expression.py

import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from keras.layers import Conv2D, MaxPooling2D
import os

num_classes = 5
img_rows, img_cols = 48,48
batch_size = 32

train_data = r'C:\Users\HP\Downloads\Facial exp recog\train'
val_data = r'C:\Users\HP\Downloads\Facial exp recog\validation'

train_data_gen= ImageDataGenerator(rescale=1./255, rotation_range=30, shear_range=0.3,
        zoom_range=0.3, width_shift_range=0.4, height_shift_range=0.4, horizontal_flip=True, vertical_flip=True)

val_data_gen = ImageDataGenerator(rescale=1./255)

train_gen = train_data_gen.flow_from_directory(train_data, color_mode='grayscale',target_size=(img_rows,img_cols),
        batch_size=batch_size, class_mode='categorical', shuffle=True)
val_gen = val_data_gen.flow_from_directory(val_data, color_mode='grayscale',target_size=(img_rows,img_cols),
        batch_size=batch_size, class_mode='categorical', shuffle=True)

model = Sequential()

#LayerSet1
model.add(Conv2D(32,(3,3),padding='same',kernel_initializer='he_normal', input_shape=(img_rows,img_cols,1)))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))

#LayerSet2
model.add(Conv2D(64,(3,3),padding='same',kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))

#LayerSet3
model.add(Conv2D(128,(3,3),padding='same',kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))

#LayerSet4
model.add(Conv2D(256,(3,3),padding='same',kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))

#LayerSet5
model.add(Flatten())
model.add(Dense(64,kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

#LayerSet6
model.add(Dense(64,kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))

#LayerSet7
model.add(Dense(num_classes,kernel_initializer='he_normal'))
model.add(Activation('softmax'))

print(model.summary())

from keras.optimizers import RMSprop,SGD,Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau

checkpoint = ModelCheckpoint('Emotion_vgg.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1)

earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, restore_best_weights=True)

reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, verbose=1, min_delta=0.0001)

callbacks = [earlystop,checkpoint,reduce_lr]

model.compile(loss='categorical_crossentropy', optimizer = Adam(lr=0.001), metrics=['accuracy'])

nb_train_samples = 24176
nb_validation_samples = 3006
epochs=5

history=model.fit_generator(train_data_gen, steps_per_epoch=nb_train_samples/batch_size, epochs=epochs, callbacks=callbacks,
        validation_data=val_data_gen, validation_steps=nb_validation_samples//batch_size)