Custom_Image_Processing_Malaria.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Custom Images Data\n",
    "\n",
    "## The Data\n",
    "ORIGINAL DATA SOURCE:\n",
    "\n",
    "The dataset contains 2 folders - Infected - Uninfected\n",
    "\n",
    "And a total of 27,558 images.\n",
    "This Dataset is taken from the official NIH Website: https://ceb.nlm.nih.gov/repositories/malaria-datasets/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.image import imread\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_dir = 'C:\\\\Users\\\\DAY17\\\\Desktop\\\\cell_images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test', 'train']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(my_data_dir) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_path = my_data_dir+'\\\\test\\\\'\n",
    "train_path = my_data_dir+'\\\\train\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['parasitized', 'uninfected']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['parasitized', 'uninfected']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C100P61ThinF_IMG_20150918_144104_cell_162.png'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'\\\\parasitized')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "para_cell = train_path+'\\\\parasitized'+'\\\\C100P61ThinF_IMG_20150918_144104_cell_162.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "para_img= imread(para_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cfff6aee08>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(para_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148, 142, 3)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cfff7ff808>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unifected_cell_path = train_path+'\\\\uninfected\\\\'+os.listdir(train_path+'\\\\uninfected')[0]\n",
    "unifected_cell = imread(unifected_cell_path)\n",
    "plt.imshow(unifected_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'\\\\parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'\\\\uninfected'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Image Dimensions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 160, 3)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unifected_cell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148, 142, 3)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim1 = []\n",
    "dim2 = []\n",
    "for image_filename in os.listdir(test_path+'\\\\uninfected'):\n",
    "    \n",
    "    img = imread(test_path+'\\\\uninfected'+'\\\\'+image_filename)\n",
    "    d1,d2,colors = img.shape\n",
    "    dim1.append(d1)\n",
    "    dim2.append(d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1cff7166848>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(dim1,dim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.92538461538462"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.75"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (130,130,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data for the model\n",
    "\n",
    "### Image Manipulation using ImageDataGenerator library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class ImageDataGenerator in module tensorflow.python.keras.preprocessing.image:\n",
      "\n",
      "class ImageDataGenerator(keras_preprocessing.image.image_data_generator.ImageDataGenerator)\n",
      " |  ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
      " |  \n",
      " |  Generate batches of tensor image data with real-time data augmentation.\n",
      " |  \n",
      " |   The data will be looped over (in batches).\n",
      " |  \n",
      " |  Arguments:\n",
      " |      featurewise_center: Boolean.\n",
      " |          Set input mean to 0 over the dataset, feature-wise.\n",
      " |      samplewise_center: Boolean. Set each sample mean to 0.\n",
      " |      featurewise_std_normalization: Boolean.\n",
      " |          Divide inputs by std of the dataset, feature-wise.\n",
      " |      samplewise_std_normalization: Boolean. Divide each input by its std.\n",
      " |      zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.\n",
      " |      zca_whitening: Boolean. Apply ZCA whitening.\n",
      " |      rotation_range: Int. Degree range for random rotations.\n",
      " |      width_shift_range: Float, 1-D array-like or int\n",
      " |          - float: fraction of total width, if < 1, or pixels if >= 1.\n",
      " |          - 1-D array-like: random elements from the array.\n",
      " |          - int: integer number of pixels from interval\n",
      " |              `(-width_shift_range, +width_shift_range)`\n",
      " |          - With `width_shift_range=2` possible values\n",
      " |              are integers `[-1, 0, +1]`,\n",
      " |              same as with `width_shift_range=[-1, 0, +1]`,\n",
      " |              while with `width_shift_range=1.0` possible values are floats\n",
      " |              in the interval [-1.0, +1.0).\n",
      " |      height_shift_range: Float, 1-D array-like or int\n",
      " |          - float: fraction of total height, if < 1, or pixels if >= 1.\n",
      " |          - 1-D array-like: random elements from the array.\n",
      " |          - int: integer number of pixels from interval\n",
      " |              `(-height_shift_range, +height_shift_range)`\n",
      " |          - With `height_shift_range=2` possible values\n",
      " |              are integers `[-1, 0, +1]`,\n",
      " |              same as with `height_shift_range=[-1, 0, +1]`,\n",
      " |              while with `height_shift_range=1.0` possible values are floats\n",
      " |              in the interval [-1.0, +1.0).\n",
      " |      brightness_range: Tuple or list of two floats. Range for picking\n",
      " |          a brightness shift value from.\n",
      " |      shear_range: Float. Shear Intensity\n",
      " |          (Shear angle in counter-clockwise direction in degrees)\n",
      " |      zoom_range: Float or [lower, upper]. Range for random zoom.\n",
      " |          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.\n",
      " |      channel_shift_range: Float. Range for random channel shifts.\n",
      " |      fill_mode: One of {\"constant\", \"nearest\", \"reflect\" or \"wrap\"}.\n",
      " |          Default is 'nearest'.\n",
      " |          Points outside the boundaries of the input are filled\n",
      " |          according to the given mode:\n",
      " |          - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)\n",
      " |          - 'nearest':  aaaaaaaa|abcd|dddddddd\n",
      " |          - 'reflect':  abcddcba|abcd|dcbaabcd\n",
      " |          - 'wrap':  abcdabcd|abcd|abcdabcd\n",
      " |      cval: Float or Int.\n",
      " |          Value used for points outside the boundaries\n",
      " |          when `fill_mode = \"constant\"`.\n",
      " |      horizontal_flip: Boolean. Randomly flip inputs horizontally.\n",
      " |      vertical_flip: Boolean. Randomly flip inputs vertically.\n",
      " |      rescale: rescaling factor. Defaults to None.\n",
      " |          If None or 0, no rescaling is applied,\n",
      " |          otherwise we multiply the data by the value provided\n",
      " |          (after applying all other transformations).\n",
      " |      preprocessing_function: function that will be implied on each input.\n",
      " |          The function will run after the image is resized and augmented.\n",
      " |          The function should take one argument:\n",
      " |          one image (Numpy tensor with rank 3),\n",
      " |          and should output a Numpy tensor with the same shape.\n",
      " |      data_format: Image data format,\n",
      " |          either \"channels_first\" or \"channels_last\".\n",
      " |          \"channels_last\" mode means that the images should have shape\n",
      " |          `(samples, height, width, channels)`,\n",
      " |          \"channels_first\" mode means that the images should have shape\n",
      " |          `(samples, channels, height, width)`.\n",
      " |          It defaults to the `image_data_format` value found in your\n",
      " |          Keras config file at `~/.keras/keras.json`.\n",
      " |          If you never set it, then it will be \"channels_last\".\n",
      " |      validation_split: Float. Fraction of images reserved for validation\n",
      " |          (strictly between 0 and 1).\n",
      " |      dtype: Dtype to use for the generated arrays.\n",
      " |  \n",
      " |  Examples:\n",
      " |  \n",
      " |  Example of using `.flow(x, y)`:\n",
      " |  \n",
      " |  ```python\n",
      " |  (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
      " |  y_train = np_utils.to_categorical(y_train, num_classes)\n",
      " |  y_test = np_utils.to_categorical(y_test, num_classes)\n",
      " |  datagen = ImageDataGenerator(\n",
      " |      featurewise_center=True,\n",
      " |      featurewise_std_normalization=True,\n",
      " |      rotation_range=20,\n",
      " |      width_shift_range=0.2,\n",
      " |      height_shift_range=0.2,\n",
      " |      horizontal_flip=True)\n",
      " |  # compute quantities required for featurewise normalization\n",
      " |  # (std, mean, and principal components if ZCA whitening is applied)\n",
      " |  datagen.fit(x_train)\n",
      " |  # fits the model on batches with real-time data augmentation:\n",
      " |  model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),\n",
      " |                      steps_per_epoch=len(x_train) / 32, epochs=epochs)\n",
      " |  # here's a more \"manual\" example\n",
      " |  for e in range(epochs):\n",
      " |      print('Epoch', e)\n",
      " |      batches = 0\n",
      " |      for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):\n",
      " |          model.fit(x_batch, y_batch)\n",
      " |          batches += 1\n",
      " |          if batches >= len(x_train) / 32:\n",
      " |              # we need to break the loop by hand because\n",
      " |              # the generator loops indefinitely\n",
      " |              break\n",
      " |  ```\n",
      " |  \n",
      " |  Example of using `.flow_from_directory(directory)`:\n",
      " |  \n",
      " |  ```python\n",
      " |  train_datagen = ImageDataGenerator(\n",
      " |          rescale=1./255,\n",
      " |          shear_range=0.2,\n",
      " |          zoom_range=0.2,\n",
      " |          horizontal_flip=True)\n",
      " |  test_datagen = ImageDataGenerator(rescale=1./255)\n",
      " |  train_generator = train_datagen.flow_from_directory(\n",
      " |          'data/train',\n",
      " |          target_size=(150, 150),\n",
      " |          batch_size=32,\n",
      " |          class_mode='binary')\n",
      " |  validation_generator = test_datagen.flow_from_directory(\n",
      " |          'data/validation',\n",
      " |          target_size=(150, 150),\n",
      " |          batch_size=32,\n",
      " |          class_mode='binary')\n",
      " |  model.fit_generator(\n",
      " |          train_generator,\n",
      " |          steps_per_epoch=2000,\n",
      " |          epochs=50,\n",
      " |          validation_data=validation_generator,\n",
      " |          validation_steps=800)\n",
      " |  ```\n",
      " |  \n",
      " |  Example of transforming images and masks together.\n",
      " |  \n",
      " |  ```python\n",
      " |  # we create two instances with the same arguments\n",
      " |  data_gen_args = dict(featurewise_center=True,\n",
      " |                       featurewise_std_normalization=True,\n",
      " |                       rotation_range=90,\n",
      " |                       width_shift_range=0.1,\n",
      " |                       height_shift_range=0.1,\n",
      " |                       zoom_range=0.2)\n",
      " |  image_datagen = ImageDataGenerator(**data_gen_args)\n",
      " |  mask_datagen = ImageDataGenerator(**data_gen_args)\n",
      " |  # Provide the same seed and keyword arguments to the fit and flow methods\n",
      " |  seed = 1\n",
      " |  image_datagen.fit(images, augment=True, seed=seed)\n",
      " |  mask_datagen.fit(masks, augment=True, seed=seed)\n",
      " |  image_generator = image_datagen.flow_from_directory(\n",
      " |      'data/images',\n",
      " |      class_mode=None,\n",
      " |      seed=seed)\n",
      " |  mask_generator = mask_datagen.flow_from_directory(\n",
      " |      'data/masks',\n",
      " |      class_mode=None,\n",
      " |      seed=seed)\n",
      " |  # combine generators into one which yields image and masks\n",
      " |  train_generator = zip(image_generator, mask_generator)\n",
      " |  model.fit_generator(\n",
      " |      train_generator,\n",
      " |      steps_per_epoch=2000,\n",
      " |      epochs=50)\n",
      " |  ```\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      ImageDataGenerator\n",
      " |      keras_preprocessing.image.image_data_generator.ImageDataGenerator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
      " |  \n",
      " |  apply_transform(self, x, transform_parameters)\n",
      " |      Applies a transformation to an image according to given parameters.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: 3D tensor, single image.\n",
      " |          transform_parameters: Dictionary with string - parameter pairs\n",
      " |              describing the transformation.\n",
      " |              Currently, the following parameters\n",
      " |              from the dictionary are used:\n",
      " |              - `'theta'`: Float. Rotation angle in degrees.\n",
      " |              - `'tx'`: Float. Shift in the x direction.\n",
      " |              - `'ty'`: Float. Shift in the y direction.\n",
      " |              - `'shear'`: Float. Shear angle in degrees.\n",
      " |              - `'zx'`: Float. Zoom in the x direction.\n",
      " |              - `'zy'`: Float. Zoom in the y direction.\n",
      " |              - `'flip_horizontal'`: Boolean. Horizontal flip.\n",
      " |              - `'flip_vertical'`: Boolean. Vertical flip.\n",
      " |              - `'channel_shift_intencity'`: Float. Channel shift intensity.\n",
      " |              - `'brightness'`: Float. Brightness shift intensity.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A transformed version of the input (same shape).\n",
      " |  \n",
      " |  fit(self, x, augment=False, rounds=1, seed=None)\n",
      " |      Fits the data generator to some sample data.\n",
      " |      \n",
      " |      This computes the internal data stats related to the\n",
      " |      data-dependent transformations, based on an array of sample data.\n",
      " |      \n",
      " |      Only required if `featurewise_center` or\n",
      " |      `featurewise_std_normalization` or `zca_whitening` are set to True.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Sample data. Should have rank 4.\n",
      " |           In case of grayscale data,\n",
      " |           the channels axis should have value 1, in case\n",
      " |           of RGB data, it should have value 3, and in case\n",
      " |           of RGBA data, it should have value 4.\n",
      " |          augment: Boolean (default: False).\n",
      " |              Whether to fit on randomly augmented samples.\n",
      " |          rounds: Int (default: 1).\n",
      " |              If using data augmentation (`augment=True`),\n",
      " |              this is how many augmentation passes over the data to use.\n",
      " |          seed: Int (default: None). Random seed.\n",
      " |  \n",
      " |  flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None)\n",
      " |      Takes data & label arrays, generates batches of augmented data.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Input data. Numpy array of rank 4 or a tuple.\n",
      " |              If tuple, the first element\n",
      " |              should contain the images and the second element\n",
      " |              another numpy array or a list of numpy arrays\n",
      " |              that gets passed to the output\n",
      " |              without any modifications.\n",
      " |              Can be used to feed the model miscellaneous data\n",
      " |              along with the images.\n",
      " |              In case of grayscale data, the channels axis of the image array\n",
      " |              should have value 1, in case\n",
      " |              of RGB data, it should have value 3, and in case\n",
      " |              of RGBA data, it should have value 4.\n",
      " |          y: Labels.\n",
      " |          batch_size: Int (default: 32).\n",
      " |          shuffle: Boolean (default: True).\n",
      " |          sample_weight: Sample weights.\n",
      " |          seed: Int (default: None).\n",
      " |          save_to_dir: None or str (default: None).\n",
      " |              This allows you to optionally specify a directory\n",
      " |              to which to save the augmented pictures being generated\n",
      " |              (useful for visualizing what you are doing).\n",
      " |          save_prefix: Str (default: `''`).\n",
      " |              Prefix to use for filenames of saved pictures\n",
      " |              (only relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |              `validation_split` is set in `ImageDataGenerator`.\n",
      " |      \n",
      " |      # Returns\n",
      " |          An `Iterator` yielding tuples of `(x, y)`\n",
      " |              where `x` is a numpy array of image data\n",
      " |              (in the case of a single image input) or a list\n",
      " |              of numpy arrays (in the case with\n",
      " |              additional inputs) and `y` is a numpy array\n",
      " |              of corresponding labels. If 'sample_weight' is not None,\n",
      " |              the yielded tuples are of the form `(x, y, sample_weight)`.\n",
      " |              If `y` is None, only the numpy array `x` is returned.\n",
      " |  \n",
      " |  flow_from_dataframe(self, dataframe, directory=None, x_col='filename', y_col='class', weight_col=None, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None, interpolation='nearest', validate_filenames=True, **kwargs)\n",
      " |      Takes the dataframe and the path to a directory\n",
      " |       and generates batches of augmented/normalized data.\n",
      " |      \n",
      " |      **A simple tutorial can be found **[here](\n",
      " |                                  http://bit.ly/keras_flow_from_dataframe).\n",
      " |      \n",
      " |      # Arguments\n",
      " |          dataframe: Pandas dataframe containing the filepaths relative to\n",
      " |              `directory` (or absolute paths if `directory` is None) of the\n",
      " |              images in a string column. It should include other column/s\n",
      " |              depending on the `class_mode`:\n",
      " |              - if `class_mode` is `\"categorical\"` (default value) it must\n",
      " |                  include the `y_col` column with the class/es of each image.\n",
      " |                  Values in column can be string/list/tuple if a single class\n",
      " |                  or list/tuple if multiple classes.\n",
      " |              - if `class_mode` is `\"binary\"` or `\"sparse\"` it must include\n",
      " |                  the given `y_col` column with class values as strings.\n",
      " |              - if `class_mode` is `\"raw\"` or `\"multi_output\"` it should contain\n",
      " |              the columns specified in `y_col`.\n",
      " |              - if `class_mode` is `\"input\"` or `None` no extra column is needed.\n",
      " |          directory: string, path to the directory to read images from. If `None`,\n",
      " |              data in `x_col` column should be absolute paths.\n",
      " |          x_col: string, column in `dataframe` that contains the filenames (or\n",
      " |              absolute paths if `directory` is `None`).\n",
      " |          y_col: string or list, column/s in `dataframe` that has the target data.\n",
      " |          weight_col: string, column in `dataframe` that contains the sample\n",
      " |              weights. Default: `None`.\n",
      " |          target_size: tuple of integers `(height, width)`, default: `(256, 256)`.\n",
      " |              The dimensions to which all images found will be resized.\n",
      " |          color_mode: one of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\".\n",
      " |              Whether the images will be converted to have 1 or 3 color channels.\n",
      " |          classes: optional list of classes (e.g. `['dogs', 'cats']`).\n",
      " |              Default: None. If not provided, the list of classes will be\n",
      " |              automatically inferred from the `y_col`,\n",
      " |              which will map to the label indices, will be alphanumeric).\n",
      " |              The dictionary containing the mapping from class names to class\n",
      " |              indices can be obtained via the attribute `class_indices`.\n",
      " |          class_mode: one of \"binary\", \"categorical\", \"input\", \"multi_output\",\n",
      " |              \"raw\", sparse\" or None. Default: \"categorical\".\n",
      " |              Mode for yielding the targets:\n",
      " |              - `\"binary\"`: 1D numpy array of binary labels,\n",
      " |              - `\"categorical\"`: 2D numpy array of one-hot encoded labels.\n",
      " |                  Supports multi-label output.\n",
      " |              - `\"input\"`: images identical to input images (mainly used to\n",
      " |                  work with autoencoders),\n",
      " |              - `\"multi_output\"`: list with the values of the different columns,\n",
      " |              - `\"raw\"`: numpy array of values in `y_col` column(s),\n",
      " |              - `\"sparse\"`: 1D numpy array of integer labels,\n",
      " |              - `None`, no targets are returned (the generator will only yield\n",
      " |                  batches of image data, which is useful to use in\n",
      " |                  `model.predict_generator()`).\n",
      " |          batch_size: size of the batches of data (default: 32).\n",
      " |          shuffle: whether to shuffle the data (default: True)\n",
      " |          seed: optional random seed for shuffling and transformations.\n",
      " |          save_to_dir: None or str (default: None).\n",
      " |              This allows you to optionally specify a directory\n",
      " |              to which to save the augmented pictures being generated\n",
      " |              (useful for visualizing what you are doing).\n",
      " |          save_prefix: str. Prefix to use for filenames of saved pictures\n",
      " |              (only relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          follow_links: whether to follow symlinks inside class subdirectories\n",
      " |              (default: False).\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |              `validation_split` is set in `ImageDataGenerator`.\n",
      " |          interpolation: Interpolation method used to resample the image if the\n",
      " |              target size is different from that of the loaded image.\n",
      " |              Supported methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`.\n",
      " |              If PIL version 1.1.3 or newer is installed, `\"lanczos\"` is also\n",
      " |              supported. If PIL version 3.4.0 or newer is installed, `\"box\"` and\n",
      " |              `\"hamming\"` are also supported. By default, `\"nearest\"` is used.\n",
      " |          validate_filenames: Boolean, whether to validate image filenames in\n",
      " |              `x_col`. If `True`, invalid images will be ignored. Disabling this\n",
      " |              option can lead to speed-up in the execution of this function.\n",
      " |              Default: `True`.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A `DataFrameIterator` yielding tuples of `(x, y)`\n",
      " |          where `x` is a numpy array containing a batch\n",
      " |          of images with shape `(batch_size, *target_size, channels)`\n",
      " |          and `y` is a numpy array of corresponding labels.\n",
      " |  \n",
      " |  flow_from_directory(self, directory, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest')\n",
      " |      Takes the path to a directory & generates batches of augmented data.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          directory: string, path to the target directory.\n",
      " |              It should contain one subdirectory per class.\n",
      " |              Any PNG, JPG, BMP, PPM or TIF images\n",
      " |              inside each of the subdirectories directory tree\n",
      " |              will be included in the generator.\n",
      " |              See [this script](\n",
      " |              https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)\n",
      " |              for more details.\n",
      " |          target_size: Tuple of integers `(height, width)`,\n",
      " |              default: `(256, 256)`.\n",
      " |              The dimensions to which all images found will be resized.\n",
      " |          color_mode: One of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\".\n",
      " |              Whether the images will be converted to\n",
      " |              have 1, 3, or 4 channels.\n",
      " |          classes: Optional list of class subdirectories\n",
      " |              (e.g. `['dogs', 'cats']`). Default: None.\n",
      " |              If not provided, the list of classes will be automatically\n",
      " |              inferred from the subdirectory names/structure\n",
      " |              under `directory`, where each subdirectory will\n",
      " |              be treated as a different class\n",
      " |              (and the order of the classes, which will map to the label\n",
      " |              indices, will be alphanumeric).\n",
      " |              The dictionary containing the mapping from class names to class\n",
      " |              indices can be obtained via the attribute `class_indices`.\n",
      " |          class_mode: One of \"categorical\", \"binary\", \"sparse\",\n",
      " |              \"input\", or None. Default: \"categorical\".\n",
      " |              Determines the type of label arrays that are returned:\n",
      " |              - \"categorical\" will be 2D one-hot encoded labels,\n",
      " |              - \"binary\" will be 1D binary labels,\n",
      " |                  \"sparse\" will be 1D integer labels,\n",
      " |              - \"input\" will be images identical\n",
      " |                  to input images (mainly used to work with autoencoders).\n",
      " |              - If None, no labels are returned\n",
      " |                (the generator will only yield batches of image data,\n",
      " |                which is useful to use with `model.predict_generator()`).\n",
      " |                Please note that in case of class_mode None,\n",
      " |                the data still needs to reside in a subdirectory\n",
      " |                of `directory` for it to work correctly.\n",
      " |          batch_size: Size of the batches of data (default: 32).\n",
      " |          shuffle: Whether to shuffle the data (default: True)\n",
      " |              If set to False, sorts the data in alphanumeric order.\n",
      " |          seed: Optional random seed for shuffling and transformations.\n",
      " |          save_to_dir: None or str (default: None).\n",
      " |              This allows you to optionally specify\n",
      " |              a directory to which to save\n",
      " |              the augmented pictures being generated\n",
      " |              (useful for visualizing what you are doing).\n",
      " |          save_prefix: Str. Prefix to use for filenames of saved pictures\n",
      " |              (only relevant if `save_to_dir` is set).\n",
      " |          save_format: One of \"png\", \"jpeg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          follow_links: Whether to follow symlinks inside\n",
      " |              class subdirectories (default: False).\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |              `validation_split` is set in `ImageDataGenerator`.\n",
      " |          interpolation: Interpolation method used to\n",
      " |              resample the image if the\n",
      " |              target size is different from that of the loaded image.\n",
      " |              Supported methods are `\"nearest\"`, `\"bilinear\"`,\n",
      " |              and `\"bicubic\"`.\n",
      " |              If PIL version 1.1.3 or newer is installed, `\"lanczos\"` is also\n",
      " |              supported. If PIL version 3.4.0 or newer is installed,\n",
      " |              `\"box\"` and `\"hamming\"` are also supported.\n",
      " |              By default, `\"nearest\"` is used.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A `DirectoryIterator` yielding tuples of `(x, y)`\n",
      " |              where `x` is a numpy array containing a batch\n",
      " |              of images with shape `(batch_size, *target_size, channels)`\n",
      " |              and `y` is a numpy array of corresponding labels.\n",
      " |  \n",
      " |  get_random_transform(self, img_shape, seed=None)\n",
      " |      Generates random parameters for a transformation.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          seed: Random seed.\n",
      " |          img_shape: Tuple of integers.\n",
      " |              Shape of the image that is transformed.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A dictionary containing randomly chosen parameters describing the\n",
      " |          transformation.\n",
      " |  \n",
      " |  random_transform(self, x, seed=None)\n",
      " |      Applies a random transformation to an image.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: 3D tensor, single image.\n",
      " |          seed: Random seed.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A randomly transformed version of the input (same shape).\n",
      " |  \n",
      " |  standardize(self, x)\n",
      " |      Applies the normalization configuration in-place to a batch of inputs.\n",
      " |      \n",
      " |      `x` is changed in-place since the function is mainly used internally\n",
      " |      to standarize images and feed them to your network. If a copy of `x`\n",
      " |      would be created instead it would have a significant performance cost.\n",
      " |      If you want to apply this method without changing the input in-place\n",
      " |      you can call the method creating a copy before:\n",
      " |      \n",
      " |      standarize(np.copy(x))\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Batch of inputs to be normalized.\n",
      " |      \n",
      " |      # Returns\n",
      " |          The inputs, normalized.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ImageDataGenerator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_gen = ImageDataGenerator(rotation_range=20, # rotate the image 20 degrees\n",
    "                               width_shift_range=0.10, # Shift the pic width by a max of 5%\n",
    "                               height_shift_range=0.10, # Shift the pic height by a max of 5%\n",
    "                               rescale=1/255, # Rescale the image by normalzing it.\n",
    "                               shear_range=0.1, # Shear means cutting away part of the image (max 10%)\n",
    "                               zoom_range=0.1, # Zoom in by 10% max\n",
    "                               horizontal_flip=True, # Allo horizontal flipping\n",
    "                               fill_mode='nearest' # Fill in missing pixels with the nearest filled value\n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cfff868908>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(para_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cfff989188>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_gen.random_transform(para_img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cfffae2048>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_gen.random_transform(para_img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating many manipulated images from a directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24958 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras_preprocessing.image.directory_iterator.DirectoryIterator at 0x1cfff9f71c8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2600 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras_preprocessing.image.directory_iterator.DirectoryIterator at 0x1cfffa0e4c8>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(test_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 128, 128, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 62, 62, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 31, 31, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 29, 29, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               1605760   \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 129       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 1,662,209\n",
      "Trainable params: 1,662,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method flow_from_directory in module keras_preprocessing.image.image_data_generator:\n",
      "\n",
      "flow_from_directory(directory, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest') method of tensorflow.python.keras.preprocessing.image.ImageDataGenerator instance\n",
      "    Takes the path to a directory & generates batches of augmented data.\n",
      "    \n",
      "    # Arguments\n",
      "        directory: string, path to the target directory.\n",
      "            It should contain one subdirectory per class.\n",
      "            Any PNG, JPG, BMP, PPM or TIF images\n",
      "            inside each of the subdirectories directory tree\n",
      "            will be included in the generator.\n",
      "            See [this script](\n",
      "            https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)\n",
      "            for more details.\n",
      "        target_size: Tuple of integers `(height, width)`,\n",
      "            default: `(256, 256)`.\n",
      "            The dimensions to which all images found will be resized.\n",
      "        color_mode: One of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\".\n",
      "            Whether the images will be converted to\n",
      "            have 1, 3, or 4 channels.\n",
      "        classes: Optional list of class subdirectories\n",
      "            (e.g. `['dogs', 'cats']`). Default: None.\n",
      "            If not provided, the list of classes will be automatically\n",
      "            inferred from the subdirectory names/structure\n",
      "            under `directory`, where each subdirectory will\n",
      "            be treated as a different class\n",
      "            (and the order of the classes, which will map to the label\n",
      "            indices, will be alphanumeric).\n",
      "            The dictionary containing the mapping from class names to class\n",
      "            indices can be obtained via the attribute `class_indices`.\n",
      "        class_mode: One of \"categorical\", \"binary\", \"sparse\",\n",
      "            \"input\", or None. Default: \"categorical\".\n",
      "            Determines the type of label arrays that are returned:\n",
      "            - \"categorical\" will be 2D one-hot encoded labels,\n",
      "            - \"binary\" will be 1D binary labels,\n",
      "                \"sparse\" will be 1D integer labels,\n",
      "            - \"input\" will be images identical\n",
      "                to input images (mainly used to work with autoencoders).\n",
      "            - If None, no labels are returned\n",
      "              (the generator will only yield batches of image data,\n",
      "              which is useful to use with `model.predict_generator()`).\n",
      "              Please note that in case of class_mode None,\n",
      "              the data still needs to reside in a subdirectory\n",
      "              of `directory` for it to work correctly.\n",
      "        batch_size: Size of the batches of data (default: 32).\n",
      "        shuffle: Whether to shuffle the data (default: True)\n",
      "            If set to False, sorts the data in alphanumeric order.\n",
      "        seed: Optional random seed for shuffling and transformations.\n",
      "        save_to_dir: None or str (default: None).\n",
      "            This allows you to optionally specify\n",
      "            a directory to which to save\n",
      "            the augmented pictures being generated\n",
      "            (useful for visualizing what you are doing).\n",
      "        save_prefix: Str. Prefix to use for filenames of saved pictures\n",
      "            (only relevant if `save_to_dir` is set).\n",
      "        save_format: One of \"png\", \"jpeg\"\n",
      "            (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      "        follow_links: Whether to follow symlinks inside\n",
      "            class subdirectories (default: False).\n",
      "        subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      "            `validation_split` is set in `ImageDataGenerator`.\n",
      "        interpolation: Interpolation method used to\n",
      "            resample the image if the\n",
      "            target size is different from that of the loaded image.\n",
      "            Supported methods are `\"nearest\"`, `\"bilinear\"`,\n",
      "            and `\"bicubic\"`.\n",
      "            If PIL version 1.1.3 or newer is installed, `\"lanczos\"` is also\n",
      "            supported. If PIL version 3.4.0 or newer is installed,\n",
      "            `\"box\"` and `\"hamming\"` are also supported.\n",
      "            By default, `\"nearest\"` is used.\n",
      "    \n",
      "    # Returns\n",
      "        A `DirectoryIterator` yielding tuples of `(x, y)`\n",
      "            where `x` is a numpy array containing a batch\n",
      "            of images with shape `(batch_size, *target_size, channels)`\n",
      "            and `y` is a numpy array of corresponding labels.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(image_gen.flow_from_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24958 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_image_gen = image_gen.flow_from_directory(train_path,\n",
    "                                               target_size=image_shape[:2],\n",
    "                                                color_mode='rgb',\n",
    "                                               batch_size=batch_size,\n",
    "                                               class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2600 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_image_gen = image_gen.flow_from_directory(test_path,\n",
    "                                               target_size=image_shape[:2],\n",
    "                                               color_mode='rgb',\n",
    "                                               batch_size=batch_size,\n",
    "                                               class_mode='binary',shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1560/1560 [==============================] - 97s 62ms/step - loss: 0.4588 - accuracy: 0.8060 - val_loss: 0.3180 - val_accuracy: 0.9112\n",
      "Epoch 2/20\n",
      "1560/1560 [==============================] - 97s 62ms/step - loss: 0.2411 - accuracy: 0.9300 - val_loss: 0.1947 - val_accuracy: 0.9412\n",
      "Epoch 3/20\n",
      "1560/1560 [==============================] - 95s 61ms/step - loss: 0.2015 - accuracy: 0.9372 - val_loss: 0.1841 - val_accuracy: 0.9423\n",
      "Epoch 4/20\n",
      "1560/1560 [==============================] - 98s 63ms/step - loss: 0.1670 - accuracy: 0.9485 - val_loss: 0.1693 - val_accuracy: 0.9431\n",
      "Epoch 5/20\n",
      "1560/1560 [==============================] - 98s 63ms/step - loss: 0.1606 - accuracy: 0.9500 - val_loss: 0.1882 - val_accuracy: 0.9388\n",
      "Epoch 6/20\n",
      "1560/1560 [==============================] - 97s 62ms/step - loss: 0.1583 - accuracy: 0.9514 - val_loss: 0.1464 - val_accuracy: 0.9542\n",
      "Epoch 7/20\n",
      "1560/1560 [==============================] - 97s 62ms/step - loss: 0.1559 - accuracy: 0.9497 - val_loss: 0.1595 - val_accuracy: 0.9500\n",
      "Epoch 8/20\n",
      "1560/1560 [==============================] - 98s 63ms/step - loss: 0.1506 - accuracy: 0.9517 - val_loss: 0.1523 - val_accuracy: 0.9496\n"
     ]
    }
   ],
   "source": [
    "results = model.fit_generator(train_image_gen,epochs=20,\n",
    "                              validation_data=test_image_gen,\n",
    "                             callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model.save('malaria_detector.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1f987d648>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses[['loss','val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'accuracy']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.14881458843521134, 0.94884616]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(test_image_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_probabilities = model.predict_generator(test_image_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.2703409e-04],\n",
       "       [2.1749735e-04],\n",
       "       [5.4228306e-04],\n",
       "       ...,\n",
       "       [9.7448182e-01],\n",
       "       [9.7615612e-01],\n",
       "       [9.6059412e-01]], dtype=float32)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pred_probabilities > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy can treat this as True/False for us\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.94      0.95      1300\n",
      "           1       0.94      0.96      0.95      1300\n",
      "\n",
      "    accuracy                           0.95      2600\n",
      "   macro avg       0.95      0.95      0.95      2600\n",
      "weighted avg       0.95      0.95      0.95      2600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_image_gen.classes,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1220,   80],\n",
       "       [  46, 1254]], dtype=int64)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_image_gen.classes,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting on an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = image.load_img(para_cell,target_size=image_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=130x130 at 0x1D205C4ECC8>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PIL.Image.Image"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = image.img_to_array(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 130, 3)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_image = np.expand_dims(my_image, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 130, 130, 3)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.]], dtype=float32)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(my_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.class_indices"
   ]
  }
 ],
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