model_traning.py

import pandas as pd
import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense, Dropout, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint

train_dir = "/media/asmany/Drive_D/liveness/dataset/train"
valid_dir = "/media/asmany/Drive_D/liveness/dataset/test"
img_width, img_height = 224, 224 # Default input size for VGG16

conv_base = tf.keras.applications.VGG16(weights='imagenet',
                  include_top=False,
                  input_shape=(img_width, img_height, 3))

# Show architecture
#conv_base.summary()


datagen = ImageDataGenerator(rescale=1./255)
batch_size = 32

def extract_features(directory, sample_count):
    features = np.zeros(shape=(sample_count, 7, 7, 512))  # Must be equal to the output of the convolutional base
    labels = np.zeros(shape=(sample_count,2))
    # Preprocess data
    generator = datagen.flow_from_directory(directory,
                                            target_size=(img_width,img_height),
                                            batch_size = batch_size,
                                            class_mode='categorical')
    # Pass data through convolutional base
    i = 0
    for inputs_batch, labels_batch in generator:
        features_batch = conv_base.predict(inputs_batch)
        features[i * batch_size: (i + 1) * batch_size] = features_batch
        labels[i * batch_size: (i + 1) * batch_size] = labels_batch
        i += 1
        if i * batch_size >= sample_count:
            break
    return features, labels

train_features, train_labels = extract_features(train_dir, 8895)
validation_features, validation_labels = extract_features(valid_dir, 3719)

epochs = 150

model = Sequential()
model.add(GlobalAveragePooling2D(input_shape=(7,7,512)))
model.add(Dense(2, activation='softmax'))
model.summary()

checkpoint = ModelCheckpoint('model-{epoch:03d}-{acc:03f}-{val_acc:03f}.h5', verbose=1, monitor='val_loss',save_best_only=True, mode='auto')

# Compile model
from tensorflow.keras.optimizers import Adam
model.compile(optimizer=Adam(),
              loss='categorical_crossentropy',
              metrics=['acc'])

# Train model
history = model.fit(train_features, train_labels,
                    epochs=epochs,
                    batch_size=batch_size,
                    callbacks=[checkpoint],
                    validation_data=(validation_features, validation_labels))