Transfer_Learning_with_VGGNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yKEhHnQPBplF"
   },
   "outputs": [],
   "source": [
    "# Before running , if it is windows type ctrl+shift+i, paste following text in console.\n",
    "\n",
    "#function ClickConnect(){\n",
    "#console.log(\"Working\"); \n",
    "#document.querySelector(\"colab-toolbar-button#connect\").click() \n",
    "#}\n",
    "#setInterval(ClickConnect,60000)\n",
    "\n",
    "# so that, it will not time-out the google colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ComGKuysEfpp"
   },
   "outputs": [],
   "source": [
    "#!pip3 install --upgrade tensorflow-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mt0SLEp-fPyC"
   },
   "outputs": [],
   "source": [
    "#!pip install tensorflow-gpu==1.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3VMikRqg_Lip",
    "outputId": "8235ccae-750e-445d-fdc5-f658ad6d3b04"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow 1.x selected.\n"
     ]
    }
   ],
   "source": [
    "%tensorflow_version 1.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4_t5m2TNXxwz",
    "outputId": "e1ce5870-842a-4b8f-aac7-75197fcc2bc7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow-gpu==1.15\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/ad/933140e74973fb917a194ab814785e7c23680ca5dee6d663a509fe9579b6/tensorflow_gpu-1.15.0-cp36-cp36m-manylinux2010_x86_64.whl (411.5MB)\n",
      "\u001b[K     |████████████████████████████████| 411.5MB 38kB/s \n",
      "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (0.8.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (3.3.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (0.2.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (1.15.0)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (1.32.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (1.1.2)\n",
      "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /tensorflow-1.15.2/python3.6 (from tensorflow-gpu==1.15) (1.15.0)\n",
      "Requirement already satisfied: tensorflow-estimator==1.15.1 in /tensorflow-1.15.2/python3.6 (from tensorflow-gpu==1.15) (1.15.1)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (3.12.4)\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (1.19.5)\n",
      "Collecting gast==0.2.2\n",
      "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (1.1.0)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (1.12.1)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (0.10.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /tensorflow-1.15.2/python3.6 (from tensorflow-gpu==1.15) (1.0.8)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.15) (0.36.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15) (3.3.3)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15) (1.0.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15) (51.3.3)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu==1.15) (2.10.0)\n",
      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15) (3.3.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15) (3.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow-gpu==1.15) (3.7.4.3)\n",
      "Building wheels for collected packages: gast\n",
      "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=dfd126dd7eeb631d5243f474dcc3758adbf9cd8dd4a6e71e5676f2ca27c93090\n",
      "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
      "Successfully built gast\n",
      "Installing collected packages: gast, tensorflow-gpu\n",
      "  Found existing installation: gast 0.3.3\n",
      "    Uninstalling gast-0.3.3:\n",
      "      Successfully uninstalled gast-0.3.3\n",
      "Successfully installed gast-0.2.2 tensorflow-gpu-1.15.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow-gpu==1.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "KKSw9h2VXme9",
    "outputId": "fcd42636-e052-4e02-c325-801be7a897ed"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'1.15.2'"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DsTSzGB9f-l2",
    "outputId": "7f010da7-85ec-4066-ed8e-efae3a461f4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "2qnDZIlehs20"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = (12, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "PvcJWhQxR19N"
   },
   "outputs": [],
   "source": [
    "train_dir = '/gdrive/MyDrive/Capstone/train2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "6G2lZoQKhs2t"
   },
   "outputs": [],
   "source": [
    "#Define some parameters\n",
    "img_size = 224\n",
    "img_depth = 3  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xo-3lqmWc_KZ"
   },
   "source": [
    "Function to normalize image according to Model being used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "W85rFb5Qc-kY"
   },
   "outputs": [],
   "source": [
    "def normalize_data(img):\n",
    "    \n",
    "    #Normalize for VGG\n",
    "    return tf.keras.applications.vgg16.preprocess_input(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1jdudVjc97p"
   },
   "source": [
    "Define ImageDataGenerator and Create Data Generators objects for Training and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LWaRO3kioAmP",
    "outputId": "fbf2882c-03f6-4e2e-d2e2-5b700b9f3aff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10136 images belonging to 3 classes.\n",
      "Found 3376 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "#Read the dataset\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "data_generator = ImageDataGenerator(\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    brightness_range =(2,2),\n",
    "    channel_shift_range=50.0,\n",
    "    fill_mode=\"nearest\",\n",
    "    horizontal_flip=True,\n",
    "    rescale=1./255,\n",
    "    preprocessing_function=normalize_data,\n",
    "    validation_split=0.25,\n",
    ")\n",
    "\n",
    "\n",
    "train_generator = data_generator.flow_from_directory(train_dir, target_size=(img_size, img_size), shuffle=True, seed=13,\n",
    "                                                     class_mode='categorical', batch_size=64, subset=\"training\")\n",
    "\n",
    "validation_generator = data_generator.flow_from_directory(train_dir, target_size=(img_size, img_size), shuffle=True, seed=13,\n",
    "                                                     class_mode='categorical', batch_size=64, subset=\"validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "fj46fozcH-5S"
   },
   "outputs": [],
   "source": [
    "# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.\n",
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(1, 5, figsize=(20,20))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip( images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "xNve4ujuH-2T",
    "outputId": "bdfe3475-b94d-4778-ecd5-47326a6a3d78"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "augmented_images = [train_generator[0][0][0] for i in range(5)]\n",
    "plotImages(augmented_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1DOwu8Bhs29"
   },
   "source": [
    "### Load pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tWQJ4SzZhs2-",
    "outputId": "7ce711c9-7c27-47d8-fa99-ca715225131d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "model = tf.keras.applications.vgg16.VGG16(include_top=False, #Do not include classification layer for imagenet\n",
    "                                          input_shape=(img_size,img_size, img_depth),\n",
    "                                          weights='imagenet')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Rrq1kyn2lcGJ"
   },
   "outputs": [],
   "source": [
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PdSlv9B8hs3K"
   },
   "source": [
    "### Add FC layer for new classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "pK8neaBM9WTb"
   },
   "outputs": [],
   "source": [
    "# Set pretrained model layers to non-trainable\n",
    "for layer in model.layers:\n",
    "  layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6QxI59einXj9",
    "outputId": "91420783-3946-4e91-f4d9-9b5bda6e8897"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "3E7ylh60nQx_"
   },
   "outputs": [],
   "source": [
    "for layer in model.layers[16:]:\n",
    "  layer.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "KuK7oJ-xhs3O"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import regularizers\n",
    "\n",
    "x = model.output\n",
    "x = tf.keras.layers.Flatten()(x)\n",
    "x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "x= tf.keras.layers.Dense(1024,\n",
    "                         kernel_regularizer=regularizers.l1_l2(l1=1e-5, l2=1e-4),\n",
    "                         bias_regularizer=regularizers.l1_l2(l1=1e-4),\n",
    "                         activity_regularizer=regularizers.l2(1e-5),\n",
    "                         activation='relu')(x)\n",
    "\n",
    "x = tf.keras.layers.BatchNormalization()(x)\n",
    "x = tf.keras.layers.Dropout(0.50)(x)\n",
    "\n",
    "x= tf.keras.layers.Dense(256,\n",
    "                         kernel_regularizer=regularizers.l1_l2(l1=1e-5, l2=1e-4),\n",
    "                         bias_regularizer=regularizers.l1_l2(l1=1e-4),\n",
    "                         activity_regularizer=regularizers.l2(1e-5),\n",
    "                         activation='relu')(x)\n",
    "                         \n",
    "x = tf.keras.layers.BatchNormalization()(x)\n",
    "#Add output layer\n",
    "prediction= tf.keras.layers.Dense(3, activation='softmax')(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vo6C0vDWhs3T"
   },
   "source": [
    "### Building final model for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "ymVI9p5Shs3V"
   },
   "outputs": [],
   "source": [
    "#Using Keras Model class\n",
    "final_model = tf.keras.models.Model(inputs=model.input, #Pre-trained model input as input layer\n",
    "                                    outputs=prediction) #Output layer added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "xuUV2CXGhs3b"
   },
   "outputs": [],
   "source": [
    "#Compile the model\n",
    "nadam = tf.keras.optimizers.Nadam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "final_model.compile(optimizer=nadam, loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JRbZIcdJZmtM",
    "outputId": "897abcb3-5927-4123-a95f-9f03b80eb41f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 25088)             100352    \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              25691136  \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 771       \n",
      "=================================================================\n",
      "Total params: 40,774,467\n",
      "Trainable params: 30,726,659\n",
      "Non-trainable params: 10,047,808\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#How does our overall model looks\n",
    "final_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0v7UlgC5hs3g"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "GEIUY-2Ohs3i"
   },
   "outputs": [],
   "source": [
    "reduce_ = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, mode='auto')\n",
    "\n",
    "early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=10, mode='auto')\n",
    "\n",
    "model_checkpoint=tf.keras.callbacks.ModelCheckpoint('pneumonia_vgg16.h5', \n",
    "                                                    save_best_only=True, \n",
    "                                                    monitor='val_acc', \n",
    "                                                    mode='max', \n",
    "                                                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7YGqTxKQhs3l",
    "outputId": "22d675a2-cbce-4c1b-8875-22898071a13f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/70\n",
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "157/158 [============================>.] - ETA: 17s - loss: 2.0892 - acc: 0.8709Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 29:24 - loss: 1.7410 - acc: 0.8961\n",
      "Epoch 00001: val_acc improved from -inf to 0.89613, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 3579s 23s/step - loss: 2.0861 - acc: 0.8714 - val_loss: 1.7410 - val_acc: 0.8961\n",
      "Epoch 2/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 1.3021 - acc: 0.9024Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 2:51 - loss: 1.1786 - acc: 0.8711\n",
      "Epoch 00002: val_acc did not improve from 0.89613\n",
      "158/158 [==============================] - 357s 2s/step - loss: 1.3006 - acc: 0.9023 - val_loss: 1.1786 - val_acc: 0.8711\n",
      "Epoch 3/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.8613 - acc: 0.9085Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 2:53 - loss: 0.7742 - acc: 0.9110\n",
      "Epoch 00003: val_acc improved from 0.89613 to 0.91096, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 348s 2s/step - loss: 0.8598 - acc: 0.9088 - val_loss: 0.7742 - val_acc: 0.9110\n",
      "Epoch 4/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.6581 - acc: 0.9127Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 2:54 - loss: 0.7226 - acc: 0.9009\n",
      "Epoch 00004: val_acc did not improve from 0.91096\n",
      "158/158 [==============================] - 345s 2s/step - loss: 0.6574 - acc: 0.9129 - val_loss: 0.7226 - val_acc: 0.9009\n",
      "Epoch 5/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.5551 - acc: 0.9180Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:01 - loss: 0.5607 - acc: 0.9108\n",
      "Epoch 00005: val_acc did not improve from 0.91096\n",
      "158/158 [==============================] - 345s 2s/step - loss: 0.5547 - acc: 0.9181 - val_loss: 0.5607 - val_acc: 0.9108\n",
      "Epoch 6/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.5195 - acc: 0.9187Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 2:55 - loss: 0.5413 - acc: 0.9090\n",
      "Epoch 00006: val_acc did not improve from 0.91096\n",
      "158/158 [==============================] - 346s 2s/step - loss: 0.5192 - acc: 0.9187 - val_loss: 0.5413 - val_acc: 0.9090\n",
      "Epoch 7/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.4898 - acc: 0.9210Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 2:59 - loss: 1.0453 - acc: 0.7993\n",
      "Epoch 00007: val_acc did not improve from 0.91096\n",
      "158/158 [==============================] - 345s 2s/step - loss: 0.4897 - acc: 0.9211 - val_loss: 1.0453 - val_acc: 0.7993\n",
      "Epoch 8/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.5422 - acc: 0.9196Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:05 - loss: 0.6299 - acc: 0.8951\n",
      "Epoch 00008: val_acc did not improve from 0.91096\n",
      "158/158 [==============================] - 342s 2s/step - loss: 0.5417 - acc: 0.9197 - val_loss: 0.6299 - val_acc: 0.8951\n",
      "Epoch 9/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.5524 - acc: 0.9228Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:05 - loss: 0.5787 - acc: 0.9126\n",
      "Epoch 00009: val_acc improved from 0.91096 to 0.91256, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 350s 2s/step - loss: 0.5522 - acc: 0.9228 - val_loss: 0.5787 - val_acc: 0.9126\n",
      "Epoch 10/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.5222 - acc: 0.9203Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:04 - loss: 0.5989 - acc: 0.9049\n",
      "Epoch 00010: val_acc did not improve from 0.91256\n",
      "158/158 [==============================] - 344s 2s/step - loss: 0.5222 - acc: 0.9200 - val_loss: 0.5989 - val_acc: 0.9049\n",
      "Epoch 11/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.4953 - acc: 0.9207Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:12 - loss: 0.6965 - acc: 0.8697\n",
      "Epoch 00011: val_acc did not improve from 0.91256\n",
      "158/158 [==============================] - 348s 2s/step - loss: 0.4953 - acc: 0.9206 - val_loss: 0.6965 - val_acc: 0.8697\n",
      "Epoch 12/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.4637 - acc: 0.9301Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.4426 - acc: 0.9271\n",
      "Epoch 00012: val_acc improved from 0.91256 to 0.92708, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 344s 2s/step - loss: 0.4632 - acc: 0.9301 - val_loss: 0.4426 - val_acc: 0.9271\n",
      "Epoch 13/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.4160 - acc: 0.9339Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.4141 - acc: 0.9302\n",
      "Epoch 00013: val_acc improved from 0.92708 to 0.93019, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.4161 - acc: 0.9337 - val_loss: 0.4141 - val_acc: 0.9302\n",
      "Epoch 14/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3894 - acc: 0.9353Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.3930 - acc: 0.9266\n",
      "Epoch 00014: val_acc did not improve from 0.93019\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.3891 - acc: 0.9353 - val_loss: 0.3930 - val_acc: 0.9266\n",
      "Epoch 15/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3748 - acc: 0.9346Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.3738 - acc: 0.9309\n",
      "Epoch 00015: val_acc improved from 0.93019 to 0.93089, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.3746 - acc: 0.9346 - val_loss: 0.3738 - val_acc: 0.9309\n",
      "Epoch 16/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3546 - acc: 0.9367Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.3615 - acc: 0.9287\n",
      "Epoch 00016: val_acc did not improve from 0.93089\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.3543 - acc: 0.9368 - val_loss: 0.3615 - val_acc: 0.9287\n",
      "Epoch 17/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3456 - acc: 0.9366Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.3685 - acc: 0.9206\n",
      "Epoch 00017: val_acc did not improve from 0.93089\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.3452 - acc: 0.9368 - val_loss: 0.3685 - val_acc: 0.9206\n",
      "Epoch 18/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3352 - acc: 0.9343Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.3578 - acc: 0.9201\n",
      "Epoch 00018: val_acc did not improve from 0.93089\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.3348 - acc: 0.9344 - val_loss: 0.3578 - val_acc: 0.9201\n",
      "Epoch 19/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3250 - acc: 0.9376Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.3329 - acc: 0.9273\n",
      "Epoch 00019: val_acc did not improve from 0.93089\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.3248 - acc: 0.9376 - val_loss: 0.3329 - val_acc: 0.9273\n",
      "Epoch 20/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3196 - acc: 0.9366Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.3269 - acc: 0.9298\n",
      "Epoch 00020: val_acc did not improve from 0.93089\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.3196 - acc: 0.9366 - val_loss: 0.3269 - val_acc: 0.9298\n",
      "Epoch 21/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3122 - acc: 0.9359Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.3191 - acc: 0.9327\n",
      "Epoch 00021: val_acc improved from 0.93089 to 0.93269, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 341s 2s/step - loss: 0.3122 - acc: 0.9359 - val_loss: 0.3191 - val_acc: 0.9327\n",
      "Epoch 22/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3051 - acc: 0.9384Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.3384 - acc: 0.9200\n",
      "Epoch 00022: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 342s 2s/step - loss: 0.3050 - acc: 0.9384 - val_loss: 0.3384 - val_acc: 0.9200\n",
      "Epoch 23/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.3041 - acc: 0.9345Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.3308 - acc: 0.9259\n",
      "Epoch 00023: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.3044 - acc: 0.9345 - val_loss: 0.3308 - val_acc: 0.9259\n",
      "Epoch 24/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2989 - acc: 0.9371Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.3511 - acc: 0.9139\n",
      "Epoch 00024: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2994 - acc: 0.9368 - val_loss: 0.3511 - val_acc: 0.9139\n",
      "Epoch 25/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2979 - acc: 0.9348Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.3143 - acc: 0.9295\n",
      "Epoch 00025: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2974 - acc: 0.9350 - val_loss: 0.3143 - val_acc: 0.9295\n",
      "Epoch 26/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2900 - acc: 0.9409Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.3120 - acc: 0.9297\n",
      "Epoch 00026: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2897 - acc: 0.9409 - val_loss: 0.3120 - val_acc: 0.9297\n",
      "Epoch 27/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2920 - acc: 0.9367Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:14 - loss: 0.3109 - acc: 0.9298\n",
      "Epoch 00027: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 345s 2s/step - loss: 0.2918 - acc: 0.9370 - val_loss: 0.3109 - val_acc: 0.9298\n",
      "Epoch 28/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2892 - acc: 0.9375Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:11 - loss: 0.3106 - acc: 0.9305\n",
      "Epoch 00028: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 348s 2s/step - loss: 0.2896 - acc: 0.9375 - val_loss: 0.3106 - val_acc: 0.9305\n",
      "Epoch 29/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2874 - acc: 0.9373Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.3065 - acc: 0.9306\n",
      "Epoch 00029: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2870 - acc: 0.9374 - val_loss: 0.3065 - val_acc: 0.9306\n",
      "Epoch 30/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2815 - acc: 0.9376Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.3007 - acc: 0.9266\n",
      "Epoch 00030: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 336s 2s/step - loss: 0.2814 - acc: 0.9376 - val_loss: 0.3007 - val_acc: 0.9266\n",
      "Epoch 31/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2741 - acc: 0.9399Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.2936 - acc: 0.9323\n",
      "Epoch 00031: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2738 - acc: 0.9401 - val_loss: 0.2936 - val_acc: 0.9323\n",
      "Epoch 32/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2748 - acc: 0.9391Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.3037 - acc: 0.9270\n",
      "Epoch 00032: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 336s 2s/step - loss: 0.2746 - acc: 0.9392 - val_loss: 0.3037 - val_acc: 0.9270\n",
      "Epoch 33/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2759 - acc: 0.9384Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.3013 - acc: 0.9268\n",
      "Epoch 00033: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2758 - acc: 0.9385 - val_loss: 0.3013 - val_acc: 0.9268\n",
      "Epoch 34/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2710 - acc: 0.9409Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:10 - loss: 0.2935 - acc: 0.9315\n",
      "Epoch 00034: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.2714 - acc: 0.9409 - val_loss: 0.2935 - val_acc: 0.9315\n",
      "Epoch 35/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2739 - acc: 0.9384Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.3434 - acc: 0.9226\n",
      "Epoch 00035: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 336s 2s/step - loss: 0.2739 - acc: 0.9384 - val_loss: 0.3434 - val_acc: 0.9226\n",
      "Epoch 36/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2804 - acc: 0.9378Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:05 - loss: 0.3001 - acc: 0.9274\n",
      "Epoch 00036: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2798 - acc: 0.9381 - val_loss: 0.3001 - val_acc: 0.9274\n",
      "Epoch 37/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2817 - acc: 0.9384Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.3068 - acc: 0.9264\n",
      "Epoch 00037: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2816 - acc: 0.9383 - val_loss: 0.3068 - val_acc: 0.9264\n",
      "Epoch 38/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2766 - acc: 0.9385Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.3099 - acc: 0.9253\n",
      "Epoch 00038: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 336s 2s/step - loss: 0.2764 - acc: 0.9387 - val_loss: 0.3099 - val_acc: 0.9253\n",
      "Epoch 39/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2704 - acc: 0.9422Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:04 - loss: 0.3121 - acc: 0.9251\n",
      "Epoch 00039: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 336s 2s/step - loss: 0.2709 - acc: 0.9420 - val_loss: 0.3121 - val_acc: 0.9251\n",
      "Epoch 40/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2595 - acc: 0.9428Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.2849 - acc: 0.9327\n",
      "Epoch 00040: val_acc improved from 0.93269 to 0.93269, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.2592 - acc: 0.9430 - val_loss: 0.2849 - val_acc: 0.9327\n",
      "Epoch 41/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2602 - acc: 0.9445Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.2872 - acc: 0.9288\n",
      "Epoch 00041: val_acc did not improve from 0.93269\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2605 - acc: 0.9443 - val_loss: 0.2872 - val_acc: 0.9288\n",
      "Epoch 42/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2529 - acc: 0.9427Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:11 - loss: 0.2774 - acc: 0.9329\n",
      "Epoch 00042: val_acc improved from 0.93269 to 0.93289, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 342s 2s/step - loss: 0.2529 - acc: 0.9427 - val_loss: 0.2774 - val_acc: 0.9329\n",
      "Epoch 43/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2490 - acc: 0.9449Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.2804 - acc: 0.9307\n",
      "Epoch 00043: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2489 - acc: 0.9450 - val_loss: 0.2804 - val_acc: 0.9307\n",
      "Epoch 44/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2483 - acc: 0.9429Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.2818 - acc: 0.9289\n",
      "Epoch 00044: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2491 - acc: 0.9425 - val_loss: 0.2818 - val_acc: 0.9289\n",
      "Epoch 45/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2537 - acc: 0.9434Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.2770 - acc: 0.9321\n",
      "Epoch 00045: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 336s 2s/step - loss: 0.2535 - acc: 0.9434 - val_loss: 0.2770 - val_acc: 0.9321\n",
      "Epoch 46/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2467 - acc: 0.9452Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.2742 - acc: 0.9318\n",
      "Epoch 00046: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.2466 - acc: 0.9452 - val_loss: 0.2742 - val_acc: 0.9318\n",
      "Epoch 47/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2477 - acc: 0.9439Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:05 - loss: 0.2722 - acc: 0.9327\n",
      "Epoch 00047: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2473 - acc: 0.9440 - val_loss: 0.2722 - val_acc: 0.9327\n",
      "Epoch 48/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2465 - acc: 0.9442Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.2778 - acc: 0.9292\n",
      "Epoch 00048: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2465 - acc: 0.9441 - val_loss: 0.2778 - val_acc: 0.9292\n",
      "Epoch 49/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2397 - acc: 0.9471Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.2732 - acc: 0.9291\n",
      "Epoch 00049: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2399 - acc: 0.9469 - val_loss: 0.2732 - val_acc: 0.9291\n",
      "Epoch 50/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2398 - acc: 0.9447Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.2687 - acc: 0.9308\n",
      "Epoch 00050: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2401 - acc: 0.9447 - val_loss: 0.2687 - val_acc: 0.9308\n",
      "Epoch 51/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2401 - acc: 0.9448Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:05 - loss: 0.2658 - acc: 0.9328\n",
      "Epoch 00051: val_acc did not improve from 0.93289\n",
      "158/158 [==============================] - 337s 2s/step - loss: 0.2401 - acc: 0.9447 - val_loss: 0.2658 - val_acc: 0.9328\n",
      "Epoch 52/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2378 - acc: 0.9462Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.2668 - acc: 0.9351\n",
      "Epoch 00052: val_acc improved from 0.93289 to 0.93510, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.2377 - acc: 0.9462 - val_loss: 0.2668 - val_acc: 0.9351\n",
      "Epoch 53/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2356 - acc: 0.9466Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.2715 - acc: 0.9337\n",
      "Epoch 00053: val_acc did not improve from 0.93510\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.2354 - acc: 0.9467 - val_loss: 0.2715 - val_acc: 0.9337\n",
      "Epoch 54/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2362 - acc: 0.9452Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:10 - loss: 0.2657 - acc: 0.9307\n",
      "Epoch 00054: val_acc did not improve from 0.93510\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2359 - acc: 0.9454 - val_loss: 0.2657 - val_acc: 0.9307\n",
      "Epoch 55/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2351 - acc: 0.9468Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:12 - loss: 0.2696 - acc: 0.9295\n",
      "Epoch 00055: val_acc did not improve from 0.93510\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2354 - acc: 0.9466 - val_loss: 0.2696 - val_acc: 0.9295\n",
      "Epoch 56/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2305 - acc: 0.9474Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:10 - loss: 0.2602 - acc: 0.9337\n",
      "Epoch 00056: val_acc did not improve from 0.93510\n",
      "158/158 [==============================] - 342s 2s/step - loss: 0.2302 - acc: 0.9475 - val_loss: 0.2602 - val_acc: 0.9337\n",
      "Epoch 57/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2371 - acc: 0.9449Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:13 - loss: 0.2630 - acc: 0.9320\n",
      "Epoch 00057: val_acc did not improve from 0.93510\n",
      "158/158 [==============================] - 345s 2s/step - loss: 0.2368 - acc: 0.9451 - val_loss: 0.2630 - val_acc: 0.9320\n",
      "Epoch 58/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2377 - acc: 0.9446Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:10 - loss: 0.2609 - acc: 0.9338\n",
      "Epoch 00058: val_acc did not improve from 0.93510\n",
      "158/158 [==============================] - 341s 2s/step - loss: 0.2377 - acc: 0.9446 - val_loss: 0.2609 - val_acc: 0.9338\n",
      "Epoch 59/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2280 - acc: 0.9472Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.2635 - acc: 0.9352\n",
      "Epoch 00059: val_acc improved from 0.93510 to 0.93520, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 342s 2s/step - loss: 0.2282 - acc: 0.9471 - val_loss: 0.2635 - val_acc: 0.9352\n",
      "Epoch 60/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2274 - acc: 0.9452Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.2574 - acc: 0.9331\n",
      "Epoch 00060: val_acc did not improve from 0.93520\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2273 - acc: 0.9452 - val_loss: 0.2574 - val_acc: 0.9331\n",
      "Epoch 61/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2253 - acc: 0.9480Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.2566 - acc: 0.9330\n",
      "Epoch 00061: val_acc did not improve from 0.93520\n",
      "158/158 [==============================] - 348s 2s/step - loss: 0.2250 - acc: 0.9481 - val_loss: 0.2566 - val_acc: 0.9330\n",
      "Epoch 62/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2295 - acc: 0.9467Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.2538 - acc: 0.9359\n",
      "Epoch 00062: val_acc improved from 0.93520 to 0.93590, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 341s 2s/step - loss: 0.2292 - acc: 0.9468 - val_loss: 0.2538 - val_acc: 0.9359\n",
      "Epoch 63/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2330 - acc: 0.9433Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:06 - loss: 0.2568 - acc: 0.9335\n",
      "Epoch 00063: val_acc did not improve from 0.93590\n",
      "158/158 [==============================] - 341s 2s/step - loss: 0.2328 - acc: 0.9433 - val_loss: 0.2568 - val_acc: 0.9335\n",
      "Epoch 64/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2266 - acc: 0.9462Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:10 - loss: 0.2637 - acc: 0.9314\n",
      "Epoch 00064: val_acc did not improve from 0.93590\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2266 - acc: 0.9461 - val_loss: 0.2637 - val_acc: 0.9314\n",
      "Epoch 65/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2261 - acc: 0.9478Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:10 - loss: 0.2607 - acc: 0.9320\n",
      "Epoch 00065: val_acc did not improve from 0.93590\n",
      "158/158 [==============================] - 339s 2s/step - loss: 0.2262 - acc: 0.9478 - val_loss: 0.2607 - val_acc: 0.9320\n",
      "Epoch 66/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2263 - acc: 0.9462Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:09 - loss: 0.2631 - acc: 0.9265\n",
      "Epoch 00066: val_acc did not improve from 0.93590\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2262 - acc: 0.9462 - val_loss: 0.2631 - val_acc: 0.9265\n",
      "Epoch 67/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2275 - acc: 0.9453Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.2556 - acc: 0.9362\n",
      "Epoch 00067: val_acc improved from 0.93590 to 0.93620, saving model to pneumonia_vgg16.h5\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2273 - acc: 0.9455 - val_loss: 0.2556 - val_acc: 0.9362\n",
      "Epoch 68/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2236 - acc: 0.9472Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.2604 - acc: 0.9325\n",
      "Epoch 00068: val_acc did not improve from 0.93620\n",
      "158/158 [==============================] - 341s 2s/step - loss: 0.2241 - acc: 0.9469 - val_loss: 0.2604 - val_acc: 0.9325\n",
      "Epoch 69/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2186 - acc: 0.9482Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:08 - loss: 0.2561 - acc: 0.9316\n",
      "Epoch 00069: val_acc did not improve from 0.93620\n",
      "158/158 [==============================] - 340s 2s/step - loss: 0.2183 - acc: 0.9482 - val_loss: 0.2561 - val_acc: 0.9316\n",
      "Epoch 70/70\n",
      "157/158 [============================>.] - ETA: 1s - loss: 0.2244 - acc: 0.9473Epoch 1/70\n",
      " 52/158 [========>.....................] - ETA: 3:07 - loss: 0.2529 - acc: 0.9327\n",
      "Epoch 00070: val_acc did not improve from 0.93620\n",
      "158/158 [==============================] - 338s 2s/step - loss: 0.2247 - acc: 0.9472 - val_loss: 0.2529 - val_acc: 0.9327\n"
     ]
    }
   ],
   "source": [
    "history = final_model.fit(train_generator, \n",
    "                          epochs=70,\n",
    "                          steps_per_epoch= 10136//64,\n",
    "                          validation_data=validation_generator,\n",
    "                          validation_steps = 3376//64, \n",
    "                          callbacks=[model_checkpoint, reduce_, early])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 747
    },
    "id": "_vnNyh4r_reG",
    "outputId": "fafb511a-76bf-47ac-be81-2ecc1c5e2137"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efdca7f6080>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['acc'], label='Accuracy')\n",
    "plt.plot(history.history['val_acc'], label = 'Val_acc')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TJnlqk0zr8Cc",
    "outputId": "c52c64f8-1c22-4242-ddc1-07d710a5a905"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 93.59%\n"
     ]
    }
   ],
   "source": [
    "score = final_model.evaluate_generator(validation_generator,steps = 32)                          \n",
    "print(\"%s: %.2f%%\" % (final_model.metrics_names[1], score[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "IwwKjbI-sLvT"
   },
   "outputs": [],
   "source": [
    "y_test = final_model.predict_generator(validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bBwQYmO7sNTX",
    "outputId": "446ddddd-5587-4283-cb33-0498e3979454"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    3,    2],\n",
       "       [  14, 1257,  862],\n",
       "       [  10,  731,  497]])"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_non_category = [ np.argmax(t) for t in y_test ]\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "conf_mat = confusion_matrix(y_test_non_category, validation_generator.classes)\n",
    "conf_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2WmT3DDxsRlw",
    "outputId": "c529c89b-37c8-4498-9522-10c61e701f80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    COVID-19       0.00      0.00      0.00         5\n",
      "      normal       0.63      0.59      0.61      2133\n",
      "   pneumonia       0.37      0.40      0.38      1238\n",
      "\n",
      "    accuracy                           0.52      3376\n",
      "   macro avg       0.33      0.33      0.33      3376\n",
      "weighted avg       0.53      0.52      0.53      3376\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test_non_category, validation_generator.classes,target_names=validation_generator.class_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "0tK2n_8TsV2m"
   },
   "outputs": [],
   "source": [
    "model_json = final_model.to_json()\n",
    "with open(\"final_model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "0vePGDrFTgBe"
   },
   "outputs": [],
   "source": [
    "model.save_weights('pneumonia_vgg16_1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "VSS7ytOjBX6P"
   },
   "outputs": [],
   "source": [
    "!cp ./pneumonia_vgg16.h5 '/gdrive/MyDrive/Capstone/Stage1_trail'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cdp5TdEKDU2N"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "RDiwkHqmsymr"
   },
   "outputs": [],
   "source": [
    " # load json and create model\n",
    "#json_file = open('final_model.json', 'r')\n",
    "#loaded_model_json = json_file.read()\n",
    "#json_file.close()\n",
    "#loaded_model = final_model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "#loaded_model.load_weights(\"pneumonia_vgg16_1.h5\")\n",
    "#print(\"Loaded model from disk\")\n",
    " \n",
    "# evaluate loaded model on test data\n",
    "#loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['acc'])\n",
    "#score = loaded_model.evaluate(validation_generator, verbose=0)\n",
    "#print(\"%s: %.2f%%\" % (loaded_model.metrics_names[1], score[1]*100))"
   ]
  }
 ],
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   "name": "Transfer Learning with VGGNet.ipynb",
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