new_model = build_model()
if pretrained_path is not None:
new_model.load_weights(pretrained_path)
adam = keras.optimizers.Adam(lr=1e-4,
beta_1=0.9,
beta_2=0.99,
epsilon=1e-08,
decay=5E-6)
# sgd = keras.optimizers.SGD(lr=1e-5, decay=1e-6, momentum=0.9, nesterov=True)
new_model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
# new_model.compile(optimizer=adam, loss=[focal_loss(alpha=.25, gamma=2)], metrics=['accuracy'])
print(new_model.summary())
# Final callbacks
callbacks = [tensor_board, model_checkpoint, early_stop, reduce_lr]
# Start Fine-tuning
new_model.fit_generator(train_gen(),
steps_per_epoch=num_train_samples // batch_size,
validation_data=valid_gen(),
validation_steps=num_valid_samples // batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
use_multiprocessing=True,
workers=int(get_available_cpus() * 0.80))
model_checkpoint = MyCbk(model)
else:
model = build_encoder_decoder()
final = build_refinement(model)
if pretrained_path is not None:
final.load_weights(pretrained_path)
else:
migrate_model(final)
decoder_target = tf.placeholder(dtype='float32',
shape=(None, None, None, None))
final.compile(optimizer='nadam',
loss=overall_loss,
target_tensors=[decoder_target])
print(final.summary())
# Final callbacks
callbacks = [tensor_board, model_checkpoint, early_stop, reduce_lr]
# Start Fine-tuning
final.fit_generator(train_gen(),
steps_per_epoch=num_train_samples // batch_size,
validation_data=valid_gen(),
validation_steps=num_valid_samples // batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
use_multiprocessing=True,
workers=int(get_available_cpus() / 2))
# count = 0
# for i in encoder_decoder.layers:
# count += 1
# print(count)
# for i in refinement.layers:
# count += 1
# print(count)
# sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
refinement.compile(optimizer='nadam', loss=alpha_prediction_loss)
print(refinement.summary())
# Summarize then go!
num_cpu = get_available_cpus()
workers = int(round(num_cpu / 2))
# Final callbacks
callbacks = [tensor_board, model_checkpoint, early_stop, reduce_lr]
# Start Fine-tuning
refinement.fit_generator(train_gen(),
steps_per_epoch=num_train_samples // batch_size,
validation_data=valid_gen(),
validation_steps=num_valid_samples // batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
use_multiprocessing=False,
workers=workers)
# rewrite the callback: saving through the original model and not the multi-gpu model.
model_checkpoint = MyCbk(model)
else:
new_model = build_model()
if pretrained_path is not None:
new_model.load_weights(pretrained_path)
sgd = keras.optimizers.SGD(lr=1e-5,
momentum=0.9,
nesterov=True,
decay=1e-6)
# adam = keras.optimizers.Adam(lr=0.001)
new_model.compile(optimizer=sgd, loss=triplet_loss)
print(new_model.summary())
# Final callbacks
callbacks = [tensor_board, model_checkpoint, early_stop, reduce_lr]
# Start Fine-tuning
new_model.fit_generator(DataGenSequence('train'),
steps_per_epoch=num_train_samples // batch_size,
validation_data=DataGenSequence('valid'),
validation_steps=num_lfw_valid_samples //
batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
use_multiprocessing=True,
workers=get_available_cpus() // 2)
else:
new_model = build_model()
if pretrained_path is not None:
new_model.load_weights(pretrained_path)
# finetune the whole network together.
for layer in new_model.layers:
layer.trainable = True
sgd = keras.optimizers.SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
new_model.compile(optimizer=sgd, loss=alpha_prediction_loss)
print(new_model.summary())
# Summarize then go!
num_cpu = get_available_cpus()
workers = int(round(num_cpu / 2))
# Final callbacks
callbacks = [tensor_board, model_checkpoint, early_stop, reduce_lr]
# Start Fine-tuning
new_model.fit_generator(train_gen(),
steps_per_epoch=num_train_samples // batch_size,
validation_data=valid_gen(),
validation_steps=num_valid_samples // batch_size,
epochs=epochs,
verbose=1,
callbacks=callbacks,
use_multiprocessing=True,
workers=workers
