def main():
# data generator
data_generator = ImageDataGenerator(
featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=0,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=.1,
horizontal_flip=True)
model = CNN()
opt = optimizers.Adam(lr=0.0001)
# opt = optimizers.SGD(lr=0.001)
model.compile(opt, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
# callbacks
f = open(base_path + 'gender_classification_training.log', 'w')
f.close()
log_file_path = base_path + 'gender_classification_training.log'
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1, patience=int(patience/4), verbose=1)
trained_models = base_path + 'CNN.{epoch:02d}-{val_loss:.3f}-{val_acc:.2f}.hdf5'
# model_cp = ModelCheckpoint(trained_models, 'val_acc', verbose=1, save_best_only=True)
model_cp = ModelCheckpoint(trained_models, 'val_loss', verbose=1, save_best_only=True)
callbacks = [model_cp, csv_logger, early_stop, reduce_lr]
# load data
faces, labels = load_data(data_path)
print (len(faces))
print (len(labels))
faces = preprocess_input(faces)
order = np.argsort(np.random.random(len(faces)))
faces = faces[order]
labels = labels[order]
train_data, val_data = split_data(faces, labels, validation_split)
train_faces, train_labels = train_data
model.fit_generator(data_generator.flow(train_faces, train_labels, batch_size),
steps_per_epoch=len(train_faces)/batch_size,
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=val_data)
def run():
(X_train, y_train), (X_test, y_test) = datasets.load_data(img_rows=32,
img_cols=32)
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = CNN(input_shape=X_train.shape[1:], nb_classes=nb_classes)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
X_train = preprocess_input(X_train)
X_test = preprocess_input(X_test)
csv_logger = CSVLogger('../log/cnn.log')
checkpointer = ModelCheckpoint(filepath="/tmp/weights.hdf5",
monitor="val_acc",
verbose=1,
save_best_only=True)
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=
False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=
0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=
0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=
0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
datagen.fit(X_train)
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
callbacks=[csv_logger, checkpointer])
