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)