dl_project_4_cifar_10_object_recognition_using_resnet50.py

# -*- 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()