model.py

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    rescale=1./255,
#     rotation_range=40,
#     width_shift_range=0.2,
#     height_shift_range=0.2,
#     zoom_range=0.2,
#     shear_range=0.2,
#     horizontal_flip=True,
#     fill_mode='nearest'
    )

TRAINING_DIR = 'CK+48'
train_generator = train_datagen.flow_from_directory(TRAINING_DIR,
                                                    class_mode='categorical',
                                                    target_size=(48, 48))

class FinalCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if(logs.get('acc') > 0.99):
            print('\nReached above accuracy of 0.99 so training is stopped')
            self.model.stop_training=True
callbacks = FinalCallback()


model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(48, 48, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu',padding='same'),
    tf.keras.layers.MaxPooling2D(2, 2),
    
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu',padding='same'),
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu',padding='same'),
    tf.keras.layers.MaxPooling2D(2, 2),
    
    tf.keras.layers.Flatten(),

    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    
    tf.keras.layers.Dense(128, activation='relu'),

    tf.keras.layers.Dense(4, activation='softmax')
])

model.summary()

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])

history = model.fit_generator(train_generator,
                              epochs=40,
                              verbose=1,callbacks = [callbacks])


model.save('CKmodel.h5')