vgg19_ct.py

# -*- coding: utf-8 -*-
"""VGG19_ct.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1kxgR7cmluctCIMesitVTHV5ps1ghcRtS
"""

from google.colab import drive
drive.mount('/content/drive')

# Installing Keras
# pip install --upgrade keras

# Part 1 - Building the CNN

# Importing the Keras libraries and packages

import tensorflow as tf

from tensorflow.keras.models import Model
import tensorflow as tf
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, AveragePooling2D, GlobalAveragePooling2D


# Initialising the CNN
classifier_base = tf.keras.applications.VGG19(input_shape = (64,64,3),
    include_top=False, weights = "imagenet"
)

# Compiling the CNN
classifier_base.trainable = False
classifier_base.summary()
x = classifier_base.output
x = GlobalAveragePooling2D()(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)


predictions = Dense(1, activation = 'sigmoid')(x)
classifier = Model(classifier_base.input,predictions)
classifier.summary()

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

tf.keras.utils.plot_model(
    classifier, to_file='model.png', show_shapes=False, show_layer_names=True,
    rankdir='TB', expand_nested=False, dpi=96
)

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/content/drive/My Drive/covid-dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/content/drive/My Drive/covid-dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
from keras.callbacks import  EarlyStopping

#early = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=20, verbose=1, mode='auto')
#,callbacks = early

classifier.fit(training_set,
                         epochs = 10,
                         validation_data = test_set)
classifier.save("vgg19.h5")
import matplotlib.pyplot as plt
plt.plot(classifier.history.history['accuracy'])
plt.plot(classifier.history.history['val_accuracy'])
plt.plot(classifier.history.history['loss'])
plt.plot(classifier.history.history['val_loss'])
plt.title("model accuracy")
plt.ylabel("Accuracy")
plt.xlabel("Epoch")
plt.legend(["Accuracy","Validation Accuracy","loss","Validation Loss"])
plt.show()