def _fine_tuning(self):
self.freeze_top_layers()
self.model.compile(
loss='categorical_crossentropy',
optimizer=Adam(lr=1e-5),
metrics=['accuracy'])
self.model.summary()
train_data = self.get_train_datagen(rotation_range=30., shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
callbacks = self.get_callbacks(config.get_fine_tuned_weights_path(), patience=self.fine_tuning_patience)
if util.is_keras2():
self.model.fit_generator(
train_data,
steps_per_epoch=config.nb_train_samples / float(self.batch_size),
epochs=self.nb_epoch,
validation_data=self.get_validation_datagen(),
validation_steps=config.nb_validation_samples / float(self.batch_size),
callbacks=callbacks,
class_weight=self.class_weight)
else:
self.model.fit_generator(
train_data,
samples_per_epoch=config.nb_train_samples,
nb_epoch=self.nb_epoch,
validation_data=self.get_validation_datagen(),
nb_val_samples=config.nb_validation_samples,
callbacks=callbacks,
class_weight=self.class_weight)
self.model.save(config.get_model_path())
def _fine_tuning(self):
self.freeze_top_layers()
self.model.compile(
loss='categorical_crossentropy',
optimizer=Adam(lr=1e-5),
metrics=['accuracy'])
self.model.summary()
# save the model to png in order to visualize
plot_model(self.model, to_file='model1.png', show_shapes=True)
train_data = self.get_train_datagen(rotation_range=30., shear_range=0.2, zoom_range=0.2, horizontal_flip=True,
preprocessing_function=self.preprocess_input)
# early stopping
callbacks = self.get_callbacks(config.get_fine_tuned_weights_path(), patience=self.fine_tuning_patience)
if util.is_keras2():
self.model.fit_generator(
train_data,
steps_per_epoch=config.nb_train_samples / float(self.batch_size),
epochs=self.nb_epoch,
validation_data=self.get_validation_datagen(),
validation_steps=config.nb_validation_samples / float(self.batch_size),
callbacks=callbacks,
class_weight=self.class_weight)
else:
self.model.fit_generator(
train_data,
samples_per_epoch=config.nb_train_samples,
nb_epoch=self.nb_epoch,
validation_data=self.get_validation_datagen(),
nb_val_samples=config.nb_validation_samples,
callbacks=callbacks,
class_weight=self.class_weight)
self.model.save(config.get_model_path())
def _fine_tuning(self):
self.freeze_top_layers()
self.model.compile(
loss='binary_crossentropy',
optimizer=Adam(lr=1e-5),
#optimizer=SGD(lr=5e-6, momentum=0.9),
metrics=['binary_accuracy'])
train_data = self.get_train_datagen(
rescale=1. / 255,
rotation_range=60.,
#shear_range=0.2,
#zoom_range=0.2,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
vertical_flip=True)
callbacks = self.get_callbacks(config.get_fine_tuned_weights_path(),
patience=self.fine_tuning_patience)
if util.is_keras2():
hist = self.model.fit_generator(
train_data,
steps_per_epoch=config.nb_train_samples /
float(self.batch_size),
epochs=self.nb_epoch,
validation_data=self.get_validation_datagen(rescale=1. / 255),
#validation_data=self.get_validation_datagen(),
validation_steps=config.nb_validation_samples /
float(self.batch_size),
callbacks=callbacks,
class_weight=self.class_weight)
else:
hist = self.model.fit_generator(
train_data,
samples_per_epoch=config.nb_train_samples,
nb_epoch=self.nb_epoch,
validation_data=self.get_validation_datagen(),
nb_val_samples=config.nb_validation_samples,
callbacks=callbacks,
class_weight=self.class_weight)
print(hist.history)
util.save_history(history=hist, prefix=time.time())
self.model.save(config.get_model_path())
def _fine_tuning(self):
self.freeze_top_layers1()
train_data = self.get_train_datagen(
rotation_range=30.,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
preprocessing_function=self.preprocess_input)
checkpoint_dir = os.path.join(os.path.abspath('.'), 'checkpoint')
callbacks = self.get_callbacks(config.get_fine_tuned_weights_path(),
checkpoint_dir,
patience=self.fine_tuning_patience)
if util.is_keras2():
if config.isCenterLoss:
self.center_model.load_weights(
'/home/yuzhg/Inception-v3/trained/fine-tuned-best-inception-weights.h5',
by_name=True)
self.center_model.compile(loss=[
'categorical_crossentropy', lambda y_true, y_pred: y_pred
],
loss_weights=[1, 0.2],
metrics=['accuracy'],
optimizer=Adam(lr=1e-5))
self.center_model.summary()
self.history = self.center_model.fit_generator(
util.clone_y_generator(train_data),
steps_per_epoch=config.nb_train_samples /
float(self.batch_size),
epochs=self.nb_epoch,
validation_data=util.clone_y_generator(
self.get_validation_datagen()),
validation_steps=config.nb_validation_samples /
float(self.batch_size),
callbacks=callbacks,
class_weight=self.class_weight)
elif config.isTripletLoss:
self.triplet_model.load_weights(
'/home/yuzhg/Inception-v3/trained/fine-tuned-best-inception-weights.h5',
by_name=True)
#self.triplet_model.compile(loss=self.hard_triplet_loss, optimizer=Adam(lr=1e-5), metrics=['accuracy'])
self.triplet_model.compile(
optimizer=Adam(lr=1e-5),
loss=['categorical_crossentropy', self.hard_triplet_loss],
loss_weights=[1.0, 1.0],
metrics=['accuracy'])
self.triplet_model.summary()
valid_data = self.get_validation_datagen(
rotation_range=30.,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
preprocessing_function=self.preprocess_input)
# util.clone_y_generator1(train_data),
self.history = self.triplet_model.fit_generator(
#util.triplet_transformed_generator(train_data, 4096),
util.clone_y_generator1(train_data),
steps_per_epoch=config.nb_train_samples /
float(self.batch_size),
epochs=self.nb_epoch,
#validation_data=util.triplet_transformed_generator(valid_data, 4096),
validation_data=util.clone_y_generator1(valid_data),
validation_steps=config.nb_validation_samples /
float(self.batch_size),
callbacks=callbacks,
class_weight=self.class_weight)
else:
self.model.load_weights(
'/home/yuzhg/Inception-v3/trained/fine-tuned-best-inception-weights.h5',
by_name=True)
self.model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=1e-5),
metrics=['accuracy'])
self.model.summary()
self.history = self.model.fit_generator(
train_data,
steps_per_epoch=config.nb_train_samples /
float(self.batch_size),
epochs=self.nb_epoch,
validation_data=self.get_validation_datagen(
rotation_range=30.,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
preprocessing_function=self.preprocess_input),
validation_steps=config.nb_validation_samples /
float(self.batch_size),
callbacks=callbacks,
class_weight=self.class_weight)
# else:
# if config.isCenterLoss:
# self.center_model.compile(loss=['categorical_crossentropy', lambda y_true, y_pred:y_pred],
# loss_weights=[1, 0.2], metrics=['accuracy'],
# optimizer=Adam(lr=1e-5))
# self.center_model.summary()
# self.history = self.center_model.fit_generator(
# util.clone_y_generator(train_data),
# samples_per_epoch=config.nb_train_samples,
# nb_epoch=self.nb_epoch,
# validation_data=util.clone_y_generator(self.get_validation_datagen()),
# nb_val_samples=config.nb_validation_samples,
# callbacks=callbacks,
# class_weight=self.class_weight)
# elif config.isTripletLoss:
# self.triplet_model.compile(loss=triplet_loss, optimizer=Adam(lr=1e-5))
# self.triplet_model.summary()
# self.history = self.triplet_model.fit_generator(
# util.clone_y_generator(train_data),
# steps_per_epoch=config.nb_train_samples / float(self.batch_size),
# epochs=self.nb_epoch,
# validation_data=util.clone_y_generator(self.get_validation_datagen()),
# validation_steps=config.nb_validation_samples / float(self.batch_size),
# callbacks=callbacks,
# class_weight=self.class_weight
# )
# else:
# self.model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=1e-5), metrics=['accuracy'])
# self.model.summary()
# self.history = self.model.fit_generator(
# train_data,
# steps_per_epoch=config.nb_train_samples / float(self.batch_size),
# epochs=self.nb_epoch,
# validation_data=self.get_validation_datagen(),
# validation_steps=config.nb_validation_samples / float(self.batch_size),
# callbacks=callbacks,
# class_weight=self.class_weight
# )
if config.isCenterLoss:
#self.center_model.save_weights('vgg16-model-weights.h5')
self.center_model.save(config.get_model_path())
util.save_history(self.history, self.center_model)
elif config.isTripletLoss:
self.triplet_model.save(config.get_model_path())
util.save_history(self.history, self.triplet_model)
else:
self.model.save(config.get_model_path())
util.save_history(self.history, self.model)