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emotion_detection.p

#data augmentation x_train=x_train.reshape((x_train.shape[0],48,48,1)) x_test=x_test.reshape((x_test.shape[0],48,48,1)) from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=60, shear_range=0.5, zoom_range=0.5, width_shift_range=0.5, height_shift_range=0.5, horizontal_flip=True, fill_mode=’nearest’) validation_datagen = ImageDataGenerator(rescale=1./255) train_datagen.fit(x_train) validation_datagen.fit(x_test)