]
    }
   ],
   "source": [
    "# show randomly chosen image\n",
    "rows, cols = 64, 64\n",
    "labels_values = 6\n",
    "train_data = [data, label]\n",
    "import random\n",
    "for i in range(10):\n",
    "    random_image = random.randint(0, 100800)\n",
    "    img=immatrix[random_image].reshape(rows, cols)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    print(\"There are {} dots, number is {}\".format(labels[random_image] + 1, random_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, y) = (train_data[0], train_data[1])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80640, 64, 64, 1) (20160, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "# reshape to [color_layers][pixels_height][pixels_width], color_layers equal to 2 because photos are in GRAYSCALE\n",
    "X_train = X_train.reshape(X_train.shape[0], rows, cols, 1).astype('float32')\n",
    "X_test = X_test.reshape(X_test.shape[0], rows, cols, 1).astype('float32')\n",
    "\n",
    "# normalize to 0-1\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80640, 6) (20160, 6)\n"
     ]
    }
   ],
   "source": [
    "# onehot\n",
    "y_train = to_categorical(y_train, 6)\n",
    "y_test = to_categorical(y_test, 6)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create functional model with 2NN layers\n",
    "\n",
    "visible = Input(shape=(64, 64, 1))\n",
    "\n",
    "conv1 = Conv2D(32, kernel_size=5, padding='same', activation='relu')(visible)\n",
    "pool1 = MaxPooling2D((2, 2))(conv1)\n",
    "conv2 = Conv2D(64, kernel_size=5, padding='same', activation='relu')(pool1)\n",
    "dropout1 = Dropout(0.2)(conv2)\n",
    "flat1 = Flatten()(dropout1)\n",
    "hidden1 = Dense(784, kernel_initializer='random_uniform', activation='relu')(flat1)\n",
    "dropout2 = Dropout(0.2)(hidden1)\n",
    "hidden2 = Dense(196, kernel_initializer='random_uniform', activation='relu')(dropout2)\n",
    "dropout3 = Dropout(0.2)(hidden2)\n",
    "hidden3 = Dense(64, kernel_initializer='random_uniform', activation='relu')(dropout3)\n",
    "predictions = Dense(labels_values, activation='softmax')(hidden3)\n",
    "model = Model(inputs=visible, outputs=predictions)\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 64, 64, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 64)        51264     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 65536)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 784)               51381008  \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 196)               153860    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 196)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                12608     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 6)                 390       \n",
      "=================================================================\n",
      "Total params: 51,599,962\n",
      "Trainable params: 51,599,962\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# summarize model and apply checkpoints\n",
    "print(model.summary())\n",
    "filepath = 'model2API_MAC-{epoch:02d}-{loss:.4f}.h5'\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualization of a model\n",
    "plot_model(model, to_file='model2API_MAC_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 80640 samples, validate on 20160 samples\n",
      "Epoch 1/25\n",
      " 6656/80640 [=>............................] - ETA: 15:17 - loss: 1.7923 - acc: 0.1681"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-92d8b0b1e111>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1710\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1711\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1712\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1714\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1233\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1235\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1236\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1237\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2473\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2474\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2475\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2476\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=25, batch_size=256, verbose=1, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(history.history.keys())\n",
    "print(\"Accuracy: %.2f%%\" % (score[1]*100))\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('training results:')\n",
    "for i in range(len(model.metrics_names)):\n",
    "    print(str(model.metrics_names[i]) + \": \" + str(score[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#summarize for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#summarize for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n",
    "with open('model2API_MAC_model.json', 'w') as json_file:\n",
    "    json_file.write(model_json)\n",
    "    \n",
    "model.save_weights('model2API_weights.h5')\n",
    "print('Saved model weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = load_model('model2-25-0.0074.h5')\n",
    "# check in models are similar to the given tolerance\n",
    "# assert_allclose(model.predict(X_train),\n",
    "#                new_model.predict(X_train),\n",
    "#                1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'model2API_MAC_continue-{epoch:02d}-{loss:.4f}.h5'\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_history = new_model.fit(X_train, y_train, epochs=10, batch_size=2048, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_score = new_model.evaluate(X_test, y_test, verbose=1)\n",
    "print('training results:')\n",
    "for i in range(len(new_model.metrics_names)):\n",
    "    print(str(new_model.metrics_names[i]) + \": \" + str(new_score[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n",
    "with open('model2API_continue.json', 'w') as json_file:\n",
    "    json_file.write(model_json)\n",
    "    \n",
    "model.save_weights('model2API_MAC_weights_continue.h5')\n",
    "print('Saved model weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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