layeredLearningRate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TtNbkrL-PMlB"
   },
   "source": [
    "this code is about the classification of the chest CT images into one of the four cancerous conditions namely adenocarcinoma, large cell carcinoma, squamous cell carcinoma and normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "JyqpGx_FGGgR"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-04 17:15:45.592508: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/Users/shreeyakhurana/opt/anaconda3/lib/python3.9/site-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.8.0 and strictly below 2.11.0 (nightly versions are not supported). \n",
      " The versions of TensorFlow you are currently using is 2.11.0 and is not supported. \n",
      "Some things might work, some things might not.\n",
      "If you were to encounter a bug, do not file an issue.\n",
      "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n",
      "You can find the compatibility matrix in TensorFlow Addon's readme:\n",
      "https://github.com/tensorflow/addons\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "from keras import Sequential\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import * \n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications import EfficientNetB0\n",
    "from tensorflow.keras.applications import EfficientNetB7\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './../10-617-Project'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "_SyNkU-QG29J"
   },
   "outputs": [],
   "source": [
    "train_datagen = image.ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest',\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")\n",
    "val_datagen= image.ImageDataGenerator(    \n",
    "    rotation_range=15,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest',\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1)\n",
    "test_datagen= image.ImageDataGenerator(    \n",
    "    rotation_range=15,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest',\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "i4LbtpsZG4JM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 613 images belonging to 4 classes.\n",
      "Found 72 images belonging to 4 classes.\n",
      "Found 315 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "    f'{path}/Data/train',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 32,\n",
    "    shuffle=True,\n",
    "    class_mode = 'categorical')\n",
    "    \n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "    f'{path}/Data/valid',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 1,\n",
    "    shuffle=True,\n",
    "    class_mode = 'categorical')\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    f'{path}/Data/test',\n",
    "    target_size = (224,224),\n",
    "    batch_size = 1,\n",
    "    shuffle=True,\n",
    "    class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFEWiBVlQY1q"
   },
   "source": [
    "I have developed a transfer learning based EfficientNetB3 model that has been trained on imagenet dataset. I have added some additional layers to the basic model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "e1qnH-5tG7Pu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "238\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " efficientnetb0 (Functional)  (None, None, None, 1280)  4049571  \n",
      "                                                                 \n",
      " gap (GlobalMaxPooling2D)    (None, 1280)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 4)                 5124      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,054,695\n",
      "Trainable params: 4,012,672\n",
      "Non-trainable params: 42,023\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#base_model = EfficientNetB7(weights='imagenet', classifier_activation=\"softmax\", include_top=False)\n",
    "base_model = EfficientNetB0(weights='imagenet', classifier_activation=\"softmax\", include_top=False)\n",
    "\n",
    "print(len(base_model.layers))\n",
    "#print(\"Layers:\", base_model.layers)\n",
    "lr = 0.1\n",
    "lr_mult = 0.9\n",
    "\n",
    "parameters = []\n",
    "\n",
    "i = 0\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable=True\n",
    "\n",
    "model = Sequential()\n",
    "model.add(base_model)\n",
    "model.add(GlobalMaxPooling2D(name=\"gap\"))\n",
    "model.add(Dense(4, activation='softmax'))\n",
    "model.summary()\n",
    "\n",
    "\n",
    "#model.compile(loss='categorical_crossentropy',\n",
    "              #optimizer=tf.keras.optimizers.Adam(learning_rate = 0.0001),\n",
    "              #metrics=['accuracy','AUC','Precision','Recall'])\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    0.0001,\n",
    "    decay_steps=100000,\n",
    "    decay_rate=0.96,\n",
    "    staircase=False)\n",
    "optimizers = [tf.keras.optimizers.Adam(learning_rate = lr_schedule), tf.keras.optimizers.Adam(learning_rate = 0.0001)]\n",
    "optimizers_and_layers = [(optimizers[0], model.layers[0]), (optimizers[1], model.layers[1:])]\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=tfa.optimizers.MultiOptimizer(optimizers_and_layers),\n",
    "              metrics=['accuracy','AUC','Precision','Recall'])\n",
    "              \n",
    "#model.compile(loss='categorical_crossentropy',\n",
    "              #optimizer=tf.keras.optimizers.Adam(learning_rate = lr_schedule),\n",
    "              #metrics=['accuracy','AUC','Precision','Recall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Q3jD0vsxG-qd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "20/20 [==============================] - 109s 4s/step - loss: 3.7032 - accuracy: 0.3409 - auc: 0.6224 - precision: 0.3477 - recall: 0.3295 - val_loss: 2.7356 - val_accuracy: 0.3333 - val_auc: 0.5985 - val_precision: 0.3485 - val_recall: 0.3194\n",
      "Epoch 2/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 1.5298 - accuracy: 0.5873 - auc: 0.8283 - precision: 0.6041 - recall: 0.5726 - val_loss: 2.6352 - val_accuracy: 0.3750 - val_auc: 0.6346 - val_precision: 0.3803 - val_recall: 0.3750\n",
      "Epoch 3/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.9649 - accuracy: 0.6852 - auc: 0.8944 - precision: 0.6914 - recall: 0.6688 - val_loss: 2.3951 - val_accuracy: 0.4583 - val_auc: 0.7050 - val_precision: 0.4706 - val_recall: 0.4444\n",
      "Epoch 4/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.7751 - accuracy: 0.7357 - auc: 0.9199 - precision: 0.7590 - recall: 0.7243 - val_loss: 1.9454 - val_accuracy: 0.4861 - val_auc: 0.7290 - val_precision: 0.4853 - val_recall: 0.4583\n",
      "Epoch 5/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.7352 - accuracy: 0.7439 - auc: 0.9318 - precision: 0.7555 - recall: 0.7308 - val_loss: 1.6263 - val_accuracy: 0.5556 - val_auc: 0.7989 - val_precision: 0.5652 - val_recall: 0.5417\n",
      "Epoch 6/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.6346 - accuracy: 0.7716 - auc: 0.9411 - precision: 0.7887 - recall: 0.7488 - val_loss: 1.8567 - val_accuracy: 0.4722 - val_auc: 0.7720 - val_precision: 0.4789 - val_recall: 0.4722\n",
      "Epoch 7/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.5166 - accuracy: 0.8173 - auc: 0.9602 - precision: 0.8294 - recall: 0.8010 - val_loss: 1.4465 - val_accuracy: 0.5556 - val_auc: 0.8256 - val_precision: 0.5634 - val_recall: 0.5556\n",
      "Epoch 8/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.4825 - accuracy: 0.8385 - auc: 0.9632 - precision: 0.8492 - recall: 0.8271 - val_loss: 1.5359 - val_accuracy: 0.5833 - val_auc: 0.8253 - val_precision: 0.5775 - val_recall: 0.5694\n",
      "Epoch 9/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.3867 - accuracy: 0.8548 - auc: 0.9737 - precision: 0.8595 - recall: 0.8385 - val_loss: 1.6276 - val_accuracy: 0.5694 - val_auc: 0.8202 - val_precision: 0.5652 - val_recall: 0.5417\n",
      "Epoch 10/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.4079 - accuracy: 0.8434 - auc: 0.9727 - precision: 0.8531 - recall: 0.8336 - val_loss: 1.5446 - val_accuracy: 0.6528 - val_auc: 0.8241 - val_precision: 0.6479 - val_recall: 0.6389\n",
      "Epoch 11/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.3308 - accuracy: 0.8744 - auc: 0.9816 - precision: 0.8814 - recall: 0.8728 - val_loss: 1.2370 - val_accuracy: 0.6667 - val_auc: 0.8555 - val_precision: 0.6812 - val_recall: 0.6528\n",
      "Epoch 12/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.3082 - accuracy: 0.8891 - auc: 0.9817 - precision: 0.8939 - recall: 0.8793 - val_loss: 1.2468 - val_accuracy: 0.6528 - val_auc: 0.8713 - val_precision: 0.6528 - val_recall: 0.6528\n",
      "Epoch 13/30\n",
      "20/20 [==============================] - 80s 4s/step - loss: 0.2307 - accuracy: 0.9021 - auc: 0.9903 - precision: 0.9056 - recall: 0.8923 - val_loss: 1.2788 - val_accuracy: 0.6806 - val_auc: 0.8596 - val_precision: 0.6957 - val_recall: 0.6667\n",
      "Epoch 14/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.2211 - accuracy: 0.9201 - auc: 0.9904 - precision: 0.9287 - recall: 0.9135 - val_loss: 1.2315 - val_accuracy: 0.6528 - val_auc: 0.8663 - val_precision: 0.6620 - val_recall: 0.6528\n",
      "Epoch 15/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.2184 - accuracy: 0.9217 - auc: 0.9904 - precision: 0.9246 - recall: 0.9201 - val_loss: 1.1711 - val_accuracy: 0.6806 - val_auc: 0.8728 - val_precision: 0.6812 - val_recall: 0.6528\n",
      "Epoch 16/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.1937 - accuracy: 0.9315 - auc: 0.9921 - precision: 0.9389 - recall: 0.9282 - val_loss: 0.9047 - val_accuracy: 0.6528 - val_auc: 0.8985 - val_precision: 0.6667 - val_recall: 0.6389\n",
      "Epoch 17/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.2090 - accuracy: 0.9331 - auc: 0.9924 - precision: 0.9373 - recall: 0.9266 - val_loss: 1.0695 - val_accuracy: 0.6806 - val_auc: 0.8741 - val_precision: 0.7000 - val_recall: 0.6806\n",
      "Epoch 18/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.1337 - accuracy: 0.9527 - auc: 0.9968 - precision: 0.9556 - recall: 0.9478 - val_loss: 0.7461 - val_accuracy: 0.7639 - val_auc: 0.9314 - val_precision: 0.7857 - val_recall: 0.7639\n",
      "Epoch 19/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1337 - accuracy: 0.9462 - auc: 0.9967 - precision: 0.9493 - recall: 0.9462 - val_loss: 0.9337 - val_accuracy: 0.7222 - val_auc: 0.9091 - val_precision: 0.7183 - val_recall: 0.7083\n",
      "Epoch 20/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1125 - accuracy: 0.9527 - auc: 0.9972 - precision: 0.9572 - recall: 0.9494 - val_loss: 0.8451 - val_accuracy: 0.7361 - val_auc: 0.9095 - val_precision: 0.7429 - val_recall: 0.7222\n",
      "Epoch 21/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1334 - accuracy: 0.9560 - auc: 0.9967 - precision: 0.9557 - recall: 0.9511 - val_loss: 1.0032 - val_accuracy: 0.7500 - val_auc: 0.8967 - val_precision: 0.7606 - val_recall: 0.7500\n",
      "Epoch 22/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1420 - accuracy: 0.9527 - auc: 0.9955 - precision: 0.9557 - recall: 0.9511 - val_loss: 0.9322 - val_accuracy: 0.6806 - val_auc: 0.9003 - val_precision: 0.6901 - val_recall: 0.6806\n",
      "Epoch 23/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1077 - accuracy: 0.9608 - auc: 0.9980 - precision: 0.9624 - recall: 0.9592 - val_loss: 0.7924 - val_accuracy: 0.8056 - val_auc: 0.9261 - val_precision: 0.7971 - val_recall: 0.7639\n",
      "Epoch 24/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1219 - accuracy: 0.9543 - auc: 0.9973 - precision: 0.9605 - recall: 0.9511 - val_loss: 0.8293 - val_accuracy: 0.7778 - val_auc: 0.9162 - val_precision: 0.7857 - val_recall: 0.7639\n",
      "Epoch 25/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.0989 - accuracy: 0.9723 - auc: 0.9975 - precision: 0.9753 - recall: 0.9657 - val_loss: 0.8584 - val_accuracy: 0.7500 - val_auc: 0.9301 - val_precision: 0.7571 - val_recall: 0.7361\n",
      "Epoch 26/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.0795 - accuracy: 0.9723 - auc: 0.9989 - precision: 0.9738 - recall: 0.9706 - val_loss: 0.5890 - val_accuracy: 0.8333 - val_auc: 0.9515 - val_precision: 0.8696 - val_recall: 0.8333\n",
      "Epoch 27/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.0825 - accuracy: 0.9706 - auc: 0.9989 - precision: 0.9754 - recall: 0.9706 - val_loss: 0.4004 - val_accuracy: 0.8611 - val_auc: 0.9772 - val_precision: 0.8611 - val_recall: 0.8611\n",
      "Epoch 28/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.1187 - accuracy: 0.9560 - auc: 0.9974 - precision: 0.9573 - recall: 0.9511 - val_loss: 0.4635 - val_accuracy: 0.8611 - val_auc: 0.9649 - val_precision: 0.8857 - val_recall: 0.8611\n",
      "Epoch 29/30\n",
      "20/20 [==============================] - 79s 4s/step - loss: 0.0945 - accuracy: 0.9625 - auc: 0.9982 - precision: 0.9688 - recall: 0.9608 - val_loss: 0.6010 - val_accuracy: 0.7778 - val_auc: 0.9619 - val_precision: 0.8000 - val_recall: 0.7778\n",
      "Epoch 30/30\n",
      "20/20 [==============================] - 78s 4s/step - loss: 0.0609 - accuracy: 0.9804 - auc: 0.9992 - precision: 0.9804 - recall: 0.9788 - val_loss: 0.4619 - val_accuracy: 0.8750 - val_auc: 0.9569 - val_precision: 0.8873 - val_recall: 0.8750\n"
     ]
    }
   ],
   "source": [
    "\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    epochs=30,\n",
    "    validation_data=validation_generator,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'auc', 'precision', 'recall', 'val_loss', 'val_accuracy', 'val_auc', 'val_precision', 'val_recall'])\n",
      "[0.3333333432674408, 0.375, 0.4583333432674408, 0.4861111044883728, 0.5555555820465088, 0.4722222089767456, 0.5555555820465088, 0.5833333134651184, 0.5694444179534912, 0.6527777910232544, 0.6666666865348816, 0.6527777910232544, 0.6805555820465088, 0.6527777910232544, 0.6805555820465088, 0.6527777910232544, 0.6805555820465088, 0.7638888955116272, 0.7222222089767456, 0.7361111044883728, 0.75, 0.6805555820465088, 0.8055555820465088, 0.7777777910232544, 0.75, 0.8333333134651184, 0.8611111044883728, 0.8611111044883728, 0.7777777910232544, 0.875]\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "h = history.history\n",
    "print(h['val_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'accuracy', 'auc', 'precision', 'recall', 'val_loss', 'val_accuracy', 'val_auc', 'val_precision', 'val_recall']\n",
      "loss loss val_loss\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy accuracy val_accuracy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision auc val_auc\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall recall val_recall\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hdict = history.history\n",
    "keys = list(hdict.keys())\n",
    "print(keys)\n",
    "def graph(name, index):\n",
    "    print(name, keys[index], keys[(len(keys) // 2) + index])\n",
    "    train_metric, test_metric = hdict[keys[index]], hdict[keys[(len(keys) // 2) + index]]\n",
    "    epochs = len(train_metric)\n",
    "    x = list(range(epochs))\n",
    "\n",
    "    plt.plot(x, train_metric, label=f'train {name}')\n",
    "    plt.plot(x, test_metric, label=f'test {name}')\n",
    "\n",
    "    plt.xlabel('Epoch #')\n",
    "    plt.ylabel(name.capitalize())\n",
    "    plt.legend(loc='lower left')\n",
    "    plt.savefig(f'./initial_plots/{name}.png')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "\n",
    "graph('loss', 0)\n",
    "graph('accuracy', 1)\n",
    "graph('precision', 2)\n",
    "graph('recall', 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "C-ygUFFeIiC9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 19s 930ms/step - loss: 0.0684 - accuracy: 0.9772 - auc: 0.9991 - precision: 0.9771 - recall: 0.9739\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.06836698204278946,\n",
       " 0.9771615266799927,\n",
       " 0.9991289973258972,\n",
       " 0.9770867228507996,\n",
       " 0.9738988876342773]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(train_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "8d-sK2fUJDs_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72/72 [==============================] - 3s 39ms/step - loss: 0.5156 - accuracy: 0.8194 - auc: 0.9588 - precision: 0.8194 - recall: 0.8194\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5155884623527527,\n",
       " 0.8194444179534912,\n",
       " 0.9588155746459961,\n",
       " 0.8194444179534912,\n",
       " 0.8194444179534912]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation_generator)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315/315 [==============================] - 13s 42ms/step - loss: 0.4835 - accuracy: 0.8381 - auc: 0.9696 - precision: 0.8534 - recall: 0.8317\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.4834965169429779,\n",
       " 0.8380952477455139,\n",
       " 0.9695624709129333,\n",
       " 0.8534201979637146,\n",
       " 0.8317460417747498]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_generator)\n",
    "# loss, 'accuracy','AUC','Precision','Recall'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "h4MxdPKjJFlW"
   },
   "outputs": [],
   "source": [
    "# from keras.preprocessing import image\n",
    "# img = image.load_img('/content/Data/valid/squamous_cell_carcinoma_left.lower.lobe_T2_N0_M0_Ib/000117 (6).png',target_size=(224,224))\n",
    "# imag = image.img_to_array(img)\n",
    "# imaga = np.expand_dims(imag,axis=0) \n",
    "# ypred = model.predict(imaga)\n",
    "# print(ypred)\n",
    "# a=np.argmax(ypred,-1)\n",
    "# if a==0:\n",
    "#   op=\"Adenocarcinoma\"\n",
    "# elif a==1:\n",
    "#   op=\"large cell carcinoma\"\n",
    "# elif a==2:\n",
    "#   op=\"normal (void of cancer)\"\n",
    "# else:\n",
    "#   op=\"squamous cell carcinoma\"    \n",
    "# plt.imshow(img)\n",
    "# print(\"THE UPLOADED IMAGE IS SUSPECTED AS: \"+str(op))  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "QUlvhi8zVf7Z"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Unable to serialize [2.0896919 2.1128857 2.1081853] to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/h8/dz3f13cd23d_9vc7mkt_j5bw0000gn/T/ipykernel_42880/2816900310.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mymodel.hdf5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/json/__init__.py\u001b[0m in \u001b[0;36mdumps\u001b[0;34m(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    232\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m     return cls(\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0mskipkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_ascii\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ascii\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0mcheck_circular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_circular\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_nan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/json/encoder.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;31m# exceptions aren't as detailed.  The list call should be roughly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m         \u001b[0;31m# equivalent to the PySequence_Fast that ''.join() would do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m         \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_one_shot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/json/encoder.py\u001b[0m in \u001b[0;36miterencode\u001b[0;34m(self, o, _one_shot)\u001b[0m\n\u001b[1;32m    255\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_separator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_separator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                 self.skipkeys, _one_shot)\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,\n",
      "\u001b[0;31mTypeError\u001b[0m: Unable to serialize [2.0896919 2.1128857 2.1081853] to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>."
     ]
    }
   ],
   "source": [
    "tf.keras.models.save_model(model,'mymodel.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "name": "lung CT.ipynb",
   "provenance": []
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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