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Diff for: dl_project_4_cifar_10_object_recognition_using_resnet50.py

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+# -*- coding: utf-8 -*-
+"""DL Project 4. CIFAR-10 Object Recognition using ResNet50.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1T46DXyngES0gbtxZrJ41mCzYS7ss3KUn
+"""
+
+!pip install kaggle
+
+# configuring the path of Kaggle.json file
+!mkdir -p ~/.kaggle
+!cp kaggle.json ~/.kaggle/
+!chmod 600 ~/.kaggle/kaggle.json
+
+# daatset api
+!kaggle competitions download -c cifar-10
+
+!ls
+
+# extracting the compessed Dataset
+from zipfile import ZipFile
+dataset = '/content/cifar-10.zip'
+
+with ZipFile(dataset,'r') as zip:
+  zip.extractall()
+  print('The dataset is extracted')
+
+!ls
+
+!pip install py7zr
+
+import py7zr
+
+archive = py7zr.SevenZipFile('/content/train.7z', mode='r')
+archive.extractall()     #archive.extractall(path='/content/Training Data')
+archive.close()
+
+!ls
+
+"""Importing the Dependencies"""
+
+import os
+import numpy as np
+import pandas as pd
+from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+from sklearn.model_selection import train_test_split
+
+filenames = os.listdir('/content/train')
+
+type(filenames)
+
+len(filenames)
+
+print(filenames[0:5])
+print(filenames[-5:])
+
+"""**Labels Processing**"""
+
+labels_df = pd.read_csv('/content/trainLabels.csv')
+
+labels_df.shape
+
+labels_df.head()
+
+labels_df[labels_df['id'] == 7796]
+
+labels_df.head(10)
+
+labels_df.tail(10)
+
+labels_df['label'].value_counts()
+
+labels_df['label']
+
+labels_dictionary = {'airplane':0, 'automobile':1, 'bird':2, 'cat':3, 'deer':4, 'dog':5, 'frog':6, 'horse':7, 'ship':8, 'truck':9}
+
+labels = [labels_dictionary[i] for i in labels_df['label']]
+
+print(labels[0:5])
+print(labels[-5:])
+
+# displaying sample image
+import cv2
+from google.colab.patches import cv2_imshow
+
+img = cv2.imread('/content/train/7796.png')
+cv2_imshow(img)
+
+# displaying sample image
+import cv2
+from google.colab.patches import cv2_imshow
+
+img = cv2.imread('/content/train/45888.png')
+cv2_imshow(img)
+
+labels_df[labels_df['id'] == 45888]
+
+labels_df.head()
+
+id_list = list(labels_df['id'])
+
+print(id_list[0:5])
+print(id_list[-5:])
+
+"""**Image Processing**"""
+
+# convert images to numpy arrays
+
+train_data_folder = '/content/train/'
+
+data = []
+
+for id in id_list:
+
+  image = Image.open(train_data_folder + str(id) + '.png')
+  image = np.array(image)
+  data.append(image)
+
+type(data)
+
+len(data)
+
+type(data[0])
+
+data[0].shape
+
+data[0]
+
+# convert image list and label list to numpy arrays
+
+X = np.array(data)
+Y = np.array(labels)
+
+type(X)
+
+print(X.shape)
+print(Y.shape)
+
+"""**Train Test Split**"""
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)
+
+print(X.shape, X_train.shape, X_test.shape)
+
+# scaling the data
+
+X_train_scaled = X_train/255
+
+X_test_scaled = X_test/255
+
+X_train_scaled
+
+X_train[0]
+
+"""**Building the Neural Network**"""
+
+import tensorflow as tf
+from tensorflow import keras
+
+num_of_classes = 10
+
+# setting up the layers of Neural Network
+
+model = keras.Sequential([
+
+    keras.layers.Flatten(input_shape=(32,32,3)),
+    keras.layers.Dense(64, activation='relu'),
+    keras.layers.Dense(num_of_classes, activation='softmax')
+])
+
+# compile the neural network
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['acc'])
+
+# training the neural network
+model.fit(X_train_scaled, Y_train, validation_split=0.1, epochs=10)
+
+"""**ResNet50**"""
+
+from tensorflow.keras import Sequential, models, layers
+from tensorflow.keras.layers import Dense, Dropout, Flatten
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.models import load_model
+from tensorflow.keras.models import Model
+from tensorflow.keras.applications.resnet50 import ResNet50
+from tensorflow.keras import optimizers
+
+convolutional_base = ResNet50(weights='imagenet', include_top=False, input_shape=(256,256,3))
+convolutional_base.summary()
+
+num_of_classes = 10
+
+model = models.Sequential()
+model.add(layers.UpSampling2D((2,2)))
+model.add(layers.UpSampling2D((2,2)))
+model.add(layers.UpSampling2D((2,2)))
+model.add(convolutional_base)
+model.add(layers.Flatten())
+model.add(layers.BatchNormalization())
+model.add(layers.Dense(128, activation='relu'))
+model.add(layers.Dropout(0.5))
+model.add(layers.BatchNormalization())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dropout(0.5))
+model.add(layers.BatchNormalization())
+model.add(layers.Dense(num_of_classes, activation='softmax'))
+
+model.compile(optimizer=optimizers.RMSprop(lr=2e-5), loss='sparse_categorical_crossentropy', metrics=['acc'])
+
+history = model.fit(X_train_scaled, Y_train, validation_split=0.1, epochs=10)
+
+loss, accuracy = model.evaluate(X_test_scaled, Y_test)
+print('Test Accuracy =', accuracy)
+
+h = history
+
+# plot the loss value
+plt.plot(h.history['loss'], label='train loss')
+plt.plot(h.history['val_loss'], label='validation loss')
+plt.legend()
+plt.show()
+
+# plot the accuracy value
+plt.plot(h.history['acc'], label='train accuracy')
+plt.plot(h.history['val_acc'], label='validation accuracy')
+plt.legend()
+plt.show()
+