def main_vgg(argv):
# dataset = keras.datasets.cifar10
scale = 1
if len(argv) >= 3:
scale = float(argv[2])
(x_train, y_train), (x_test, y_test) = dataprocessing.preprocess(
scale=scale) # dataset.load_data()
mean, std = meanStd(x_train.astype('float32'), x_test.astype('float32'))
x_train = normalize(x_train.astype('float32'), mean, std)
x_test = normalize(x_test.astype('float32'), mean, std)
# y_train = keras.utils.to_categorical(y_train, num_classes=10)
# y_test = keras.utils.to_categorical(y_test, num_classes=10)
datagen = ImageDataGenerator(featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=15,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=False)
datagen.fit(x_train)
model = VGG(argv[1])
lr = 0.01
lr_decay = 1e-6
lr_drop = 20
def lr_scheduler(epoch):
return lr * (0.5**(epoch // lr_drop))
reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)
sgd = SGD(lr=lr, decay=lr_decay, momentum=0.9, nesterov=True)
model_cp = keras.callbacks.ModelCheckpoint("Model/{0}.model".format(
argv[1]))
model.compile(optimizer=sgd,
loss='binary_crossentropy',
metrics=['accuracy'])
print(model.summary())
batch_size = 128
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=int(argv[2]),
validation_data=(x_test, y_test),
callbacks=[reduce_lr, model_cp])
print(model.evaluate(x_test, y_test))
model.save("Model/{0}.model".format(argv[1]))
model.save_weights("Model/{0}.weights".format(argv[1]))
from vgg import VGG
import os
model = VGG()
print(model.summary())
weights = './model/emotion_best_weights.h5'
if os.path.exists(weights):
print("Loading weight")
model.load_weights(weights)
print("Saving model")
model.save("./model/emotion_recognition.h5")
from vgg import VGG
import os
model = VGG()
print(model.summary())
weights = './Xml_File/emotion_best_weights.h5'
if os.path.exists(weights):
print("Loading weight")
model.load_weights(weights)
print("Saving model")
model.save("./Xml_File/emotion_recognition.h5")