diff --git a/Railway Track Fault Detection/Model/conv.py b/Railway Track Fault Detection/Model/conv.py index f45b3a649..62c38dafb 100644 --- a/Railway Track Fault Detection/Model/conv.py +++ b/Railway Track Fault Detection/Model/conv.py @@ -1,80 +1,80 @@ -# A Python script to change the size of images and save them inside a new directory -# The script contains two functions: -# 1. To change the names of current images to standard number labels. -# 2. To convert the images of random sizes to a fixed size 200X200 resolution. -# This makes the further process of training the model easy as dimensions of each image -# are the same - - -# Import all the necessary modules -import cv2 -import glob -import os - - -# Source folder location of images -defective = "D://ML DL AI DSBDA//Railway Track Fault Detection//archive//images//Defective" -nonDefective = "D://ML DL AI DSBDA//Railway Track Fault Detection//archive//images//Non Defective" -# Create a separate directory to store processed images - - - -def changeNames(path: str) -> None: - 'Change the naming conventions of images:' - k = 0 - location = os.chdir(path) - for old_file_name in os.listdir(location): - new_file_name = '{}.jpg'.format(k) - os.rename(old_file_name, new_file_name) - k += 1 - - -def changeDimensionsForDefectiveImages(width: int, height: int) -> None: - 'Change the random dimensions of all images to a fixed resolution and then insert into a new folder' - i = 0 - for img in glob.glob(defective + '//*.jpg'): - image = cv2.imread(img) - imageResized = cv2.resize(image, (width, height)) - - # cv2.imshow('frame', image) - - cv2.imwrite( - "D://ML DL AI DSBDA//Railway Track Fault Detection//Processed Images//Defective//000{}.jpg".format(i), imageResized) - cv2.waitKey(0) - i += 1 - - # if i == 2: - # break - - cv2.destroyAllWindows() - -def changeDimensionsForNonDefectiveImages(width: int, height: int) -> None: - 'Change the random dimensions of all images to a fixed resolution and then insert into a new folder' - i = 0 - for img in glob.glob(nonDefective + '//*.jpg'): - image = cv2.imread(img) - imageResized = cv2.resize(image, (width, height)) - - # cv2.imshow('frame', image) - cv2.imwrite( - "D://ML DL AI DSBDA//Railway Track Fault Detection//Processed Images//Non Defective//000{}.jpg".format(i), imageResized) - cv2.waitKey(0) - i += 1 - - # if i == 2: - # break - - cv2.destroyAllWindows() - - -def main(): - defectiveImagePath = 'D://ML DL AI DSBDA//Railway Track Fault Detection//archive//Images//Defective' - nonDefectivePath = 'D://ML DL AI DSBDA//Railway Track Fault Detection//archive//Images//Non Defective' - changeNames(defectiveImagePath) - changeNames(nonDefectivePath) - changeDimensionsForDefectiveImages(256, 256) - changeDimensionsForNonDefectiveImages(256, 256) - - -if __name__ == '__main__': - main() +# A Python script to change the size of images and save them inside a new directory +# The script contains two functions: +# 1. To change the names of current images to standard number labels. +# 2. To convert the images of random sizes to a fixed size 200X200 resolution. +# This makes the further process of training the model easy as dimensions of each image +# are the same + + +# Import all the necessary modules +import cv2 +import glob +import os + + +# Source folder location of images +defective = "D://ML DL AI DSBDA//Railway Track Fault Detection//archive//images//Defective" +nonDefective = "D://ML DL AI DSBDA//Railway Track Fault Detection//archive//images//Non Defective" +# Create a separate directory to store processed images + + + +def changeNames(path: str) -> None: + 'Change the naming conventions of images:' + k = 0 + location = os.chdir(path) + for old_file_name in os.listdir(location): + new_file_name = '{}.jpg'.format(k) + os.rename(old_file_name, new_file_name) + k += 1 + + +def changeDimensionsForDefectiveImages(width: int, height: int) -> None: + 'Change the random dimensions of all images to a fixed resolution and then insert into a new folder' + i = 0 + for img in glob.glob(defective + '//*.jpg'): + image = cv2.imread(img) + imageResized = cv2.resize(image, (width, height)) + + # cv2.imshow('frame', image) + + cv2.imwrite( + "D://ML DL AI DSBDA//Railway Track Fault Detection//Processed Images//Defective//000{}.jpg".format(i), imageResized) + cv2.waitKey(0) + i += 1 + + # if i == 2: + # break + + cv2.destroyAllWindows() + +def changeDimensionsForNonDefectiveImages(width: int, height: int) -> None: + 'Change the random dimensions of all images to a fixed resolution and then insert into a new folder' + i = 0 + for img in glob.glob(nonDefective + '//*.jpg'): + image = cv2.imread(img) + imageResized = cv2.resize(image, (width, height)) + + # cv2.imshow('frame', image) + cv2.imwrite( + "D://ML DL AI DSBDA//Railway Track Fault Detection//Processed Images//Non Defective//000{}.jpg".format(i), imageResized) + cv2.waitKey(0) + i += 1 + + # if i == 2: + # break + + cv2.destroyAllWindows() + + +def main(): + defectiveImagePath = 'D://ML DL AI DSBDA//Railway Track Fault Detection//archive//Images//Defective' + nonDefectivePath = 'D://ML DL AI DSBDA//Railway Track Fault Detection//archive//Images//Non Defective' + changeNames(defectiveImagePath) + changeNames(nonDefectivePath) + changeDimensionsForDefectiveImages(256, 256) + changeDimensionsForNonDefectiveImages(256, 256) + + +if __name__ == '__main__': + main() diff --git a/Track Fault Detection using DL/README.md b/Track Fault Detection using DL/README.md new file mode 100644 index 000000000..96c746adf --- /dev/null +++ b/Track Fault Detection using DL/README.md @@ -0,0 +1,53 @@ +#

Track Fault Detection using DL

+## Aim of the project: +### The project focuses on detecting the faults in the railway tracks using various Deep Learning Algorithms +### Dataset Link: [railway Track fault Detection Resized (224 X 224) | Kaggle](https://www.kaggle.com/datasets/gpiosenka/railway-track-fault-detection-resized-224-x-224) + +### Libraries and Frameworks used: +1. Pandas +2. Numpy +3. Matplotlib +4. Seaborn +5. Tensorflow +6. Keras +7. visualkeras +8. pickle +9. pillow +10. opendatasets + + +## Deep Learning Algorithms used: +1. Vgg16 +2. ResNet50 +3. InceptionV3 +4. DenseNet +5. MobileNet + +## Accuracy and training time comparison of all the Deep Learning Algorithms +| | Accuracy | +|-------------|---------------| +| Vgg16 | 96% | +| ResNet50 | 71% | +| InceptionV3 | 94% | +| DenseNet | 98% | +| MobileNet | 97% | + +# Accuracy plots of all models + +## Vgg16 +![vgg16](https://github.com/the-silent-geek/DL-Simplified/blob/55596e0bfc60b5aba38f5bb64519fa9363dd5d61/Railway%20Track%20Fault%20Detection/images/vgg16.png) + +## ResNet50 +![resnet](https://github.com/the-silent-geek/DL-Simplified/blob/55596e0bfc60b5aba38f5bb64519fa9363dd5d61/Railway%20Track%20Fault%20Detection/images/ResNet.png) + +## InceptionV3 +![inception](https://github.com/the-silent-geek/DL-Simplified/blob/55596e0bfc60b5aba38f5bb64519fa9363dd5d61/Railway%20Track%20Fault%20Detection/images/inceptionV3.png) + +## DenseNet +![densenet](https://github.com/the-silent-geek/DL-Simplified/blob/55596e0bfc60b5aba38f5bb64519fa9363dd5d61/Railway%20Track%20Fault%20Detection/images/densenet.png) + +## MobileNet +![mobilenet](https://github.com/the-silent-geek/DL-Simplified/blob/55596e0bfc60b5aba38f5bb64519fa9363dd5d61/Railway%20Track%20Fault%20Detection/images/mobileNet.png) + +# Conclusion +DenseNet model performs better comparative to other models used on the above dataset. diff --git a/Track Fault Detection using DL/dataset/README.md b/Track Fault Detection using DL/dataset/README.md new file mode 100644 index 000000000..7338f7de2 --- /dev/null +++ b/Track Fault Detection using DL/dataset/README.md @@ -0,0 +1,3 @@ +## You can download the dataset from the following Link 馃敆 : + +### [https://www.kaggle.com/datasets/gpiosenka/railway-track-fault-detection-resized-224-x-224](url) \ No newline at end of file diff --git a/Track Fault Detection using DL/images/InceptionV3_model.png b/Track Fault Detection using DL/images/InceptionV3_model.png new file mode 100644 index 000000000..41b2b8185 Binary files /dev/null and b/Track Fault Detection using DL/images/InceptionV3_model.png differ diff --git a/Track Fault Detection using DL/images/ResNet.png b/Track Fault Detection using DL/images/ResNet.png new file mode 100644 index 000000000..64e47b148 Binary files /dev/null and b/Track Fault Detection using DL/images/ResNet.png differ diff --git a/Track Fault Detection using DL/images/cf_matrix_vgg16.png b/Track Fault Detection using DL/images/cf_matrix_vgg16.png new file mode 100644 index 000000000..82ee57c95 Binary files /dev/null and b/Track Fault Detection using DL/images/cf_matrix_vgg16.png differ diff --git a/Track Fault Detection using DL/images/densenet.png b/Track Fault Detection using DL/images/densenet.png new file mode 100644 index 000000000..da391178d Binary files /dev/null and b/Track Fault Detection using DL/images/densenet.png differ diff --git a/Track Fault Detection using DL/images/inceptionV3.png b/Track Fault Detection using DL/images/inceptionV3.png new file mode 100644 index 000000000..47a043634 Binary files /dev/null and b/Track Fault Detection using DL/images/inceptionV3.png differ diff --git a/Track Fault Detection using DL/images/mobileNet.png b/Track Fault Detection using DL/images/mobileNet.png new file mode 100644 index 000000000..08fa8bc46 Binary files /dev/null and b/Track Fault Detection using DL/images/mobileNet.png differ diff --git a/Track Fault Detection using DL/images/vgg16.png b/Track Fault Detection using DL/images/vgg16.png new file mode 100644 index 000000000..e0d8e57e0 Binary files /dev/null and b/Track Fault Detection using DL/images/vgg16.png differ diff --git a/Track Fault Detection using DL/model/model.ipynb b/Track Fault Detection using DL/model/model.ipynb new file mode 100644 index 000000000..ab736edf1 --- /dev/null +++ b/Track Fault Detection using DL/model/model.ipynb @@ -0,0 +1,2636 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "bDayGxRjLxRG" + }, + "outputs": [], + "source": [ + "#import the necessary libraries\n", + "import pickle\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import os,os.path\n", + "import visualkeras\n", + "import seaborn as sns\n", + "\n", + "from PIL import ImageFont, Image\n", + "from tensorflow.keras import Model\n", + "from keras.applications.inception_v3 import InceptionV3\n", + "from tensorflow.keras.layers import Dense, Dropout, Flatten, BatchNormalization, Conv2D, MaxPool2D, Reshape, BatchNormalization\n", + "from tensorflow.keras.preprocessing import image_dataset_from_directory\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from sklearn.model_selection import train_test_split\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from keras.utils import plot_model\n", + "import opendatasets as od\n", + "from tensorflow.keras.models import Sequential, Model\n", + "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\n", + "from sklearn.metrics import classification_report,confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "oumFkqN3L2CP" + }, + "outputs": [], + "source": [ + "datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nyK1RBQ6MGek", + "outputId": "23cc97bd-7da6-42e4-9501-9a415beb58df" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 299 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "train_gen = datagen.flow_from_directory(directory = '/content/railway-track-fault-detection-resized-224-x-224/train',\n", + " class_mode=\"categorical\",\n", + " target_size = (224, 224),\n", + " batch_size = 32,\n", + " color_mode='rgb',\n", + " seed = 1234,\n", + " shuffle = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xRzZGAAjMJXb", + "outputId": "aa99c7d2-9f8a-481f-870c-2fedd6a0e7e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n", + "Your Kaggle username: dammalaudaykiran\n", + "Your Kaggle Key: 路路路路路路路路路路\n", + "Downloading railway-track-fault-detection-resized-224-x-224.zip to ./railway-track-fault-detection-resized-224-x-224\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|鈻堚枅鈻堚枅鈻堚枅鈻堚枅鈻堚枅| 12.7M/12.7M [00:00<00:00, 14.7MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#For downloading dataset in google colab\n", + "# od.download('https://www.kaggle.com/datasets/gpiosenka/railway-track-fault-detection-resized-224-x-224/data')" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lUIzDy7mM2cu", + "outputId": "9b43ace7-7c74-4ce8-a1e4-1314d4ae8438" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 62 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "val_gen = datagen.flow_from_directory(directory = '/content/railway-track-fault-detection-resized-224-x-224/valid',\n", + " class_mode=\"categorical\",\n", + " target_size = (224, 224),\n", + " batch_size = 32,\n", + " color_mode='rgb',\n", + " seed = 1234,\n", + " shuffle = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "StEaa3oVNd3s", + "outputId": "b208c912-f728-4003-a3a5-aa432f9a5b9f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 22 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "test_gen = datagen.flow_from_directory(directory = '/content/railway-track-fault-detection-resized-224-x-224/test',\n", + " class_mode=\"categorical\",\n", + " target_size = (224, 224),\n", + " batch_size = 32,\n", + " color_mode='rgb',\n", + " shuffle = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "BxuyqPiwN0XK" + }, + "outputs": [], + "source": [ + "def reset_graph(seed=41):\n", + " tf.random.set_seed(seed)\n", + " np.random.seed(seed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### InceptionV3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "KceBnwnHN2mZ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From D:\\anaconda3\\Lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n", + "\n", + "WARNING:tensorflow:From D:\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\normalization\\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n", + "\n", + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n", + "96112376/96112376 [==============================] - 60s 1us/step\n" + ] + } + ], + "source": [ + "InceptionV3_model = InceptionV3(weights='imagenet')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101 + }, + "id": "h-Ij0LKJN5EV", + "outputId": "3e3d27d4-ab85-4298-ea3e-2612dc30e7fd" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#visual representation of inceptionV3 model\n", + "visualkeras.layered_view(InceptionV3_model, legend=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "slH5OVyUN9Lj" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"inception_v3\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 299, 299, 3)] 0 [] \n", + " \n", + " conv2d (Conv2D) (None, 149, 149, 32) 864 ['input_1[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 149, 149, 32) 96 ['conv2d[0][0]'] \n", + " Normalization) \n", + " \n", + " activation (Activation) (None, 149, 149, 32) 0 ['batch_normalization[0][0]'] \n", + " \n", + " conv2d_1 (Conv2D) (None, 147, 147, 32) 9216 ['activation[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 147, 147, 32) 96 ['conv2d_1[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_1 (Activation) (None, 147, 147, 32) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " conv2d_2 (Conv2D) (None, 147, 147, 64) 18432 ['activation_1[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 147, 147, 64) 192 ['conv2d_2[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_2 (Activation) (None, 147, 147, 64) 0 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 73, 73, 64) 0 ['activation_2[0][0]'] \n", + " D) \n", + " \n", + " conv2d_3 (Conv2D) (None, 73, 73, 80) 5120 ['max_pooling2d[0][0]'] \n", + " \n", + " batch_normalization_3 (Bat (None, 73, 73, 80) 240 ['conv2d_3[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_3 (Activation) (None, 73, 73, 80) 0 ['batch_normalization_3[0][0]'\n", + " ] \n", + " \n", + " conv2d_4 (Conv2D) (None, 71, 71, 192) 138240 ['activation_3[0][0]'] \n", + " \n", + " batch_normalization_4 (Bat (None, 71, 71, 192) 576 ['conv2d_4[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_4 (Activation) (None, 71, 71, 192) 0 ['batch_normalization_4[0][0]'\n", + " ] \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 35, 35, 192) 0 ['activation_4[0][0]'] \n", + " g2D) \n", + " \n", + " conv2d_8 (Conv2D) (None, 35, 35, 64) 12288 ['max_pooling2d_1[0][0]'] \n", + " \n", + " batch_normalization_8 (Bat (None, 35, 35, 64) 192 ['conv2d_8[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_8 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_8[0][0]'\n", + " ] \n", + " \n", + " conv2d_6 (Conv2D) (None, 35, 35, 48) 9216 ['max_pooling2d_1[0][0]'] \n", + " \n", + " conv2d_9 (Conv2D) (None, 35, 35, 96) 55296 ['activation_8[0][0]'] \n", + " \n", + " batch_normalization_6 (Bat (None, 35, 35, 48) 144 ['conv2d_6[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_9 (Bat (None, 35, 35, 96) 288 ['conv2d_9[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_6 (Activation) (None, 35, 35, 48) 0 ['batch_normalization_6[0][0]'\n", + " ] \n", + " \n", + " activation_9 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_9[0][0]'\n", + " ] \n", + " \n", + " average_pooling2d (Average (None, 35, 35, 192) 0 ['max_pooling2d_1[0][0]'] \n", + " Pooling2D) \n", + " \n", + " conv2d_5 (Conv2D) (None, 35, 35, 64) 12288 ['max_pooling2d_1[0][0]'] \n", + " \n", + " conv2d_7 (Conv2D) (None, 35, 35, 64) 76800 ['activation_6[0][0]'] \n", + " \n", + " conv2d_10 (Conv2D) (None, 35, 35, 96) 82944 ['activation_9[0][0]'] \n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " conv2d_11 (Conv2D) (None, 35, 35, 32) 6144 ['average_pooling2d[0][0]'] \n", + " \n", + " batch_normalization_5 (Bat (None, 35, 35, 64) 192 ['conv2d_5[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_7 (Bat (None, 35, 35, 64) 192 ['conv2d_7[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_10 (Ba (None, 35, 35, 96) 288 ['conv2d_10[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_11 (Ba (None, 35, 35, 32) 96 ['conv2d_11[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_5 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_5[0][0]'\n", + " ] \n", + " \n", + " activation_7 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_7[0][0]'\n", + " ] \n", + " \n", + " activation_10 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_10[0][0]\n", + " '] \n", + " \n", + " activation_11 (Activation) (None, 35, 35, 32) 0 ['batch_normalization_11[0][0]\n", + " '] \n", + " \n", + " mixed0 (Concatenate) (None, 35, 35, 256) 0 ['activation_5[0][0]', \n", + " 'activation_7[0][0]', \n", + " 'activation_10[0][0]', \n", + " 'activation_11[0][0]'] \n", + " \n", + " conv2d_15 (Conv2D) (None, 35, 35, 64) 16384 ['mixed0[0][0]'] \n", + " \n", + " batch_normalization_15 (Ba (None, 35, 35, 64) 192 ['conv2d_15[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_15 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_15[0][0]\n", + " '] \n", + " \n", + " conv2d_13 (Conv2D) (None, 35, 35, 48) 12288 ['mixed0[0][0]'] \n", + " \n", + " conv2d_16 (Conv2D) (None, 35, 35, 96) 55296 ['activation_15[0][0]'] \n", + " \n", + " batch_normalization_13 (Ba (None, 35, 35, 48) 144 ['conv2d_13[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_16 (Ba (None, 35, 35, 96) 288 ['conv2d_16[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_13 (Activation) (None, 35, 35, 48) 0 ['batch_normalization_13[0][0]\n", + " '] \n", + " \n", + " activation_16 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_16[0][0]\n", + " '] \n", + " \n", + " average_pooling2d_1 (Avera (None, 35, 35, 256) 0 ['mixed0[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_12 (Conv2D) (None, 35, 35, 64) 16384 ['mixed0[0][0]'] \n", + " \n", + " conv2d_14 (Conv2D) (None, 35, 35, 64) 76800 ['activation_13[0][0]'] \n", + " \n", + " conv2d_17 (Conv2D) (None, 35, 35, 96) 82944 ['activation_16[0][0]'] \n", + " \n", + " conv2d_18 (Conv2D) (None, 35, 35, 64) 16384 ['average_pooling2d_1[0][0]'] \n", + " \n", + " batch_normalization_12 (Ba (None, 35, 35, 64) 192 ['conv2d_12[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_14 (Ba (None, 35, 35, 64) 192 ['conv2d_14[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_17 (Ba (None, 35, 35, 96) 288 ['conv2d_17[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_18 (Ba (None, 35, 35, 64) 192 ['conv2d_18[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_12 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_12[0][0]\n", + " '] \n", + " \n", + " activation_14 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_14[0][0]\n", + " '] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " activation_17 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_17[0][0]\n", + " '] \n", + " \n", + " activation_18 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_18[0][0]\n", + " '] \n", + " \n", + " mixed1 (Concatenate) (None, 35, 35, 288) 0 ['activation_12[0][0]', \n", + " 'activation_14[0][0]', \n", + " 'activation_17[0][0]', \n", + " 'activation_18[0][0]'] \n", + " \n", + " conv2d_22 (Conv2D) (None, 35, 35, 64) 18432 ['mixed1[0][0]'] \n", + " \n", + " batch_normalization_22 (Ba (None, 35, 35, 64) 192 ['conv2d_22[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_22 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_22[0][0]\n", + " '] \n", + " \n", + " conv2d_20 (Conv2D) (None, 35, 35, 48) 13824 ['mixed1[0][0]'] \n", + " \n", + " conv2d_23 (Conv2D) (None, 35, 35, 96) 55296 ['activation_22[0][0]'] \n", + " \n", + " batch_normalization_20 (Ba (None, 35, 35, 48) 144 ['conv2d_20[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_23 (Ba (None, 35, 35, 96) 288 ['conv2d_23[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_20 (Activation) (None, 35, 35, 48) 0 ['batch_normalization_20[0][0]\n", + " '] \n", + " \n", + " activation_23 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_23[0][0]\n", + " '] \n", + " \n", + " average_pooling2d_2 (Avera (None, 35, 35, 288) 0 ['mixed1[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_19 (Conv2D) (None, 35, 35, 64) 18432 ['mixed1[0][0]'] \n", + " \n", + " conv2d_21 (Conv2D) (None, 35, 35, 64) 76800 ['activation_20[0][0]'] \n", + " \n", + " conv2d_24 (Conv2D) (None, 35, 35, 96) 82944 ['activation_23[0][0]'] \n", + " \n", + " conv2d_25 (Conv2D) (None, 35, 35, 64) 18432 ['average_pooling2d_2[0][0]'] \n", + " \n", + " batch_normalization_19 (Ba (None, 35, 35, 64) 192 ['conv2d_19[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_21 (Ba (None, 35, 35, 64) 192 ['conv2d_21[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_24 (Ba (None, 35, 35, 96) 288 ['conv2d_24[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_25 (Ba (None, 35, 35, 64) 192 ['conv2d_25[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_19 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_19[0][0]\n", + " '] \n", + " \n", + " activation_21 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_21[0][0]\n", + " '] \n", + " \n", + " activation_24 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_24[0][0]\n", + " '] \n", + " \n", + " activation_25 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_25[0][0]\n", + " '] \n", + " \n", + " mixed2 (Concatenate) (None, 35, 35, 288) 0 ['activation_19[0][0]', \n", + " 'activation_21[0][0]', \n", + " 'activation_24[0][0]', \n", + " 'activation_25[0][0]'] \n", + " \n", + " conv2d_27 (Conv2D) (None, 35, 35, 64) 18432 ['mixed2[0][0]'] \n", + " \n", + " batch_normalization_27 (Ba (None, 35, 35, 64) 192 ['conv2d_27[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_27 (Activation) (None, 35, 35, 64) 0 ['batch_normalization_27[0][0]\n", + " '] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " conv2d_28 (Conv2D) (None, 35, 35, 96) 55296 ['activation_27[0][0]'] \n", + " \n", + " batch_normalization_28 (Ba (None, 35, 35, 96) 288 ['conv2d_28[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_28 (Activation) (None, 35, 35, 96) 0 ['batch_normalization_28[0][0]\n", + " '] \n", + " \n", + " conv2d_26 (Conv2D) (None, 17, 17, 384) 995328 ['mixed2[0][0]'] \n", + " \n", + " conv2d_29 (Conv2D) (None, 17, 17, 96) 82944 ['activation_28[0][0]'] \n", + " \n", + " batch_normalization_26 (Ba (None, 17, 17, 384) 1152 ['conv2d_26[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_29 (Ba (None, 17, 17, 96) 288 ['conv2d_29[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_26 (Activation) (None, 17, 17, 384) 0 ['batch_normalization_26[0][0]\n", + " '] \n", + " \n", + " activation_29 (Activation) (None, 17, 17, 96) 0 ['batch_normalization_29[0][0]\n", + " '] \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 17, 17, 288) 0 ['mixed2[0][0]'] \n", + " g2D) \n", + " \n", + " mixed3 (Concatenate) (None, 17, 17, 768) 0 ['activation_26[0][0]', \n", + " 'activation_29[0][0]', \n", + " 'max_pooling2d_2[0][0]'] \n", + " \n", + " conv2d_34 (Conv2D) (None, 17, 17, 128) 98304 ['mixed3[0][0]'] \n", + " \n", + " batch_normalization_34 (Ba (None, 17, 17, 128) 384 ['conv2d_34[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_34 (Activation) (None, 17, 17, 128) 0 ['batch_normalization_34[0][0]\n", + " '] \n", + " \n", + " conv2d_35 (Conv2D) (None, 17, 17, 128) 114688 ['activation_34[0][0]'] \n", + " \n", + " batch_normalization_35 (Ba (None, 17, 17, 128) 384 ['conv2d_35[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_35 (Activation) (None, 17, 17, 128) 0 ['batch_normalization_35[0][0]\n", + " '] \n", + " \n", + " conv2d_31 (Conv2D) (None, 17, 17, 128) 98304 ['mixed3[0][0]'] \n", + " \n", + " conv2d_36 (Conv2D) (None, 17, 17, 128) 114688 ['activation_35[0][0]'] \n", + " \n", + " batch_normalization_31 (Ba (None, 17, 17, 128) 384 ['conv2d_31[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_36 (Ba (None, 17, 17, 128) 384 ['conv2d_36[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_31 (Activation) (None, 17, 17, 128) 0 ['batch_normalization_31[0][0]\n", + " '] \n", + " \n", + " activation_36 (Activation) (None, 17, 17, 128) 0 ['batch_normalization_36[0][0]\n", + " '] \n", + " \n", + " conv2d_32 (Conv2D) (None, 17, 17, 128) 114688 ['activation_31[0][0]'] \n", + " \n", + " conv2d_37 (Conv2D) (None, 17, 17, 128) 114688 ['activation_36[0][0]'] \n", + " \n", + " batch_normalization_32 (Ba (None, 17, 17, 128) 384 ['conv2d_32[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_37 (Ba (None, 17, 17, 128) 384 ['conv2d_37[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_32 (Activation) (None, 17, 17, 128) 0 ['batch_normalization_32[0][0]\n", + " '] \n", + " \n", + " activation_37 (Activation) (None, 17, 17, 128) 0 ['batch_normalization_37[0][0]\n", + " '] \n", + " \n", + " average_pooling2d_3 (Avera (None, 17, 17, 768) 0 ['mixed3[0][0]'] \n", + " gePooling2D) \n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " conv2d_30 (Conv2D) (None, 17, 17, 192) 147456 ['mixed3[0][0]'] \n", + " \n", + " conv2d_33 (Conv2D) (None, 17, 17, 192) 172032 ['activation_32[0][0]'] \n", + " \n", + " conv2d_38 (Conv2D) (None, 17, 17, 192) 172032 ['activation_37[0][0]'] \n", + " \n", + " conv2d_39 (Conv2D) (None, 17, 17, 192) 147456 ['average_pooling2d_3[0][0]'] \n", + " \n", + " batch_normalization_30 (Ba (None, 17, 17, 192) 576 ['conv2d_30[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_33 (Ba (None, 17, 17, 192) 576 ['conv2d_33[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_38 (Ba (None, 17, 17, 192) 576 ['conv2d_38[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_39 (Ba (None, 17, 17, 192) 576 ['conv2d_39[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_30 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_30[0][0]\n", + " '] \n", + " \n", + " activation_33 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_33[0][0]\n", + " '] \n", + " \n", + " activation_38 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_38[0][0]\n", + " '] \n", + " \n", + " activation_39 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_39[0][0]\n", + " '] \n", + " \n", + " mixed4 (Concatenate) (None, 17, 17, 768) 0 ['activation_30[0][0]', \n", + " 'activation_33[0][0]', \n", + " 'activation_38[0][0]', \n", + " 'activation_39[0][0]'] \n", + " \n", + " conv2d_44 (Conv2D) (None, 17, 17, 160) 122880 ['mixed4[0][0]'] \n", + " \n", + " batch_normalization_44 (Ba (None, 17, 17, 160) 480 ['conv2d_44[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_44 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_44[0][0]\n", + " '] \n", + " \n", + " conv2d_45 (Conv2D) (None, 17, 17, 160) 179200 ['activation_44[0][0]'] \n", + " \n", + " batch_normalization_45 (Ba (None, 17, 17, 160) 480 ['conv2d_45[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_45 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_45[0][0]\n", + " '] \n", + " \n", + " conv2d_41 (Conv2D) (None, 17, 17, 160) 122880 ['mixed4[0][0]'] \n", + " \n", + " conv2d_46 (Conv2D) (None, 17, 17, 160) 179200 ['activation_45[0][0]'] \n", + " \n", + " batch_normalization_41 (Ba (None, 17, 17, 160) 480 ['conv2d_41[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_46 (Ba (None, 17, 17, 160) 480 ['conv2d_46[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_41 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_41[0][0]\n", + " '] \n", + " \n", + " activation_46 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_46[0][0]\n", + " '] \n", + " \n", + " conv2d_42 (Conv2D) (None, 17, 17, 160) 179200 ['activation_41[0][0]'] \n", + " \n", + " conv2d_47 (Conv2D) (None, 17, 17, 160) 179200 ['activation_46[0][0]'] \n", + " \n", + " batch_normalization_42 (Ba (None, 17, 17, 160) 480 ['conv2d_42[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_47 (Ba (None, 17, 17, 160) 480 ['conv2d_47[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_42 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_42[0][0]\n", + " '] \n", + " \n", + " activation_47 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_47[0][0]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " '] \n", + " \n", + " average_pooling2d_4 (Avera (None, 17, 17, 768) 0 ['mixed4[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_40 (Conv2D) (None, 17, 17, 192) 147456 ['mixed4[0][0]'] \n", + " \n", + " conv2d_43 (Conv2D) (None, 17, 17, 192) 215040 ['activation_42[0][0]'] \n", + " \n", + " conv2d_48 (Conv2D) (None, 17, 17, 192) 215040 ['activation_47[0][0]'] \n", + " \n", + " conv2d_49 (Conv2D) (None, 17, 17, 192) 147456 ['average_pooling2d_4[0][0]'] \n", + " \n", + " batch_normalization_40 (Ba (None, 17, 17, 192) 576 ['conv2d_40[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_43 (Ba (None, 17, 17, 192) 576 ['conv2d_43[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_48 (Ba (None, 17, 17, 192) 576 ['conv2d_48[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_49 (Ba (None, 17, 17, 192) 576 ['conv2d_49[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_40 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_40[0][0]\n", + " '] \n", + " \n", + " activation_43 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_43[0][0]\n", + " '] \n", + " \n", + " activation_48 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_48[0][0]\n", + " '] \n", + " \n", + " activation_49 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_49[0][0]\n", + " '] \n", + " \n", + " mixed5 (Concatenate) (None, 17, 17, 768) 0 ['activation_40[0][0]', \n", + " 'activation_43[0][0]', \n", + " 'activation_48[0][0]', \n", + " 'activation_49[0][0]'] \n", + " \n", + " conv2d_54 (Conv2D) (None, 17, 17, 160) 122880 ['mixed5[0][0]'] \n", + " \n", + " batch_normalization_54 (Ba (None, 17, 17, 160) 480 ['conv2d_54[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_54 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_54[0][0]\n", + " '] \n", + " \n", + " conv2d_55 (Conv2D) (None, 17, 17, 160) 179200 ['activation_54[0][0]'] \n", + " \n", + " batch_normalization_55 (Ba (None, 17, 17, 160) 480 ['conv2d_55[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_55 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_55[0][0]\n", + " '] \n", + " \n", + " conv2d_51 (Conv2D) (None, 17, 17, 160) 122880 ['mixed5[0][0]'] \n", + " \n", + " conv2d_56 (Conv2D) (None, 17, 17, 160) 179200 ['activation_55[0][0]'] \n", + " \n", + " batch_normalization_51 (Ba (None, 17, 17, 160) 480 ['conv2d_51[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_56 (Ba (None, 17, 17, 160) 480 ['conv2d_56[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_51 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_51[0][0]\n", + " '] \n", + " \n", + " activation_56 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_56[0][0]\n", + " '] \n", + " \n", + " conv2d_52 (Conv2D) (None, 17, 17, 160) 179200 ['activation_51[0][0]'] \n", + " \n", + " conv2d_57 (Conv2D) (None, 17, 17, 160) 179200 ['activation_56[0][0]'] \n", + " \n", + " batch_normalization_52 (Ba (None, 17, 17, 160) 480 ['conv2d_52[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_57 (Ba (None, 17, 17, 160) 480 ['conv2d_57[0][0]'] \n", + " tchNormalization) \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " activation_52 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_52[0][0]\n", + " '] \n", + " \n", + " activation_57 (Activation) (None, 17, 17, 160) 0 ['batch_normalization_57[0][0]\n", + " '] \n", + " \n", + " average_pooling2d_5 (Avera (None, 17, 17, 768) 0 ['mixed5[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_50 (Conv2D) (None, 17, 17, 192) 147456 ['mixed5[0][0]'] \n", + " \n", + " conv2d_53 (Conv2D) (None, 17, 17, 192) 215040 ['activation_52[0][0]'] \n", + " \n", + " conv2d_58 (Conv2D) (None, 17, 17, 192) 215040 ['activation_57[0][0]'] \n", + " \n", + " conv2d_59 (Conv2D) (None, 17, 17, 192) 147456 ['average_pooling2d_5[0][0]'] \n", + " \n", + " batch_normalization_50 (Ba (None, 17, 17, 192) 576 ['conv2d_50[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_53 (Ba (None, 17, 17, 192) 576 ['conv2d_53[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_58 (Ba (None, 17, 17, 192) 576 ['conv2d_58[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_59 (Ba (None, 17, 17, 192) 576 ['conv2d_59[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_50 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_50[0][0]\n", + " '] \n", + " \n", + " activation_53 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_53[0][0]\n", + " '] \n", + " \n", + " activation_58 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_58[0][0]\n", + " '] \n", + " \n", + " activation_59 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_59[0][0]\n", + " '] \n", + " \n", + " mixed6 (Concatenate) (None, 17, 17, 768) 0 ['activation_50[0][0]', \n", + " 'activation_53[0][0]', \n", + " 'activation_58[0][0]', \n", + " 'activation_59[0][0]'] \n", + " \n", + " conv2d_64 (Conv2D) (None, 17, 17, 192) 147456 ['mixed6[0][0]'] \n", + " \n", + " batch_normalization_64 (Ba (None, 17, 17, 192) 576 ['conv2d_64[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_64 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_64[0][0]\n", + " '] \n", + " \n", + " conv2d_65 (Conv2D) (None, 17, 17, 192) 258048 ['activation_64[0][0]'] \n", + " \n", + " batch_normalization_65 (Ba (None, 17, 17, 192) 576 ['conv2d_65[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_65 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_65[0][0]\n", + " '] \n", + " \n", + " conv2d_61 (Conv2D) (None, 17, 17, 192) 147456 ['mixed6[0][0]'] \n", + " \n", + " conv2d_66 (Conv2D) (None, 17, 17, 192) 258048 ['activation_65[0][0]'] \n", + " \n", + " batch_normalization_61 (Ba (None, 17, 17, 192) 576 ['conv2d_61[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_66 (Ba (None, 17, 17, 192) 576 ['conv2d_66[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_61 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_61[0][0]\n", + " '] \n", + " \n", + " activation_66 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_66[0][0]\n", + " '] \n", + " \n", + " conv2d_62 (Conv2D) (None, 17, 17, 192) 258048 ['activation_61[0][0]'] \n", + " \n", + " conv2d_67 (Conv2D) (None, 17, 17, 192) 258048 ['activation_66[0][0]'] \n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " batch_normalization_62 (Ba (None, 17, 17, 192) 576 ['conv2d_62[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_67 (Ba (None, 17, 17, 192) 576 ['conv2d_67[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_62 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_62[0][0]\n", + " '] \n", + " \n", + " activation_67 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_67[0][0]\n", + " '] \n", + " \n", + " average_pooling2d_6 (Avera (None, 17, 17, 768) 0 ['mixed6[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_60 (Conv2D) (None, 17, 17, 192) 147456 ['mixed6[0][0]'] \n", + " \n", + " conv2d_63 (Conv2D) (None, 17, 17, 192) 258048 ['activation_62[0][0]'] \n", + " \n", + " conv2d_68 (Conv2D) (None, 17, 17, 192) 258048 ['activation_67[0][0]'] \n", + " \n", + " conv2d_69 (Conv2D) (None, 17, 17, 192) 147456 ['average_pooling2d_6[0][0]'] \n", + " \n", + " batch_normalization_60 (Ba (None, 17, 17, 192) 576 ['conv2d_60[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_63 (Ba (None, 17, 17, 192) 576 ['conv2d_63[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_68 (Ba (None, 17, 17, 192) 576 ['conv2d_68[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_69 (Ba (None, 17, 17, 192) 576 ['conv2d_69[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_60 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_60[0][0]\n", + " '] \n", + " \n", + " activation_63 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_63[0][0]\n", + " '] \n", + " \n", + " activation_68 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_68[0][0]\n", + " '] \n", + " \n", + " activation_69 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_69[0][0]\n", + " '] \n", + " \n", + " mixed7 (Concatenate) (None, 17, 17, 768) 0 ['activation_60[0][0]', \n", + " 'activation_63[0][0]', \n", + " 'activation_68[0][0]', \n", + " 'activation_69[0][0]'] \n", + " \n", + " conv2d_72 (Conv2D) (None, 17, 17, 192) 147456 ['mixed7[0][0]'] \n", + " \n", + " batch_normalization_72 (Ba (None, 17, 17, 192) 576 ['conv2d_72[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_72 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_72[0][0]\n", + " '] \n", + " \n", + " conv2d_73 (Conv2D) (None, 17, 17, 192) 258048 ['activation_72[0][0]'] \n", + " \n", + " batch_normalization_73 (Ba (None, 17, 17, 192) 576 ['conv2d_73[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_73 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_73[0][0]\n", + " '] \n", + " \n", + " conv2d_70 (Conv2D) (None, 17, 17, 192) 147456 ['mixed7[0][0]'] \n", + " \n", + " conv2d_74 (Conv2D) (None, 17, 17, 192) 258048 ['activation_73[0][0]'] \n", + " \n", + " batch_normalization_70 (Ba (None, 17, 17, 192) 576 ['conv2d_70[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_74 (Ba (None, 17, 17, 192) 576 ['conv2d_74[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_70 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_70[0][0]\n", + " '] \n", + " \n", + " activation_74 (Activation) (None, 17, 17, 192) 0 ['batch_normalization_74[0][0]\n", + " '] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " conv2d_71 (Conv2D) (None, 8, 8, 320) 552960 ['activation_70[0][0]'] \n", + " \n", + " conv2d_75 (Conv2D) (None, 8, 8, 192) 331776 ['activation_74[0][0]'] \n", + " \n", + " batch_normalization_71 (Ba (None, 8, 8, 320) 960 ['conv2d_71[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_75 (Ba (None, 8, 8, 192) 576 ['conv2d_75[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_71 (Activation) (None, 8, 8, 320) 0 ['batch_normalization_71[0][0]\n", + " '] \n", + " \n", + " activation_75 (Activation) (None, 8, 8, 192) 0 ['batch_normalization_75[0][0]\n", + " '] \n", + " \n", + " max_pooling2d_3 (MaxPoolin (None, 8, 8, 768) 0 ['mixed7[0][0]'] \n", + " g2D) \n", + " \n", + " mixed8 (Concatenate) (None, 8, 8, 1280) 0 ['activation_71[0][0]', \n", + " 'activation_75[0][0]', \n", + " 'max_pooling2d_3[0][0]'] \n", + " \n", + " conv2d_80 (Conv2D) (None, 8, 8, 448) 573440 ['mixed8[0][0]'] \n", + " \n", + " batch_normalization_80 (Ba (None, 8, 8, 448) 1344 ['conv2d_80[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_80 (Activation) (None, 8, 8, 448) 0 ['batch_normalization_80[0][0]\n", + " '] \n", + " \n", + " conv2d_77 (Conv2D) (None, 8, 8, 384) 491520 ['mixed8[0][0]'] \n", + " \n", + " conv2d_81 (Conv2D) (None, 8, 8, 384) 1548288 ['activation_80[0][0]'] \n", + " \n", + " batch_normalization_77 (Ba (None, 8, 8, 384) 1152 ['conv2d_77[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_81 (Ba (None, 8, 8, 384) 1152 ['conv2d_81[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_77 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_77[0][0]\n", + " '] \n", + " \n", + " activation_81 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_81[0][0]\n", + " '] \n", + " \n", + " conv2d_78 (Conv2D) (None, 8, 8, 384) 442368 ['activation_77[0][0]'] \n", + " \n", + " conv2d_79 (Conv2D) (None, 8, 8, 384) 442368 ['activation_77[0][0]'] \n", + " \n", + " conv2d_82 (Conv2D) (None, 8, 8, 384) 442368 ['activation_81[0][0]'] \n", + " \n", + " conv2d_83 (Conv2D) (None, 8, 8, 384) 442368 ['activation_81[0][0]'] \n", + " \n", + " average_pooling2d_7 (Avera (None, 8, 8, 1280) 0 ['mixed8[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_76 (Conv2D) (None, 8, 8, 320) 409600 ['mixed8[0][0]'] \n", + " \n", + " batch_normalization_78 (Ba (None, 8, 8, 384) 1152 ['conv2d_78[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_79 (Ba (None, 8, 8, 384) 1152 ['conv2d_79[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_82 (Ba (None, 8, 8, 384) 1152 ['conv2d_82[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_83 (Ba (None, 8, 8, 384) 1152 ['conv2d_83[0][0]'] \n", + " tchNormalization) \n", + " \n", + " conv2d_84 (Conv2D) (None, 8, 8, 192) 245760 ['average_pooling2d_7[0][0]'] \n", + " \n", + " batch_normalization_76 (Ba (None, 8, 8, 320) 960 ['conv2d_76[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_78 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_78[0][0]\n", + " '] \n", + " \n", + " activation_79 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_79[0][0]\n", + " '] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " activation_82 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_82[0][0]\n", + " '] \n", + " \n", + " activation_83 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_83[0][0]\n", + " '] \n", + " \n", + " batch_normalization_84 (Ba (None, 8, 8, 192) 576 ['conv2d_84[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_76 (Activation) (None, 8, 8, 320) 0 ['batch_normalization_76[0][0]\n", + " '] \n", + " \n", + " mixed9_0 (Concatenate) (None, 8, 8, 768) 0 ['activation_78[0][0]', \n", + " 'activation_79[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 8, 8, 768) 0 ['activation_82[0][0]', \n", + " 'activation_83[0][0]'] \n", + " \n", + " activation_84 (Activation) (None, 8, 8, 192) 0 ['batch_normalization_84[0][0]\n", + " '] \n", + " \n", + " mixed9 (Concatenate) (None, 8, 8, 2048) 0 ['activation_76[0][0]', \n", + " 'mixed9_0[0][0]', \n", + " 'concatenate[0][0]', \n", + " 'activation_84[0][0]'] \n", + " \n", + " conv2d_89 (Conv2D) (None, 8, 8, 448) 917504 ['mixed9[0][0]'] \n", + " \n", + " batch_normalization_89 (Ba (None, 8, 8, 448) 1344 ['conv2d_89[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_89 (Activation) (None, 8, 8, 448) 0 ['batch_normalization_89[0][0]\n", + " '] \n", + " \n", + " conv2d_86 (Conv2D) (None, 8, 8, 384) 786432 ['mixed9[0][0]'] \n", + " \n", + " conv2d_90 (Conv2D) (None, 8, 8, 384) 1548288 ['activation_89[0][0]'] \n", + " \n", + " batch_normalization_86 (Ba (None, 8, 8, 384) 1152 ['conv2d_86[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_90 (Ba (None, 8, 8, 384) 1152 ['conv2d_90[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_86 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_86[0][0]\n", + " '] \n", + " \n", + " activation_90 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_90[0][0]\n", + " '] \n", + " \n", + " conv2d_87 (Conv2D) (None, 8, 8, 384) 442368 ['activation_86[0][0]'] \n", + " \n", + " conv2d_88 (Conv2D) (None, 8, 8, 384) 442368 ['activation_86[0][0]'] \n", + " \n", + " conv2d_91 (Conv2D) (None, 8, 8, 384) 442368 ['activation_90[0][0]'] \n", + " \n", + " conv2d_92 (Conv2D) (None, 8, 8, 384) 442368 ['activation_90[0][0]'] \n", + " \n", + " average_pooling2d_8 (Avera (None, 8, 8, 2048) 0 ['mixed9[0][0]'] \n", + " gePooling2D) \n", + " \n", + " conv2d_85 (Conv2D) (None, 8, 8, 320) 655360 ['mixed9[0][0]'] \n", + " \n", + " batch_normalization_87 (Ba (None, 8, 8, 384) 1152 ['conv2d_87[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_88 (Ba (None, 8, 8, 384) 1152 ['conv2d_88[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_91 (Ba (None, 8, 8, 384) 1152 ['conv2d_91[0][0]'] \n", + " tchNormalization) \n", + " \n", + " batch_normalization_92 (Ba (None, 8, 8, 384) 1152 ['conv2d_92[0][0]'] \n", + " tchNormalization) \n", + " \n", + " conv2d_93 (Conv2D) (None, 8, 8, 192) 393216 ['average_pooling2d_8[0][0]'] \n", + " \n", + " batch_normalization_85 (Ba (None, 8, 8, 320) 960 ['conv2d_85[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_87 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_87[0][0]\n", + " '] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " activation_88 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_88[0][0]\n", + " '] \n", + " \n", + " activation_91 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_91[0][0]\n", + " '] \n", + " \n", + " activation_92 (Activation) (None, 8, 8, 384) 0 ['batch_normalization_92[0][0]\n", + " '] \n", + " \n", + " batch_normalization_93 (Ba (None, 8, 8, 192) 576 ['conv2d_93[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_85 (Activation) (None, 8, 8, 320) 0 ['batch_normalization_85[0][0]\n", + " '] \n", + " \n", + " mixed9_1 (Concatenate) (None, 8, 8, 768) 0 ['activation_87[0][0]', \n", + " 'activation_88[0][0]'] \n", + " \n", + " concatenate_1 (Concatenate (None, 8, 8, 768) 0 ['activation_91[0][0]', \n", + " ) 'activation_92[0][0]'] \n", + " \n", + " activation_93 (Activation) (None, 8, 8, 192) 0 ['batch_normalization_93[0][0]\n", + " '] \n", + " \n", + " mixed10 (Concatenate) (None, 8, 8, 2048) 0 ['activation_85[0][0]', \n", + " 'mixed9_1[0][0]', \n", + " 'concatenate_1[0][0]', \n", + " 'activation_93[0][0]'] \n", + " \n", + " avg_pool (GlobalAveragePoo (None, 2048) 0 ['mixed10[0][0]'] \n", + " ling2D) \n", + " \n", + " predictions (Dense) (None, 1000) 2049000 ['avg_pool[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 23851784 (90.99 MB)\n", + "Trainable params: 23817352 (90.86 MB)\n", + "Non-trainable params: 34432 (134.50 KB)\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "InceptionV3_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "NdJYPJqlOJ5F" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_model(InceptionV3_model, to_file= 'InceptionV3_model.png', show_shapes = True, show_layer_names=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "AWSS0g85OOBq" + }, + "outputs": [], + "source": [ + "#make the non-necessary layers untrainable\n", + "InceptionV3_model.trainable = False" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "C9KQ5n3SOWa4" + }, + "outputs": [], + "source": [ + "#dataset is imbalanced so as to balance all classes extra images are added to all classes\n", + "augmentation = tf.keras.Sequential([\n", + " tf.keras.layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n", + " tf.keras.layers.experimental.preprocessing.RandomRotation(0.15),\n", + " tf.keras.layers.experimental.preprocessing.RandomZoom(0.2),\n", + " tf.keras.layers.experimental.preprocessing.RandomContrast(0.15),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "MA-JtVlgOX58" + }, + "outputs": [], + "source": [ + "#adding layers which will be trained\n", + "inputs = InceptionV3_model.input\n", + "x = augmentation(inputs)\n", + "\n", + "x = BatchNormalization()(InceptionV3_model.output)\n", + "x = Dense(1024, activation='relu')(x)\n", + "x = Dropout(0.45, seed=1234)(x)\n", + "x = Dense(512, activation='relu')(x)\n", + "x = Dropout(0.45, seed=1235)(x)\n", + "x = Flatten()(x)\n", + "\n", + "outputs = Dense(2, activation='softmax')(x)\n", + "\n", + "model = Model(inputs=inputs, outputs=outputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "id": "YvG6slb9OaS9" + }, + "outputs": [], + "source": [ + "reset_graph(seed=10)\n", + "\n", + "#model compilation with categorical crossentropy \n", + "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3KLEtqvfOmhC", + "outputId": "26204fc5-3813-4077-b339-68514e4f10bf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "10/10 [==============================] - 10s 358ms/step - loss: 0.0598 - accuracy: 0.9766 - val_loss: 0.5443 - val_accuracy: 0.7581\n", + "Epoch 2/10\n", + "10/10 [==============================] - 2s 166ms/step - loss: 0.0715 - accuracy: 0.9599 - val_loss: 1.3461 - val_accuracy: 0.5968\n", + "Epoch 3/10\n", + "10/10 [==============================] - 2s 164ms/step - loss: 0.0623 - accuracy: 0.9766 - val_loss: 0.6427 - val_accuracy: 0.7258\n", + "Epoch 4/10\n", + "10/10 [==============================] - 2s 172ms/step - loss: 0.0557 - accuracy: 0.9799 - val_loss: 0.6250 - val_accuracy: 0.7097\n", + "Epoch 5/10\n", + "10/10 [==============================] - 2s 165ms/step - loss: 0.0365 - accuracy: 0.9799 - val_loss: 0.8838 - val_accuracy: 0.6935\n", + "Epoch 6/10\n", + "10/10 [==============================] - 2s 175ms/step - loss: 0.0439 - accuracy: 0.9799 - val_loss: 0.6503 - val_accuracy: 0.7097\n", + "Epoch 7/10\n", + "10/10 [==============================] - 2s 180ms/step - loss: 0.0285 - accuracy: 0.9933 - val_loss: 0.7772 - val_accuracy: 0.7419\n", + "Epoch 8/10\n", + "10/10 [==============================] - 2s 159ms/step - loss: 0.0389 - accuracy: 0.9799 - val_loss: 0.7092 - val_accuracy: 0.7258\n", + "Epoch 9/10\n", + "10/10 [==============================] - 2s 169ms/step - loss: 0.0385 - accuracy: 0.9866 - val_loss: 0.7993 - val_accuracy: 0.7258\n", + "Epoch 10/10\n", + "10/10 [==============================] - 2s 166ms/step - loss: 0.0461 - accuracy: 0.9900 - val_loss: 0.7161 - val_accuracy: 0.7097\n" + ] + } + ], + "source": [ + "#training the model on google colab tpu for faster training\n", + "model_training = model.fit(train_gen, epochs=10, verbose=1, validation_data=val_gen)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "A7fTwYZmOobp", + "outputId": "a8e57685-9ef3-4a2e-9c2a-36d6f039e8ec" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\n" + ], + "text/plain": [ + " loss accuracy val_loss val_accuracy\n", + "0 0.059752 0.976589 0.544311 0.758065\n", + "1 0.071512 0.959866 1.346113 0.596774\n", + "2 0.062273 0.976589 0.642673 0.725806\n", + "3 0.055664 0.979933 0.625000 0.709677\n", + "4 0.036533 0.979933 0.883786 0.693548\n", + "5 0.043914 0.979933 0.650283 0.709677\n", + "6 0.028535 0.993311 0.777179 0.741935\n", + "7 0.038877 0.979933 0.709158 0.725806\n", + "8 0.038461 0.986622 0.799275 0.725806\n", + "9 0.046054 0.989967 0.716076 0.709677" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#creating a dataframe for accuracies and losses\n", + "pd.DataFrame(model_training.history)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hEGU1qFqO6Km", + "outputId": "de3ba9b1-eb58-419f-bcaf-e95d59a1aa85" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 190ms/step - loss: 0.9151 - accuracy: 0.7273\n" + ] + } + ], + "source": [ + "#evaluating results on test set\n", + "results = model.evaluate(test_gen, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 449 + }, + "id": "piNVMxtsPTz3", + "outputId": "d2a37982-a17b-48e0-a6e7-ea0194935478" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting training vs val loss\n", + "plt.plot(model_training.history['loss'], label='training loss')\n", + "plt.plot(model_training.history['val_loss'], label='val loss')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "463xwWMEPotY", + "outputId": "245e75e4-017e-4a39-e3d3-23312abccabf" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting accuracies for each epoch\n", + "Epochs = [i+1 for i in range(len(model_training.history['accuracy']))]\n", + "\n", + "plt.plot(Epochs, model_training.history['accuracy'], label = 'training accuracy')\n", + "plt.plot(Epochs, model_training.history['val_accuracy'], label = 'validation accuracy')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "plt.title('Training and Validation Accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VGG16" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "cauEVxSpP8E-" + }, + "outputs": [], + "source": [ + "#importing pre trained vgg16 model for keras\n", + "from tensorflow.keras.applications.vgg16 import VGG16" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7_Udm803QOdQ", + "outputId": "a86795ad-417a-422e-bc51-a9d28e20f41f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 2s 0us/step\n" + ] + } + ], + "source": [ + "model_train = VGG16(input_shape=(224,224,3) , weights='imagenet', include_top=False)\n", + "\n", + "#make the non-necessary layers untrainable\n", + "for layer in model_train.layers:\n", + " layer.trainable = False" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "sDhJ6mN3QQub" + }, + "outputs": [], + "source": [ + "#reshaping and flattening the output\n", + "x = Reshape((7*7,512))(model_train.output)\n", + "x = Flatten()(x)\n", + "x = Dense(128)(x)\n", + "x = Dense(64)(x)\n", + "prediction = Dense(2, activation='softmax')(x)\n", + "\n", + "# create a model object\n", + "model = Model(inputs=model_train.input, outputs=prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "qoH6OBgfQY3R" + }, + "outputs": [], + "source": [ + "#model compilation\n", + "model.compile(optimizer = tf.optimizers.Adam(learning_rate=0.001) , loss = 'categorical_crossentropy' , metrics = ['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#saving the best model for future uses and use of dynamic learning rate\n", + "def train_model(model, train_gen, val_gen, epochs,name):\n", + " path_cp = name + 'weights.hdf5'\n", + " rlr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.1, patience = 4, verbose = 1,\n", + " min_delta = 1e-4, min_lr = 1e-4, mode = 'min', cooldown=1)\n", + " ckp = ModelCheckpoint(path_cp,monitor = 'val_loss',\n", + " verbose = 1, save_best_only = True, mode = 'min')\n", + " es = EarlyStopping(monitor = 'val_loss', min_delta = 1e-4, patience = 6,mode = 'min', restore_best_weights = True, verbose = 1)\n", + "\n", + " # Fitting the model\n", + " history = model.fit(train_generator,\n", + " epochs = epochs,\n", + " validation_data = validation_generator,\n", + " verbose = 1,\n", + " callbacks = [rlr,es,ckp])\n", + "\n", + " return history" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mIDQHKK0Qe3g", + "outputId": "89c91aa9-8147-4efc-c02e-3e64bf28d2a9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "10/10 [==============================] - ETA: 0s - loss: 0.0570 - accuracy: 0.9732\n", + "Epoch 1: val_loss improved from inf to 6.05060, saving model to vgg16_vowels_weights.hdf5\n", + "10/10 [==============================] - 2s 228ms/step - loss: 0.0570 - accuracy: 0.9732 - val_loss: 6.0506 - val_accuracy: 0.5645 - lr: 1.0000e-04\n", + "Epoch 2/20\n", + "10/10 [==============================] - ETA: 0s - loss: 0.0588 - accuracy: 0.9699\n", + "Epoch 2: val_loss did not improve from 6.05060\n", + "10/10 [==============================] - 1s 101ms/step - loss: 0.0588 - accuracy: 0.9699 - val_loss: 6.1385 - val_accuracy: 0.5645 - lr: 1.0000e-04\n", + "Epoch 3/20\n", + "10/10 [==============================] - ETA: 0s - loss: 0.0550 - accuracy: 0.9766\n", + "Epoch 3: val_loss did not improve from 6.05060\n", + "10/10 [==============================] - 1s 90ms/step - loss: 0.0550 - accuracy: 0.9766 - val_loss: 6.2230 - val_accuracy: 0.5645 - lr: 1.0000e-04\n", + "Epoch 4/20\n", + "10/10 [==============================] - ETA: 0s - loss: 0.0667 - accuracy: 0.9732\n", + "Epoch 4: val_loss did not improve from 6.05060\n", + "10/10 [==============================] - 1s 94ms/step - loss: 0.0667 - accuracy: 0.9732 - val_loss: 6.2754 - val_accuracy: 0.5806 - lr: 1.0000e-04\n", + "Epoch 5/20\n", + "10/10 [==============================] - ETA: 0s - loss: 0.0491 - accuracy: 0.9833\n", + "Epoch 5: val_loss did not improve from 6.05060\n", + "10/10 [==============================] - 1s 96ms/step - loss: 0.0491 - accuracy: 0.9833 - val_loss: 6.3478 - val_accuracy: 0.5806 - lr: 1.0000e-04\n", + "Epoch 6/20\n", + "10/10 [==============================] - ETA: 0s - loss: 0.0717 - accuracy: 0.9699\n", + "Epoch 6: val_loss did not improve from 6.05060\n", + "10/10 [==============================] - 1s 103ms/step - loss: 0.0717 - accuracy: 0.9699 - val_loss: 6.4439 - val_accuracy: 0.5806 - lr: 1.0000e-04\n", + "Epoch 7/20\n", + " 9/10 [==========================>...] - ETA: 0s - loss: 0.0483 - accuracy: 0.9826Restoring model weights from the end of the best epoch: 1.\n", + "\n", + "Epoch 7: val_loss did not improve from 6.05060\n", + "10/10 [==============================] - 1s 152ms/step - loss: 0.0514 - accuracy: 0.9799 - val_loss: 6.5442 - val_accuracy: 0.5806 - lr: 1.0000e-04\n", + "Epoch 7: early stopping\n" + ] + } + ], + "source": [ + "#model training\n", + "train_history = train_model(model, train_gen,val_gen, epochs = 20,name=\"vgg16\")" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TsnZnYX-QwE0", + "outputId": "303629ad-37f6-4b40-c198-7ffe3847634e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 3s 3s/step - loss: 3.6873 - accuracy: 0.6364\n" + ] + }, + { + "data": { + "text/plain": [ + "[3.6872618198394775, 0.6363636255264282]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#evalutaing model on test set\n", + "model.evaluate(test_gen)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "id": "VwXaQ_b4TALl" + }, + "outputs": [], + "source": [ + "#opening the saved file\n", + "with open('vgg16_history.pkl', 'wb') as file_pi:\n", + " pickle.dump(train_history.history, file_pi)\n", + "\n", + "#loading the history\n", + "with open('vgg16_history.pkl', \"rb\") as file_pi:\n", + " history = pickle.load(file_pi)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "5ybMw1cjTdxn", + "outputId": "c0da4bb3-9047-40bf-8c4a-9f34bc128dc3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting accuracies and losses\n", + "print(history.keys())\n", + "\n", + "# summarize history for accuracy\n", + "plt.plot(history['accuracy'])\n", + "plt.plot(history['val_accuracy'])\n", + "plt.title('model accuracy')\n", + "plt.ylabel('accuracy')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()\n", + "\n", + "# summarize history for loss\n", + "plt.plot(history['loss'])\n", + "plt.plot(history['val_loss'])\n", + "plt.title('model loss')\n", + "plt.ylabel('loss')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "id": "2-mlcpABTgIe" + }, + "outputs": [], + "source": [ + "#Predicted label\n", + "#function for calculating model metrics\n", + "def metric_visualization(generator):\n", + "\n", + " y_pred = model.predict(generator)\n", + " y_pred_label = np.argmax(y_pred,axis=1)\n", + "\n", + " #true labels\n", + " y_test = generator.classes\n", + "\n", + " class_report = classification_report(y_test,y_pred_label,target_names=['fault','no fault'],output_dict=True)\n", + " sns.heatmap(pd.DataFrame(class_report).iloc[:-1, :].T, annot=True,cmap='inferno')\n", + "\n", + " conf_matrix = confusion_matrix(y_test, y_pred_label)\n", + " plt.figure(figsize=(10,6))\n", + " fx=sns.heatmap(confusion_matrix(y_test,y_pred_label), annot=True, fmt=\".2f\",cmap=\"inferno\")\n", + " fx.set_title('Confusion Matrix \\n')\n", + " fx.set_xlabel('\\n Predicted Values\\n')\n", + " fx.set_ylabel('Actual Values\\n')\n", + " fx.xaxis.set_ticklabels(['fault','no fault'])\n", + " fx.yaxis.set_ticklabels(['fault','no fault'])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 941 + }, + "id": "KAQqEiH_TnTF", + "outputId": "4a3684aa-d003-4793-ad82-6f08e9673e1d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2/2 [==============================] - 0s 138ms/step\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "metric_visualization(val_gen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MobileNet" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vjvYecx8UhRW", + "outputId": "91a3a06a-61ed-4dbd-f4cb-6f62713c12b0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_4\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_7 (InputLayer) [(None, 224, 224, 3)] 0 \n", + " \n", + " mobilenetv2_1.00_224 (Func (None, 7, 7, 1280) 2257984 \n", + " tional) \n", + " \n", + " flatten_4 (Flatten) (None, 62720) 0 \n", + " \n", + " dense_11 (Dense) (None, 1024) 64226304 \n", + " \n", + " dropout_5 (Dropout) (None, 1024) 0 \n", + " \n", + " dense_12 (Dense) (None, 2) 2050 \n", + " \n", + "=================================================================\n", + "Total params: 66486338 (253.63 MB)\n", + "Trainable params: 64228354 (245.01 MB)\n", + "Non-trainable params: 2257984 (8.61 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "#importing mobileNet model from keras\n", + "model_mobile =tf.keras.applications.MobileNetV2(\n", + " include_top=False,\n", + " input_shape=(224,224,3),\n", + " weights='imagenet'\n", + ")\n", + "model_mobile.trainable = False\n", + "\n", + "# adding input and output layers\n", + "input_layer = tf.keras.layers.Input(shape=(224,224,3))\n", + "x = model_mobile(input_layer,training=False)\n", + "x = tf.keras.layers.Flatten()(x)\n", + "x = tf.keras.layers.Dense(1024,activation='relu')(x)\n", + "x = tf.keras.layers.Dropout(0.3)(x)\n", + "output = tf.keras.layers.Dense(2,activation='softmax')(x)\n", + "\n", + "model_mobile = tf.keras.Model(inputs=[input_layer],outputs=[output])\n", + "model_mobile.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "id": "ZnLbPSCPVlG5" + }, + "outputs": [], + "source": [ + "#adding earlystopping, dynamic leraning rate and callbacks\n", + "earlystop = EarlyStopping(patience=3)\n", + "reduce_learning_rate = ReduceLROnPlateau(monitor=\"val_accuracy\",\n", + " patience=2, verbose=1,\n", + " factor=0.5, min_lr=0.00001)\n", + "\n", + "callbacks = [earlystop,reduce_learning_rate]\n", + "model_mobile.compile(optimizer ='adam', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KUzr0P0FVpGY", + "outputId": "f9ff6837-99a7-4eee-946e-676459abd11f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "10/10 [==============================] - 7s 381ms/step - loss: 20.3424 - accuracy: 0.6355 - val_loss: 14.6614 - val_accuracy: 0.6290 - lr: 0.0010\n", + "Epoch 2/10\n", + "10/10 [==============================] - 1s 108ms/step - loss: 5.0411 - accuracy: 0.8294 - val_loss: 6.2230 - val_accuracy: 0.7097 - lr: 0.0010\n", + "Epoch 3/10\n", + "10/10 [==============================] - 1s 105ms/step - loss: 1.5283 - accuracy: 0.9164 - val_loss: 11.0796 - val_accuracy: 0.5645 - lr: 0.0010\n", + "Epoch 4/10\n", + "10/10 [==============================] - ETA: 0s - loss: 1.2743 - accuracy: 0.9097\n", + "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", + "10/10 [==============================] - 1s 125ms/step - loss: 1.2743 - accuracy: 0.9097 - val_loss: 9.3689 - val_accuracy: 0.6129 - lr: 0.0010\n", + "Epoch 5/10\n", + "10/10 [==============================] - 1s 87ms/step - loss: 0.1753 - accuracy: 0.9833 - val_loss: 7.4646 - val_accuracy: 0.6613 - lr: 5.0000e-04\n" + ] + } + ], + "source": [ + "#training model\n", + "history_mobile = model_mobile.fit(train_gen,epochs=10,validation_data=val_gen,callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DenseNet" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rsrFqsfGVt4e", + "outputId": "8ceb96a6-6238-47f4-aae0-f403ba963956" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "74836368/74836368 [==============================] - 3s 0us/step\n", + "Model: \"model_5\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_9 (InputLayer) [(None, 224, 224, 3)] 0 \n", + " \n", + " densenet201 (Functional) (None, 7, 7, 1920) 18321984 \n", + " \n", + " flatten_5 (Flatten) (None, 94080) 0 \n", + " \n", + " dense_13 (Dense) (None, 1024) 96338944 \n", + " \n", + " dropout_6 (Dropout) (None, 1024) 0 \n", + " \n", + " dense_14 (Dense) (None, 2) 2050 \n", + " \n", + "=================================================================\n", + "Total params: 114662978 (437.40 MB)\n", + "Trainable params: 96340994 (367.51 MB)\n", + "Non-trainable params: 18321984 (69.89 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "#importing DenseNet model from keras\n", + "model_DenseNet =tf.keras.applications.DenseNet201(\n", + " include_top=False,\n", + " input_shape=(224,224,3),\n", + " weights='imagenet'\n", + ")\n", + "model_DenseNet.trainable = False\n", + "\n", + "#adding input and output layers\n", + "input_layer = tf.keras.layers.Input(shape=(224,224,3))\n", + "x = model_DenseNet(input_layer,training=False)\n", + "x = tf.keras.layers.Flatten()(x)\n", + "x = tf.keras.layers.Dense(1024,activation='relu')(x)\n", + "x = tf.keras.layers.Dropout(0.3)(x)\n", + "output = tf.keras.layers.Dense(2,activation='softmax')(x)\n", + "\n", + "model_DenseNet = tf.keras.Model(inputs=[input_layer],outputs=[output])\n", + "model_DenseNet.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "id": "nPQ4UlZlWoJH" + }, + "outputs": [], + "source": [ + "#model compilation\n", + "model_DenseNet.compile(optimizer ='adam', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-MStDmyMWq2U", + "outputId": "98b00168-917f-4b57-86a2-f0154ac42041" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "10/10 [==============================] - 36s 2s/step - loss: 30.1655 - accuracy: 0.5786 - val_loss: 5.7831 - val_accuracy: 0.8065 - lr: 0.0010\n", + "Epoch 2/10\n", + "10/10 [==============================] - 2s 194ms/step - loss: 10.9542 - accuracy: 0.8094 - val_loss: 7.4936 - val_accuracy: 0.7258 - lr: 0.0010\n", + "Epoch 3/10\n", + "10/10 [==============================] - ETA: 0s - loss: 1.7575 - accuracy: 0.9164\n", + "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", + "10/10 [==============================] - 2s 192ms/step - loss: 1.7575 - accuracy: 0.9164 - val_loss: 3.4729 - val_accuracy: 0.8065 - lr: 0.0010\n", + "Epoch 4/10\n", + "10/10 [==============================] - 2s 194ms/step - loss: 0.9850 - accuracy: 0.9298 - val_loss: 3.0720 - val_accuracy: 0.8548 - lr: 5.0000e-04\n", + "Epoch 5/10\n", + "10/10 [==============================] - 2s 211ms/step - loss: 0.7650 - accuracy: 0.9565 - val_loss: 2.5934 - val_accuracy: 0.8710 - lr: 5.0000e-04\n", + "Epoch 6/10\n", + "10/10 [==============================] - 2s 196ms/step - loss: 0.8402 - accuracy: 0.9599 - val_loss: 3.3788 - val_accuracy: 0.8387 - lr: 5.0000e-04\n", + "Epoch 7/10\n", + "10/10 [==============================] - ETA: 0s - loss: 0.1283 - accuracy: 0.9799\n", + "Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n", + "10/10 [==============================] - 2s 197ms/step - loss: 0.1283 - accuracy: 0.9799 - val_loss: 3.0846 - val_accuracy: 0.7581 - lr: 5.0000e-04\n", + "Epoch 8/10\n", + "10/10 [==============================] - 2s 194ms/step - loss: 0.1476 - accuracy: 0.9833 - val_loss: 3.8846 - val_accuracy: 0.7419 - lr: 2.5000e-04\n" + ] + } + ], + "source": [ + "#model training\n", + "history_dense = model_DenseNet.fit(train_gen,epochs=10,validation_data=val_gen,callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "id": "ODmqdy69Wwsk", + "outputId": "075b76eb-de0e-48ff-a81f-75178a8fd9a9" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell for plots between training and validation accuracy\n", + "tr_acc = history_dense.history['accuracy']\n", + "tr_loss = history_dense.history['loss']\n", + "val_acc = history_dense.history['val_accuracy']\n", + "val_loss = history_dense.history['val_loss']\n", + "index_loss = np.argmin(val_loss)\n", + "val_lowest = val_loss[index_loss]\n", + "index_acc = np.argmax(val_acc)\n", + "acc_highest = val_acc[index_acc]\n", + "Epochs = [i+1 for i in range(len(tr_acc))]\n", + "loss_label = f'best epoch= {str(index_loss + 1)}'\n", + "acc_label = f'best epoch= {str(index_acc + 1)}'\n", + "\n", + "# Plot training history\n", + "plt.figure(figsize= (20, 8))\n", + "plt.style.use('fivethirtyeight')\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(Epochs, tr_loss, 'r', label= 'Training loss')\n", + "plt.plot(Epochs, val_loss, 'g', label= 'Validation loss')\n", + "plt.scatter(index_loss + 1, val_lowest, s= 150, c= 'blue', label= loss_label)\n", + "plt.title('Training and Validation Loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(Epochs, tr_acc, 'r', label= 'Training Accuracy')\n", + "plt.plot(Epochs, val_acc, 'g', label= 'Validation Accuracy')\n", + "plt.scatter(index_acc + 1 , acc_highest, s= 150, c= 'blue', label= acc_label)\n", + "plt.title('Training and Validation Accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ResNet" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RVGAAUNGXD1H", + "outputId": "a2fa5818-aded-48c3-c1c1-af01bba9778d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "94765736/94765736 [==============================] - 3s 0us/step\n", + "Model: \"model_6\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_11 (InputLayer) [(None, 224, 224, 3)] 0 \n", + " \n", + " resnet50 (Functional) (None, 7, 7, 2048) 23587712 \n", + " \n", + " flatten_6 (Flatten) (None, 100352) 0 \n", + " \n", + " dense_15 (Dense) (None, 1024) 102761472 \n", + " \n", + " dropout_7 (Dropout) (None, 1024) 0 \n", + " \n", + " dense_16 (Dense) (None, 2) 2050 \n", + " \n", + "=================================================================\n", + "Total params: 126351234 (481.99 MB)\n", + "Trainable params: 102763522 (392.01 MB)\n", + "Non-trainable params: 23587712 (89.98 MB)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "#importing ResNet50 pre trained model from keras\n", + "model_ResNet =tf.keras.applications.ResNet50(\n", + " include_top=False,\n", + " input_shape=(224,224,3),\n", + " weights='imagenet'\n", + ")\n", + "model_ResNet.trainable = False\n", + "\n", + "#adding input and output layers\n", + "input_layer = tf.keras.layers.Input(shape=(224,224,3))\n", + "x = model_ResNet(input_layer,training=False)\n", + "x = tf.keras.layers.Flatten()(x)\n", + "x = tf.keras.layers.Dense(1024,activation='relu')(x)\n", + "x = tf.keras.layers.Dropout(0.3)(x)\n", + "output = tf.keras.layers.Dense(2,activation='softmax')(x)\n", + "\n", + "model_ResNet = tf.keras.Model(inputs=[input_layer],outputs=[output])\n", + "model_ResNet.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "id": "dEGOlAfJZ5nT" + }, + "outputs": [], + "source": [ + "#model compilation\n", + "model_ResNet.compile(optimizer ='adam', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Iv2kxzBwaAgG", + "outputId": "f0482d99-3710-474f-c1e5-497a856452e8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "10/10 [==============================] - 13s 651ms/step - loss: 26.4228 - accuracy: 0.5452 - val_loss: 38.8760 - val_accuracy: 0.5000 - lr: 0.0010\n", + "Epoch 2/10\n", + "10/10 [==============================] - 2s 149ms/step - loss: 18.5464 - accuracy: 0.4816 - val_loss: 3.6244 - val_accuracy: 0.5000 - lr: 0.0010\n", + "Epoch 3/10\n", + "10/10 [==============================] - ETA: 0s - loss: 5.9754 - accuracy: 0.5686\n", + "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", + "10/10 [==============================] - 2s 150ms/step - loss: 5.9754 - accuracy: 0.5686 - val_loss: 8.6401 - val_accuracy: 0.5000 - lr: 0.0010\n", + "Epoch 4/10\n", + "10/10 [==============================] - 2s 153ms/step - loss: 4.3717 - accuracy: 0.6722 - val_loss: 1.6121 - val_accuracy: 0.5000 - lr: 5.0000e-04\n", + "Epoch 5/10\n", + "10/10 [==============================] - 2s 156ms/step - loss: 4.0773 - accuracy: 0.5886 - val_loss: 1.7545 - val_accuracy: 0.5806 - lr: 5.0000e-04\n", + "Epoch 6/10\n", + "10/10 [==============================] - 2s 148ms/step - loss: 2.4649 - accuracy: 0.6622 - val_loss: 1.1846 - val_accuracy: 0.5484 - lr: 5.0000e-04\n", + "Epoch 7/10\n", + "10/10 [==============================] - ETA: 0s - loss: 2.6479 - accuracy: 0.5953\n", + "Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n", + "10/10 [==============================] - 2s 165ms/step - loss: 2.6479 - accuracy: 0.5953 - val_loss: 1.1590 - val_accuracy: 0.5645 - lr: 5.0000e-04\n", + "Epoch 8/10\n", + "10/10 [==============================] - 2s 150ms/step - loss: 1.9162 - accuracy: 0.6589 - val_loss: 1.0250 - val_accuracy: 0.5645 - lr: 2.5000e-04\n", + "Epoch 9/10\n", + "10/10 [==============================] - ETA: 0s - loss: 1.7687 - accuracy: 0.6689\n", + "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n", + "10/10 [==============================] - 2s 152ms/step - loss: 1.7687 - accuracy: 0.6689 - val_loss: 1.7650 - val_accuracy: 0.5323 - lr: 2.5000e-04\n", + "Epoch 10/10\n", + "10/10 [==============================] - 2s 149ms/step - loss: 1.6088 - accuracy: 0.6622 - val_loss: 1.0575 - val_accuracy: 0.6129 - lr: 1.2500e-04\n" + ] + } + ], + "source": [ + "#model training\n", + "history_res = model_ResNet.fit(train_gen,epochs=10,validation_data=val_gen,callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell for plots between training and validation accuracy\n", + "tr_acc = history_res.history['accuracy']\n", + "tr_loss = history_res.history['loss']\n", + "val_acc = history_res.history['val_accuracy']\n", + "val_loss = history_res.history['val_loss']\n", + "index_loss = np.argmin(val_loss)\n", + "val_lowest = val_loss[index_loss]\n", + "index_acc = np.argmax(val_acc)\n", + "acc_highest = val_acc[index_acc]\n", + "Epochs = [i+1 for i in range(len(tr_acc))]\n", + "loss_label = f'best epoch= {str(index_loss + 1)}'\n", + "acc_label = f'best epoch= {str(index_acc + 1)}'\n", + "\n", + "# Plot training history\n", + "plt.figure(figsize= (20, 8))\n", + "plt.style.use('fivethirtyeight')\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(Epochs, tr_loss, 'r', label= 'Training loss')\n", + "plt.plot(Epochs, val_loss, 'g', label= 'Validation loss')\n", + "plt.scatter(index_loss + 1, val_lowest, s= 150, c= 'blue', label= loss_label)\n", + "plt.title('Training and Validation Loss')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(Epochs, tr_acc, 'r', label= 'Training Accuracy')\n", + "plt.plot(Epochs, val_acc, 'g', label= 'Validation Accuracy')\n", + "plt.scatter(index_acc + 1 , acc_highest, s= 150, c= 'blue', label= acc_label)\n", + "plt.title('Training and Validation Accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Track Fault Detection using DL/requirements.txt b/Track Fault Detection using DL/requirements.txt new file mode 100644 index 000000000..540112dda --- /dev/null +++ b/Track Fault Detection using DL/requirements.txt @@ -0,0 +1,10 @@ +Pandas +Numpy +Matplotlib +Seaborn +Tensorflow +Keras +visualkeras +pickle +pillow +opendatasets \ No newline at end of file