| from tensorflow.keras.preprocessing.image import ImageDataGenerator
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| from tensorflow.keras.models import Sequential
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| from tensorflow.keras.layers import Dense, Dropout,Flatten
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| from tensorflow.keras.layers import Conv2D,MaxPooling2D
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| import os
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|
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| train_data_dir='C:/Users/radha manohar kumar/Downloads/Facial Emotion Recognition/Facial Emotion/data/train'
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| validation_data_dir='C:/Users/radha manohar kumar/Downloads/Facial Emotion Recognition/Facial Emotion/data/test'
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|
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| train_datagen = ImageDataGenerator(
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| rescale=1./255,
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| rotation_range=30,
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| shear_range=0.3,
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| zoom_range=0.3,
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| horizontal_flip=True,
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| fill_mode='nearest')
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|
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| validation_datagen = ImageDataGenerator(rescale=1./255)
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|
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| train_generator = train_datagen.flow_from_directory(
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| train_data_dir,
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| color_mode='grayscale',
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| target_size=(48, 48),
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| batch_size=32,
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| class_mode='categorical',
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| shuffle=True)
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|
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| validation_generator = validation_datagen.flow_from_directory(
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| validation_data_dir,
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| color_mode='grayscale',
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| target_size=(48, 48),
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| batch_size=32,
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| class_mode='categorical',
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| shuffle=True)
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|
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|
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| class_labels=['Angry','Disgust', 'Fear', 'Happy','Neutral','Sad','Smirk','Surprise']
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|
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| img, label = train_generator.__next__()
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|
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|
|
| model = Sequential()
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|
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| model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
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|
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| model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
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| model.add(MaxPooling2D(pool_size=(2, 2)))
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| model.add(Dropout(0.1))
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|
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| model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
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| model.add(MaxPooling2D(pool_size=(2, 2)))
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| model.add(Dropout(0.1))
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|
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| model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))
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| model.add(MaxPooling2D(pool_size=(2, 2)))
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| model.add(Dropout(0.1))
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|
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| model.add(Flatten())
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| model.add(Dense(512, activation='relu'))
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| model.add(Dropout(0.2))
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|
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| model.add(Dense(8, activation='softmax'))
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|
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| model.compile(optimizer = 'adam', loss='categorical_crossentropy', metrics=['accuracy'])
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| print(model.summary())
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|
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| train_path = "data/train"
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| test_path = "data/test"
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|
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| num_train_imgs = 0
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| for root, dirs, files in os.walk(train_path):
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|
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| num_train_imgs += len(files)
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|
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| num_test_imgs = 0
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| for root, dirs, files in os.walk(test_path):
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| num_test_imgs += len(files)
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|
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| print(num_train_imgs)
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| print(num_test_imgs)
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| epochs=100
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|
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| history=model.fit(train_generator,
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| steps_per_epoch=num_train_imgs//32,
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| epochs=epochs,
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| validation_data=validation_generator,
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| validation_steps=num_test_imgs//32)
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|
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| model.save('model.keras')
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|
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|
