ML Classification Breast Cancer 0-_ Healthy, 1-_ Has tumor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from PIL import Image\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import random \n",
    "import gc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to draw image\n",
    "\n",
    "def viewImage(image):\n",
    "    cv2.namedWindow('Display', cv2.WINDOW_NORMAL)\n",
    "    cv2.imshow('Display', image)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tumor_info = open('tumor_info.txt', 'r')\n",
    "train_pos = []\n",
    "train_neg = []\n",
    "\n",
    "test_pos = []\n",
    "test_neg = []\n",
    "\n",
    "if(tumor_info.mode == 'r'):\n",
    "    info = tumor_info.read()\n",
    "\n",
    "elements = info.split('\\n')\n",
    "for element in elements:\n",
    "    element_split = element.split(' ')\n",
    "    if(len(element_split) > 1 ):\n",
    "        element_split_split = element_split[0].split('b')\n",
    "\n",
    "        if(element_split[2] == 'NORM'):\n",
    "            if(int(element_split_split[1]) < 258):\n",
    "                train_neg.append(element_split_split[1])\n",
    "            else: \n",
    "                test_neg.append(element_split_split[1])\n",
    "\n",
    "        else:\n",
    "            if(int(element_split_split[1]) < 258):\n",
    "                train_pos.append(element_split_split[1])\n",
    "            else:\n",
    "                test_pos.append(element_split_split[1])\n",
    "                \n",
    "\n",
    "train_pos = list(set(train_pos))\n",
    "train_pos.sort()\n",
    "\n",
    "train_neg = list(set(train_neg))\n",
    "train_neg.sort()\n",
    "\n",
    "test_pos = list(set(test_pos))\n",
    "test_pos.sort()\n",
    "\n",
    "test_neg = list(set(test_neg))\n",
    "test_neg.sort()\n",
    "\n",
    "\n",
    "# print(train_neg)\n",
    "# print(len(train_neg))\n",
    "# print(train_pos)\n",
    "# print(len(train_pos))\n",
    "\n",
    "# print(test_neg)\n",
    "# print(len(test_neg))\n",
    "# print(test_pos)\n",
    "# print(len(test_pos))\n",
    "\n",
    "\n",
    "train_images = train_pos + train_neg\n",
    "random.shuffle(train_images)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhEcgEKiEEB6BQKAS/h9lxm3fJ0yCBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6ff3993cc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "076\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6ff398dd62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "245\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD8CAYAAABpXiE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6ff39683d588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg \n",
    "path_for_images = 'new_train_images/'\n",
    "for img in train_images[0:3]:\n",
    "    print(img)\n",
    "    image= cv2.imread(path_for_images + img + '.jpg')\n",
    "    new_image= cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    imgplot = plt.imshow(new_image)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_pixels = []\n",
    "train_labels = []\n",
    "nrows = 150\n",
    "ncols = 150\n",
    "\n",
    "for image in train_images:\n",
    "    train_images_pixels.append(cv2.resize(cv2.imread(path_for_images + image + '.jpg', cv2.IMREAD_COLOR), \n",
    "                               (nrows, ncols), interpolation= cv2.INTER_CUBIC))\n",
    "    \n",
    "    if(image in train_pos):\n",
    "        train_labels.append(1)\n",
    "    else:\n",
    "        train_labels.append(0)  \n",
    "# print(train_images_pixels)\n",
    "# print(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Labels for pos & neg')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEICAYAAABF82P+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICRJTf8fkfI9qL2suDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6ff39686cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "train_images_pixels = np.array(train_images_pixels)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "sns.countplot(train_labels)\n",
    "plt.title('Labels for pos & neg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train image is:  (257, 150, 150, 3)\n",
      "Shape of labels is:  (257,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of train image is: ', train_images_pixels.shape)\n",
    "print('Shape of labels is: ', train_labels.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train images is  (205, 150, 150, 3)\n",
      "Shape of validation images is  (52, 150, 150, 3)\n",
      "Shape of labels is  (205,)\n",
      "Shape of validation labels is  (52,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_val, y_train, y_val = train_test_split(train_images_pixels, train_labels, test_size = 0.2, random_state=2)\n",
    "\n",
    "print('Shape of train images is ', X_train.shape)\n",
    "print('Shape of validation images is ', X_val.shape)\n",
    "print('Shape of labels is ', y_train.shape)\n",
    "print('Shape of validation labels is ', y_val.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = len(X_train)\n",
    "nval = len(X_val)\n",
    "\n",
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing.image import img_to_array, load_img\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(256, (3, 3), activation='relu',input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "model.add(layers.Dropout(0.5))  #Dropout for regularization\n",
    "\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(128, activation='relu'))\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "# model = models.Sequential()\n",
    "# model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))\n",
    "# model.add(layers.MaxPooling2D((2, 2)))\n",
    "# model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "# model.add(layers.MaxPooling2D((2, 2)))\n",
    "# # model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "# # model.add(layers.MaxPooling2D((2, 2)))\n",
    "# model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "# model.add(layers.MaxPooling2D((2, 2)))\n",
    "# model.add(layers.Flatten())\n",
    "# model.add(layers.Dropout(0.5))  #Dropout for regularization\n",
    "# model.add(layers.Dense(512, activation='relu'))\n",
    "# model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_22 (Conv2D)           (None, 148, 148, 256)     7168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_22 (MaxPooling (None, 74, 74, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 72, 72, 128)       295040    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_23 (MaxPooling (None, 36, 36, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 34, 34, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_24 (MaxPooling (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_25 (Conv2D)           (None, 15, 15, 64)        73792     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_25 (MaxPooling (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_26 (Conv2D)           (None, 5, 5, 32)          18464     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_26 (MaxPooling (None, 2, 2, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 512)               66048     \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 682,081\n",
      "Trainable params: 682,081\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mean_squared_error', optimizer= 'sgd', metrics=['acc'])\n",
    "# optimizers.RMSprop(lr= 1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255,   #Scale the image between 0 and 1\n",
    "                                    rotation_range=40,\n",
    "                                    width_shift_range=0.2,\n",
    "                                    height_shift_range=0.2,\n",
    "                                    shear_range=0.2,\n",
    "                                    zoom_range=0.2,\n",
    "                                    horizontal_flip=True,)\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator = train_datagen.flow(X_train, y_train, batch_size=batch_size)\n",
    "val_generator = val_datagen.flow(X_val, y_val, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/128\n",
      "12/12 [==============================] - 5s 413ms/step - loss: 0.2484 - acc: 0.5710 - val_loss: 0.2472 - val_acc: 0.6250\n",
      "Epoch 2/128\n",
      "12/12 [==============================] - 2s 205ms/step - loss: 0.2470 - acc: 0.5885 - val_loss: 0.2474 - val_acc: 0.5833\n",
      "Epoch 3/128\n",
      "12/12 [==============================] - 2s 200ms/step - loss: 0.2454 - acc: 0.6198 - val_loss: 0.2410 - val_acc: 0.6944\n",
      "Epoch 4/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2475 - acc: 0.5803 - val_loss: 0.2477 - val_acc: 0.5556\n",
      "Epoch 5/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2460 - acc: 0.5794 - val_loss: 0.2432 - val_acc: 0.6250\n",
      "Epoch 6/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2477 - acc: 0.5586 - val_loss: 0.2432 - val_acc: 0.6111\n",
      "Epoch 7/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2454 - acc: 0.5907 - val_loss: 0.2497 - val_acc: 0.5278\n",
      "Epoch 8/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2437 - acc: 0.5990 - val_loss: 0.2355 - val_acc: 0.6944\n",
      "Epoch 9/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2450 - acc: 0.5828 - val_loss: 0.2444 - val_acc: 0.5833\n",
      "Epoch 10/128\n",
      "12/12 [==============================] - 2s 200ms/step - loss: 0.2437 - acc: 0.5990 - val_loss: 0.2334 - val_acc: 0.6944\n",
      "Epoch 11/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2465 - acc: 0.5773 - val_loss: 0.2383 - val_acc: 0.6389\n",
      "Epoch 12/128\n",
      "12/12 [==============================] - 2s 193ms/step - loss: 0.2443 - acc: 0.5855 - val_loss: 0.2465 - val_acc: 0.5556\n",
      "Epoch 13/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2465 - acc: 0.5710 - val_loss: 0.2415 - val_acc: 0.6042\n",
      "Epoch 14/128\n",
      "12/12 [==============================] - 2s 194ms/step - loss: 0.2449 - acc: 0.5825 - val_loss: 0.2468 - val_acc: 0.5556\n",
      "Epoch 15/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2440 - acc: 0.5990 - val_loss: 0.2273 - val_acc: 0.7222\n",
      "Epoch 16/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2472 - acc: 0.5553 - val_loss: 0.2436 - val_acc: 0.5833\n",
      "Epoch 17/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2422 - acc: 0.5970 - val_loss: 0.2385 - val_acc: 0.6250\n",
      "Epoch 18/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2442 - acc: 0.5805 - val_loss: 0.2333 - val_acc: 0.6667\n",
      "Epoch 19/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2438 - acc: 0.5907 - val_loss: 0.2567 - val_acc: 0.4722\n",
      "Epoch 20/128\n",
      "12/12 [==============================] - 2s 201ms/step - loss: 0.2455 - acc: 0.5784 - val_loss: 0.2292 - val_acc: 0.6944\n",
      "Epoch 21/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2445 - acc: 0.5803 - val_loss: 0.2404 - val_acc: 0.6042\n",
      "Epoch 22/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2383 - acc: 0.6302 - val_loss: 0.2313 - val_acc: 0.6667\n",
      "Epoch 23/128\n",
      "12/12 [==============================] - 2s 206ms/step - loss: 0.2451 - acc: 0.5721 - val_loss: 0.2316 - val_acc: 0.6667\n",
      "Epoch 24/128\n",
      "12/12 [==============================] - 2s 193ms/step - loss: 0.2468 - acc: 0.5639 - val_loss: 0.2508 - val_acc: 0.5278\n",
      "Epoch 25/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2423 - acc: 0.6034 - val_loss: 0.2345 - val_acc: 0.6458\n",
      "Epoch 26/128\n",
      "12/12 [==============================] - 2s 194ms/step - loss: 0.2454 - acc: 0.5751 - val_loss: 0.2511 - val_acc: 0.5278\n",
      "Epoch 27/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2419 - acc: 0.5929 - val_loss: 0.2428 - val_acc: 0.5833\n",
      "Epoch 28/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2462 - acc: 0.5616 - val_loss: 0.2271 - val_acc: 0.6944\n",
      "Epoch 29/128\n",
      "12/12 [==============================] - 2s 201ms/step - loss: 0.2418 - acc: 0.5990 - val_loss: 0.2367 - val_acc: 0.6250\n",
      "Epoch 30/128\n",
      "12/12 [==============================] - 2s 193ms/step - loss: 0.2441 - acc: 0.5913 - val_loss: 0.2311 - val_acc: 0.6667\n",
      "Epoch 31/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2414 - acc: 0.5981 - val_loss: 0.2470 - val_acc: 0.5556\n",
      "Epoch 32/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2464 - acc: 0.5677 - val_loss: 0.2390 - val_acc: 0.6111\n",
      "Epoch 33/128\n",
      "12/12 [==============================] - 2s 201ms/step - loss: 0.2450 - acc: 0.5803 - val_loss: 0.2339 - val_acc: 0.6458\n",
      "Epoch 34/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2404 - acc: 0.5981 - val_loss: 0.2392 - val_acc: 0.6111\n",
      "Epoch 35/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2424 - acc: 0.5918 - val_loss: 0.2636 - val_acc: 0.4444\n",
      "Epoch 36/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2444 - acc: 0.5786 - val_loss: 0.2180 - val_acc: 0.7500\n",
      "Epoch 37/128\n",
      "12/12 [==============================] - 2s 208ms/step - loss: 0.2379 - acc: 0.6198 - val_loss: 0.2363 - val_acc: 0.6250\n",
      "Epoch 38/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2515 - acc: 0.5397 - val_loss: 0.2435 - val_acc: 0.5833\n",
      "Epoch 39/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2415 - acc: 0.5970 - val_loss: 0.2341 - val_acc: 0.6389\n",
      "Epoch 40/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2449 - acc: 0.5698 - val_loss: 0.2389 - val_acc: 0.6111\n",
      "Epoch 41/128\n",
      "12/12 [==============================] - 3s 209ms/step - loss: 0.2459 - acc: 0.5687 - val_loss: 0.2368 - val_acc: 0.6250\n",
      "Epoch 42/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2367 - acc: 0.6302 - val_loss: 0.2255 - val_acc: 0.6944\n",
      "Epoch 43/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2456 - acc: 0.5690 - val_loss: 0.2514 - val_acc: 0.5278\n",
      "Epoch 44/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2425 - acc: 0.5938 - val_loss: 0.2388 - val_acc: 0.6111\n",
      "Epoch 45/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2455 - acc: 0.5690 - val_loss: 0.2332 - val_acc: 0.6458\n",
      "Epoch 46/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2393 - acc: 0.6146 - val_loss: 0.2341 - val_acc: 0.6389\n",
      "Epoch 47/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2485 - acc: 0.5503 - val_loss: 0.2430 - val_acc: 0.5833\n",
      "Epoch 48/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2450 - acc: 0.5781 - val_loss: 0.2429 - val_acc: 0.5833\n",
      "Epoch 49/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2384 - acc: 0.6149 - val_loss: 0.2363 - val_acc: 0.6250\n",
      "Epoch 50/128\n",
      "12/12 [==============================] - 2s 194ms/step - loss: 0.2432 - acc: 0.5825 - val_loss: 0.2427 - val_acc: 0.5833\n",
      "Epoch 51/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2437 - acc: 0.5762 - val_loss: 0.2345 - val_acc: 0.6389\n",
      "Epoch 52/128\n",
      "12/12 [==============================] - 2s 192ms/step - loss: 0.2419 - acc: 0.5877 - val_loss: 0.2381 - val_acc: 0.6111\n",
      "Epoch 53/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2410 - acc: 0.5981 - val_loss: 0.2396 - val_acc: 0.6042\n",
      "Epoch 54/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2419 - acc: 0.5970 - val_loss: 0.2293 - val_acc: 0.6667\n",
      "Epoch 55/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2454 - acc: 0.5710 - val_loss: 0.2476 - val_acc: 0.5556\n",
      "Epoch 56/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2453 - acc: 0.5657 - val_loss: 0.2341 - val_acc: 0.6389\n",
      "Epoch 57/128\n",
      "12/12 [==============================] - 3s 215ms/step - loss: 0.2438 - acc: 0.5825 - val_loss: 0.2364 - val_acc: 0.6250\n",
      "Epoch 58/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2424 - acc: 0.5938 - val_loss: 0.2472 - val_acc: 0.5556\n",
      "Epoch 59/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2440 - acc: 0.5816 - val_loss: 0.2343 - val_acc: 0.6389\n",
      "Epoch 60/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2427 - acc: 0.5803 - val_loss: 0.2342 - val_acc: 0.6389\n",
      "Epoch 61/128\n",
      "12/12 [==============================] - 2s 200ms/step - loss: 0.2363 - acc: 0.6242 - val_loss: 0.2395 - val_acc: 0.6042\n",
      "Epoch 62/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2491 - acc: 0.5417 - val_loss: 0.2428 - val_acc: 0.5833\n",
      "Epoch 63/128\n",
      "12/12 [==============================] - 2s 205ms/step - loss: 0.2416 - acc: 0.5943 - val_loss: 0.2384 - val_acc: 0.6111\n",
      "Epoch 64/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2437 - acc: 0.5833 - val_loss: 0.2297 - val_acc: 0.6667\n",
      "Epoch 65/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2420 - acc: 0.5970 - val_loss: 0.2362 - val_acc: 0.6250\n",
      "Epoch 66/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2411 - acc: 0.5992 - val_loss: 0.2428 - val_acc: 0.5833\n",
      "Epoch 67/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2432 - acc: 0.5888 - val_loss: 0.2477 - val_acc: 0.5556\n",
      "Epoch 68/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2432 - acc: 0.5866 - val_loss: 0.2246 - val_acc: 0.6944\n",
      "Epoch 69/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2460 - acc: 0.5698 - val_loss: 0.2394 - val_acc: 0.6042\n",
      "Epoch 70/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2386 - acc: 0.6146 - val_loss: 0.2475 - val_acc: 0.5556\n",
      "Epoch 71/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2497 - acc: 0.5460 - val_loss: 0.2339 - val_acc: 0.6389\n",
      "Epoch 72/128\n",
      "12/12 [==============================] - 2s 194ms/step - loss: 0.2425 - acc: 0.5899 - val_loss: 0.2300 - val_acc: 0.6667\n",
      "Epoch 73/128\n",
      "12/12 [==============================] - 2s 193ms/step - loss: 0.2417 - acc: 0.5940 - val_loss: 0.2397 - val_acc: 0.6042\n",
      "Epoch 74/128\n",
      "12/12 [==============================] - 2s 205ms/step - loss: 0.2474 - acc: 0.5573 - val_loss: 0.2340 - val_acc: 0.6389\n",
      "Epoch 75/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2393 - acc: 0.6133 - val_loss: 0.2383 - val_acc: 0.6111\n",
      "Epoch 76/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2405 - acc: 0.5992 - val_loss: 0.2385 - val_acc: 0.6111\n",
      "Epoch 77/128\n",
      "12/12 [==============================] - 2s 200ms/step - loss: 0.2464 - acc: 0.5677 - val_loss: 0.2395 - val_acc: 0.6042\n",
      "Epoch 78/128\n",
      "12/12 [==============================] - 2s 206ms/step - loss: 0.2427 - acc: 0.5940 - val_loss: 0.2202 - val_acc: 0.7222\n",
      "Epoch 79/128\n",
      "12/12 [==============================] - 3s 213ms/step - loss: 0.2432 - acc: 0.5844 - val_loss: 0.2522 - val_acc: 0.5278\n",
      "Epoch 80/128\n",
      "12/12 [==============================] - 2s 206ms/step - loss: 0.2439 - acc: 0.5825 - val_loss: 0.2386 - val_acc: 0.6111\n",
      "Epoch 81/128\n",
      "12/12 [==============================] - 3s 214ms/step - loss: 0.2449 - acc: 0.5773 - val_loss: 0.2431 - val_acc: 0.5833\n",
      "Epoch 82/128\n",
      "12/12 [==============================] - 3s 211ms/step - loss: 0.2428 - acc: 0.5907 - val_loss: 0.2253 - val_acc: 0.6944\n",
      "Epoch 83/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2438 - acc: 0.5885 - val_loss: 0.2297 - val_acc: 0.6667\n",
      "Epoch 84/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2435 - acc: 0.5828 - val_loss: 0.2519 - val_acc: 0.5278\n",
      "Epoch 85/128\n",
      "12/12 [==============================] - 2s 205ms/step - loss: 0.2411 - acc: 0.5970 - val_loss: 0.2431 - val_acc: 0.5833\n",
      "Epoch 86/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2435 - acc: 0.5825 - val_loss: 0.2295 - val_acc: 0.6667\n",
      "Epoch 87/128\n",
      "12/12 [==============================] - 2s 205ms/step - loss: 0.2418 - acc: 0.5899 - val_loss: 0.2341 - val_acc: 0.6389\n",
      "Epoch 88/128\n",
      "12/12 [==============================] - 2s 201ms/step - loss: 0.2448 - acc: 0.5781 - val_loss: 0.2430 - val_acc: 0.5833\n",
      "Epoch 89/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2432 - acc: 0.5910 - val_loss: 0.2363 - val_acc: 0.6250\n",
      "Epoch 90/128\n",
      "12/12 [==============================] - 2s 206ms/step - loss: 0.2447 - acc: 0.5729 - val_loss: 0.2432 - val_acc: 0.5833\n",
      "Epoch 91/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2415 - acc: 0.5929 - val_loss: 0.2431 - val_acc: 0.5833\n",
      "Epoch 92/128\n",
      "12/12 [==============================] - 2s 192ms/step - loss: 0.2424 - acc: 0.5907 - val_loss: 0.2293 - val_acc: 0.6667\n",
      "Epoch 93/128\n",
      "12/12 [==============================] - 2s 202ms/step - loss: 0.2454 - acc: 0.5680 - val_loss: 0.2397 - val_acc: 0.6042\n",
      "Epoch 94/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2347 - acc: 0.6346 - val_loss: 0.2196 - val_acc: 0.7222\n",
      "Epoch 95/128\n",
      "12/12 [==============================] - 2s 203ms/step - loss: 0.2448 - acc: 0.5754 - val_loss: 0.2430 - val_acc: 0.5833\n",
      "Epoch 96/128\n",
      "12/12 [==============================] - 2s 207ms/step - loss: 0.2476 - acc: 0.5583 - val_loss: 0.2476 - val_acc: 0.5556\n",
      "Epoch 97/128\n",
      "12/12 [==============================] - 2s 205ms/step - loss: 0.2408 - acc: 0.5990 - val_loss: 0.2361 - val_acc: 0.6250\n",
      "Epoch 98/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2479 - acc: 0.5542 - val_loss: 0.2255 - val_acc: 0.6944\n",
      "Epoch 99/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2407 - acc: 0.6022 - val_loss: 0.2518 - val_acc: 0.5278\n",
      "Epoch 100/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2414 - acc: 0.5981 - val_loss: 0.2386 - val_acc: 0.6111\n",
      "Epoch 101/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2449 - acc: 0.5751 - val_loss: 0.2329 - val_acc: 0.6458\n",
      "Epoch 102/128\n",
      "12/12 [==============================] - 2s 200ms/step - loss: 0.2442 - acc: 0.5814 - val_loss: 0.2385 - val_acc: 0.6111\n",
      "Epoch 103/128\n",
      "12/12 [==============================] - 2s 206ms/step - loss: 0.2446 - acc: 0.5795 - val_loss: 0.2476 - val_acc: 0.5556\n",
      "Epoch 104/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2417 - acc: 0.5940 - val_loss: 0.2341 - val_acc: 0.6389\n",
      "Epoch 105/128\n",
      "12/12 [==============================] - 2s 207ms/step - loss: 0.2429 - acc: 0.5817 - val_loss: 0.2329 - val_acc: 0.6458\n",
      "Epoch 106/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2388 - acc: 0.6097 - val_loss: 0.2340 - val_acc: 0.6389\n",
      "Epoch 107/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2509 - acc: 0.5363 - val_loss: 0.2518 - val_acc: 0.5278\n",
      "Epoch 108/128\n",
      "12/12 [==============================] - 2s 204ms/step - loss: 0.2318 - acc: 0.6562 - val_loss: 0.2337 - val_acc: 0.6389\n",
      "Epoch 109/128\n",
      "12/12 [==============================] - 2s 202ms/step - loss: 0.2493 - acc: 0.5479 - val_loss: 0.2363 - val_acc: 0.6250\n",
      "Epoch 110/128\n",
      "12/12 [==============================] - 2s 192ms/step - loss: 0.2458 - acc: 0.5698 - val_loss: 0.2430 - val_acc: 0.5833\n",
      "Epoch 111/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2384 - acc: 0.6127 - val_loss: 0.2340 - val_acc: 0.6389\n",
      "Epoch 112/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2440 - acc: 0.5825 - val_loss: 0.2386 - val_acc: 0.6111\n",
      "Epoch 113/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2467 - acc: 0.5646 - val_loss: 0.2397 - val_acc: 0.6042\n",
      "Epoch 114/128\n",
      "12/12 [==============================] - 3s 210ms/step - loss: 0.2480 - acc: 0.5573 - val_loss: 0.2475 - val_acc: 0.5556\n",
      "Epoch 115/128\n",
      "12/12 [==============================] - 2s 206ms/step - loss: 0.2370 - acc: 0.6212 - val_loss: 0.2156 - val_acc: 0.7500\n",
      "Epoch 116/128\n",
      "12/12 [==============================] - 2s 189ms/step - loss: 0.2421 - acc: 0.5891 - val_loss: 0.2477 - val_acc: 0.5556\n",
      "Epoch 117/128\n",
      "12/12 [==============================] - 2s 198ms/step - loss: 0.2446 - acc: 0.5781 - val_loss: 0.2398 - val_acc: 0.6042\n",
      "Epoch 118/128\n",
      "12/12 [==============================] - 2s 199ms/step - loss: 0.2428 - acc: 0.5836 - val_loss: 0.2293 - val_acc: 0.6667\n",
      "Epoch 119/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2437 - acc: 0.5792 - val_loss: 0.2432 - val_acc: 0.5833\n",
      "Epoch 120/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2459 - acc: 0.5646 - val_loss: 0.2386 - val_acc: 0.6111\n",
      "Epoch 121/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2398 - acc: 0.6042 - val_loss: 0.2431 - val_acc: 0.5833\n",
      "Epoch 122/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2428 - acc: 0.5899 - val_loss: 0.2476 - val_acc: 0.5556\n",
      "Epoch 123/128\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2381 - acc: 0.6100 - val_loss: 0.2154 - val_acc: 0.7500\n",
      "Epoch 124/128\n",
      "12/12 [==============================] - 2s 193ms/step - loss: 0.2505 - acc: 0.5356 - val_loss: 0.2432 - val_acc: 0.5833\n",
      "Epoch 125/128\n",
      "12/12 [==============================] - 2s 195ms/step - loss: 0.2335 - acc: 0.6399 - val_loss: 0.2327 - val_acc: 0.6458\n",
      "Epoch 126/128\n",
      "12/12 [==============================] - 2s 194ms/step - loss: 0.2414 - acc: 0.5940 - val_loss: 0.2525 - val_acc: 0.5278\n",
      "Epoch 127/128\n",
      "12/12 [==============================] - 2s 197ms/step - loss: 0.2542 - acc: 0.5177 - val_loss: 0.2342 - val_acc: 0.6389\n",
      "Epoch 128/128\n",
      "12/12 [==============================] - 2s 196ms/step - loss: 0.2392 - acc: 0.6042 - val_loss: 0.2340 - val_acc: 0.6389\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,\n",
    "                              steps_per_epoch=ntrain // batch_size,\n",
    "                              epochs=128,\n",
    "                              validation_data=val_generator,\n",
    "                              validation_steps=nval // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}