Malaria Detection using Cell Images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maliria Detection using Images\n",
    "\n",
    "We often have worked on Colored Cifar10 and Grayscale MNIST Data set for CNN. Since, small images say 28*28 pixels of grayscale image also have 784 datas, its very tough to create model using ANN, so we shifted to CNN.\n",
    "\n",
    "## How CNN Works?\n",
    "\n",
    "CNN is complex type of Artificial Neural Network. Generally, we can detect what image is that by looking some major parts of it. You can determine cat of dog just by their eyes. CNN works in same concept. The major image distinguisher is called filter here ( which is updated in batch using backpropagation ). Filters are those matrix which transforms our images, we may have blur filter in mobile. This works in same way. We send a large filters to CNN and CNN adjusts the best filters.\n",
    "\n",
    "1. Step 1: Input Image is multiplied by Filter\n",
    "2. Step 2: Max Pool\n",
    "3. Step 3: Repeat 1/2\n",
    "4. Step 4: Add Flatten\n",
    "5. Step 5: Add Dense and Classify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/home/evazs/Documents/KERAS - ANN/CNN/Malaria Detection using Cell Images/cell_images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['train', 'test']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imread reads the real image as pixels\n",
    "from matplotlib.image import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_path = data_dir + '/test/'\n",
    "train_path = data_dir + '/train/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/evazs/Documents/KERAS - ANN/CNN/Malaria Detection using Cell Images/cell_images/test/'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['parasitized', 'uninfected']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['parasitized', 'uninfected']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C119P80ThinF_IMG_20151002_123608_cell_141.png'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'parasitized')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "para_cell = train_path+'parasitized/'+ 'C119P80ThinF_IMG_20151002_123608_cell_141.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/evazs/Documents/KERAS - ANN/CNN/Malaria Detection using Cell Images/cell_images/train/parasitized/C119P80ThinF_IMG_20151002_123608_cell_141.png'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
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       "\n",
       "       [[0., 0., 0.],\n",
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       "        [0., 0., 0.],\n",
       "        ...,\n",
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       "\n",
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       "        [0., 0., 0.],\n",
       "        ...,\n",
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       "        [0., 0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
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       "\n",
       "       [[0., 0., 0.],\n",
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       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
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       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imread(para_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7fe3ad4750>"
      ]
     },
     "execution_count": 37,
     "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(imread(para_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C185P146ThinF_IMG_20151203_110344_cell_215.png'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'uninfected')[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "uninfected_cell = train_path + 'uninfected/'+ 'C234ThinF_IMG_20151112_162843_cell_168.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/evazs/Documents/KERAS - ANN/CNN/Malaria Detection using Cell Images/cell_images/train/uninfected/C234ThinF_IMG_20151112_162843_cell_168.png'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uninfected_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "uninfected_cell = imread(uninfected_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7fe3195e10>"
      ]
     },
     "execution_count": 49,
     "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(uninfected_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'uninfected'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(test_path+'parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(test_path+'parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIS IS REAL IMAGE, ALL IMAGES DO NO HAVE SAME\n",
    "# DIMESIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim1 = []\n",
    "dim2 = []\n",
    "\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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f7fe3172b10>"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN cannot train in different different image sizes\n",
    "# we need to reshape themm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.92538461538462"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.75"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (130,130,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ImageDataGenerator\n",
    "\n",
    "Batch Image Data Manupulation\n",
    "\n",
    "1. Resize the Image\n",
    "2. Data Augmentation ( Rotation, Scaling )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(ImageDataGenerator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_gen = ImageDataGenerator(rotation_range=20,\n",
    "                              width_shift_range=0.1,\n",
    "                              height_shift_range=0.1,\n",
    "                              rescale=1/255,\n",
    "                              shear_range=0.1,\n",
    "                              zoom_range=0.1,\n",
    "                              horizontal_flip = True,\n",
    "                              fill_mode ='nearest' \n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7f93759490>"
      ]
     },
     "execution_count": 70,
     "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": [
    "para_img = imread(para_cell)\n",
    "plt.imshow(para_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7f9358c910>"
      ]
     },
     "execution_count": 74,
     "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": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can increase imagesize using this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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 0x7f7f9353c110>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in order to use flow_from_directory we need to arrange\n",
    "# data in certain directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, MaxPool2D, Conv2D, Dropout,Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32, \n",
    "                 kernel_size=(3,3), \n",
    "                 input_shape=image_shape,\n",
    "                 activation='relu'\n",
    "                ))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters=64, \n",
    "                 kernel_size=(3,3), \n",
    "                 input_shape=image_shape,\n",
    "                 activation='relu'\n",
    "                ))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters=64, \n",
    "                 kernel_size=(3,3), \n",
    "                 input_shape=image_shape,\n",
    "                 activation='relu'\n",
    "                ))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 129       \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": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor='val_loss', patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size= 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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'\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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',\n",
    "                                               shuffle= False\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results = model.fit_generator(train_image_gen, epochs=20,\n",
    " #                            validation_data = test_image_gen,\n",
    "  #                           callbacks = [eary_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('malaria_detector.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred= model.predict_generator(test_image_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3714839e-04],\n",
       "       [3.6385773e-05],\n",
       "       [4.0358081e-04],\n",
       "       ...,\n",
       "       [9.7774804e-01],\n",
       "       [9.9245971e-01],\n",
       "       [9.8859239e-01]], dtype=float32)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pred > 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2600"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.95      0.94      1300\n",
      "           1       0.95      0.94      0.94      1300\n",
      "\n",
      "    accuracy                           0.94      2600\n",
      "   macro avg       0.94      0.94      0.94      2600\n",
      "weighted avg       0.94      0.94      0.94      2600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_image_gen.classes, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#becareful -> if you want to input new image you have to shape it to\n",
    "# (1,130,130,3)"
   ]
  }
 ],
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