train.py

# import required packages
import cv2
import scipy
import keras
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
 

# Initialize image data generator with rescaling
train_data_gen = ImageDataGenerator(rescale=1./255)
validation_data_gen = ImageDataGenerator(rescale=1./255)

# Preprocess all test images
train_generator = train_data_gen.flow_from_directory(
        'data/train',
        target_size=(48, 48),
        batch_size=64 ,
        color_mode="grayscale",
        class_mode='categorical')

# Preprocess all train images
validation_generator = validation_data_gen.flow_from_directory(
        'data/test',
        target_size=(48, 48),
        batch_size=64 ,
        color_mode="grayscale",
        class_mode='categorical')

# create model structure
emotion_model = Sequential()

emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))

emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))

emotion_model.add(Flatten())
emotion_model.add(Dense(1024, activation='relu'))
emotion_model.add(Dropout(0.5))
emotion_model.add(Dense(4, activation='softmax'))

cv2.ocl.setUseOpenCL(False)

emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])

# Train the neural network/model
emotion_model_info = emotion_model.fit(
        train_generator,
        steps_per_epoch=4090 // 64,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=4090 // 64)

# save model structure in jason file
model_json = emotion_model.to_json()
with open("kishore_model.json", "w") as json_file:
    json_file.write(model_json)

# save trained model weight in .h5 file
emotion_model.save_weights('kishore_model.h5')