def train_model(train_dir,
valid_dir,
name_optimizer='sgd',
learning_rate=1.0,
decay_learning_rate=1e-8,
all_trainable=False,
model_weights_path=None,
no_class=200,
batch_size=128,
epoch=20,
tensorboard_dir=None,
checkpoint_dir=None):
'''Train or retrain model.
Args:
train_dir: train dataset directory.
valid_dir: validation dataset directory.
name_optimizer: optimizer method.
learning_rate: learning rate.
decay_learning_rate: learning rate decay.
model_weights_path: path of keras model weights.
no_class: number of prediction classes.
batch_size: batch size.
epoch: training epoch.
tensorboard_dir: tensorboard logs directory.
If None, dismiss it.
checkpoint_dir: checkpoints directory.
If None, dismiss it.
Returns:
Training history.
'''
model_bcnn = buil_bcnn(all_trainable=all_trainable,
size_height=244,
size_width=244,
no_class=no_class,
name_optimizer=name_optimizer,
learning_rate=learning_rate,
decay_learning_rate=decay_learning_rate)
if model_weights_path:
model_bcnn.load_weights(model_weights_path)
# Load data
train_generator, valid_generator = build_generator(train_dir=train_dir,
valid_dir=valid_dir,
batch_size=batch_size)
# Callbacks
callbacks = []
if tensorboard_dir:
cb_tersoboard = TensorBoard(log_dir=tensorboard_dir,
histogram_freq=0,
batch_size=batch_size,
write_graph=False)
callbacks.append(cb_tersoboard)
if checkpoint_dir:
cb_checkpoint = ModelCheckpoint(os.path.join(
checkpoint_dir, 'Xception_model_{epoch:02d}-{val_acc:.3f}.h5'),
save_weights_only=True,
monitor='val_acc',
verbose=True)
callbacks.append(cb_checkpoint)
cb_reducer = ReduceLROnPlateau(monitor='val_acc',
factor=0.5,
patience=5,
min_lr=1e-6,
min_delta=1e-3)
cb_stopper = EarlyStopping(monitor='val_acc',
min_delta=1e-3,
patience=20,
verbose=0,
mode='auto')
callbacks += [cb_reducer, cb_stopper]
# Train
history = model_bcnn.fit_generator(train_generator,
steps_per_epoch=100,
epochs=epoch,
validation_data=valid_generator,
validation_steps=50,
callbacks=callbacks)
model_bcnn.save_weights('./new_model_weights.h5')
return history
def train_model(name_optimizer='sgd',
learning_rate=0.05,
decay_learning_rate=1e-9,
all_trainable=True,
model_weights_path=None,
no_class=10,
batch_size=BACTH_SIZE,
epoch=300,
tensorboard_dir=None,
checkpoint_dir=None):
'''Train or retrain model.
Args:
train_dir: train dataset directory.
valid_dir: validation dataset directory.
name_optimizer: optimizer method.
learning_rate: learning rate.
decay_learning_rate: learning rate decay.
model_weights_path: path of keras model weights.
no_class: number of prediction classes.
batch_size: batch size.
epoch: training epoch.
tensorboard_dir: tensorboard logs directory.
If None, dismiss it.
checkpoint_dir: checkpoints directory.
If None, dismiss it.
Returns:
Training history.
'''
model = buil_bcnn(all_trainable=all_trainable,
no_class=no_class,
name_optimizer=name_optimizer,
learning_rate=learning_rate,
decay_learning_rate=decay_learning_rate,
name_activation='softmax',
name_loss='categorical_crossentropy')
if model_weights_path:
model.load_weights(model_weights_path)
# Callbacks
callbacks = []
if tensorboard_dir:
cb_tersoboard = TensorBoard(log_dir=tensorboard_dir,
histogram_freq=0,
batch_size=batch_size,
write_graph=False)
callbacks.append(cb_tersoboard)
#if checkpoint_dir:
#cb_checkpoint = ModelCheckpoint(
#os.path.join(checkpoint_dir, 'model_{epoch:02d}-{val_acc:.3f}.h5'),
#save_weights_only=True,
#monitor='val_loss',
#verbose=True)
#callbacks.append(cb_checkpoint)
cb_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=5,
min_lr=1e-6,
min_delta=1e-3)
cb_stopper = EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=10,
verbose=0,
mode='auto')
callbacks += [cb_reducer, cb_stopper]
# Train
# save best model
filepath = "./checkpoint/sgd-weights-improvement-{epoch:02d}-{val_acc:.2f}.h5"
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_acc',
verbose=1,
save_best_only='True',
mode='max',
period=1)
callback_list = [cb_reducer, checkpoint]
train_generator = generate_batch_data_random(train_data, BACTH_SIZE,
encoder)
validation_generator = generate_batch_data_random(validation_data,
BACTH_SIZE, encoder)
#loader = DataLoader(datapath=data_dir)
# use generator
#datagen = loader.generate(batch_size)
#iterations = loader.train_size // batch_size
history = model.fit_generator(train_generator,
validation_data=validation_generator,
epochs=epoch,
steps_per_epoch=split_ratio / batch_size,
validation_steps=(50000 - split_ratio) /
batch_size,
callbacks=callback_list)
return history
test_datagen = ImageDataGenerator(rescale=1. / 255,
samplewise_center=True,
samplewise_std_normalization=True,
rotation_range=100,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='constant',
cval=0)
print(nbr_augmentation, 'Loading model and weights from training process ...')
model = buil_bcnn(all_trainable=False,
no_class=2,
name_optimizer='sgd',
learning_rate=0.0001,
decay_learning_rate=1e-8)
model.load_weights(weights_path)
for idx in range(nbr_augmentation):
print('{}th augmentation for testing ...'.format(idx))
random_seed = np.random.random_integers(0, 100000)
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
shuffle=False, # Important !!!
seed=random_seed,
classes=None,
def train_model(data_dir,
name_optimizer='sgd',
learning_rate=1.0,
decay_learning_rate=1e-8,
all_trainable=False,
model_weights_path=None,
no_class=100,
batch_size=32,
epoch=100,
tensorboard_dir=None,
checkpoint_dir=None):
'''Train or retrain model.
Args:
train_dir: train dataset directory.
valid_dir: validation dataset directory.
name_optimizer: optimizer method.
learning_rate: learning rate.
decay_learning_rate: learning rate decay.
model_weights_path: path of keras model weights.
no_class: number of prediction classes.
batch_size: batch size.
epoch: training epoch.
tensorboard_dir: tensorboard logs directory.
If None, dismiss it.
checkpoint_dir: checkpoints directory.
If None, dismiss it.
Returns:
Training history.
'''
model = buil_bcnn(all_trainable=all_trainable,
no_class=no_class,
name_optimizer=name_optimizer,
learning_rate=learning_rate,
decay_learning_rate=decay_learning_rate)
if model_weights_path:
model.load_weights(model_weights_path)
# Load data
#train_generator, valid_generator = build_generator(
#train_dir=train_dir,
#valid_dir=valid_dir,
#batch_size=batch_size)
# to do: use generator to save memory
loader = DataLoader(npypath=data_dir)
trainx, trainy, validx, validy = loader.trainx, loader.trainy, loader.testx, loader.testy
# Callbacks
callbacks = []
if tensorboard_dir:
cb_tersoboard = TensorBoard(log_dir=tensorboard_dir,
histogram_freq=0,
batch_size=batch_size,
write_graph=False)
callbacks.append(cb_tersoboard)
if checkpoint_dir:
cb_checkpoint = ModelCheckpoint(os.path.join(
checkpoint_dir, 'model_{epoch:02d}-{val_acc:.3f}.h5'),
save_weights_only=True,
monitor='val_loss',
verbose=True)
callbacks.append(cb_checkpoint)
cb_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=5,
min_lr=1e-6,
min_delta=1e-3)
cb_stopper = EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=10,
verbose=0,
mode='auto')
callbacks += [cb_reducer, cb_stopper]
# Train
#history = model.fit_generator(
#train_generator,
#epochs=epoch,
#validation_data=valid_generator,
#callbacks=callbacks)
history = model.fit(trainx,
trainy,
epochs=epoch,
batch_size=batch_size,
validation_data=(validx, validy),
verbose=1,
shuffle=True)
model.save_weights('./new_model_weights.h5')
return history