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train.py
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from __future__ import print_function
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_dir = '/Users/durgeshthakur/Deep Learning Stuff/Emotion Classification/fer2013/train'
validation_data_dir = '/Users/durgeshthakur/Deep Learning Stuff/Emotion Classification/fer2013/validation'
train_datagen = 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,
fill_mode='nearest')
validation_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
color_mode='grayscale',
target_size=(img_rows,img_cols),
batch_size=batch_size,
class_mode='categorical',
shuffle=True)
validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
color_mode='grayscale',
target_size=(img_rows,img_cols),
batch_size=batch_size,
class_mode='categorical',
shuffle=True)
model = Sequential()
# Block-1
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(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))
# Block-2
model.add(Conv2D(64,(3,3),padding='same',kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
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))
# Block-3
model.add(Conv2D(128,(3,3),padding='same',kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
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))
# Block-4
model.add(Conv2D(256,(3,3),padding='same',kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
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))
# Block-5
model.add(Flatten())
model.add(Dense(64,kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Block-6
model.add(Dense(64,kernel_initializer='he_normal'))
model.add(Activation('elu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# Block-7
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_little_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=25
history=model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples//batch_size,
epochs=epochs,
callbacks=callbacks,
validation_data=validation_generator,
validation_steps=nb_validation_samples//batch_size)