def trainModel(train_data_generator, val_data_generator, model, initial_epoch):
"""
Model training.
# Arguments
train_data_generator: Training data generated batch by batch.
val_data_generator: Validation data generated batch by batch.
model: A Model instance.
initial_epoch: Epoch from which training starts.
"""
# Configure training process
model.compile(
loss='binary_crossentropy',
optimizer=Adam(lr=resnet_models.lr_schedule(0, FLAGS.initial_lr)),
metrics=['binary_accuracy'],
loss_weights=np.ones((15, )).tolist())
# Save model with the lowest validation loss
weights_path = os.path.join(FLAGS.experiment_rootdir,
'weights_{epoch:03d}.h5')
writeBestModel = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
tensorboard = TensorBoard(log_dir="logs/{}".format(time()))
# Save training and validation losses.
logz.configure_output_dir(FLAGS.experiment_rootdir)
saveModelAndLoss = log_utils.MyCallback(filepath=FLAGS.experiment_rootdir)
# Train model
steps_per_epoch = int(
np.ceil(train_data_generator.samples / FLAGS.batch_size))
validation_steps = int(
np.ceil(val_data_generator.samples / FLAGS.batch_size)) - 1
lr_scheduler = LearningRateScheduler(resnet_models.lr_schedule)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
strTime = strftime("%Y%b%d_%Hh%Mm%Ss", localtime(time()))
# tensorboard = TensorBoard(log_dir="logs/{}".format(strTime), histogram_freq=10,
# batch_size=32, write_graph=False, write_grads=True,
# write_images=False, embeddings_freq=0,
# embeddings_layer_names=None, embeddings_metadata=None)
#tensorboard = TensorBoard(log_dir="logs/{}".format(strTime), histogram_freq=10)
tensorboard = TensorBoard(log_dir="logs/{}".format(strTime),
histogram_freq=0)
callbacks = [
writeBestModel, saveModelAndLoss, lr_reducer, lr_scheduler, tensorboard
]
model.fit_generator(train_data_generator,
epochs=FLAGS.epochs,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
validation_data=val_data_generator,
validation_steps=validation_steps,
initial_epoch=initial_epoch)
def trainModel(train_data_generator, val_data_generator, model, initial_epoch):
"""
Model training.
# Arguments
train_data_generator: Training data generated batch by batch.
val_data_generator: Validation data generated batch by batch.
model: Target image channels.
initial_epoch: Dimension of model output.
"""
# Initialize loss weights
##model.alpha = tf.Variable(1, trainable=False, name='alpha', dtype=tf.float32)
##model.beta = tf.Variable(0, trainable=False, name='beta', dtype=tf.float32)
model.beta = tf.Variable(1, trainable=False, name='beta', dtype=tf.float32)
# Initialize number of samples for hard-mining
##model.k_mse = tf.Variable(FLAGS.batch_size, trainable=False, name='k_mse', dtype=tf.int32)
model.k_entropy = tf.Variable(FLAGS.batch_size,
trainable=False,
name='k_entropy',
dtype=tf.int32)
optimizer = optimizers.Adam(decay=1e-5)
# Configure training process
##model.compile(loss=[utils.hard_mining_mse(model.k_mse),
## utils.hard_mining_entropy(model.k_entropy)],
## optimizer=optimizer, loss_weights=[model.alpha, model.beta])
model.compile(loss=utils.hard_mining_entropy(model.k_entropy),
optimizer=optimizer)
# Save model with the lowest validation loss
weights_path = os.path.join(FLAGS.experiment_rootdir,
'weights_{epoch:03d}.h5')
writeBestModel = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
# Save model every 'log_rate' epochs.
# Save training and validation losses.
logz.configure_output_dir(FLAGS.experiment_rootdir)
saveModelAndLoss = log_utils.MyCallback(filepath=FLAGS.experiment_rootdir,
period=FLAGS.log_rate,
batch_size=FLAGS.batch_size)
# Train model
steps_per_epoch = int(
np.ceil(train_data_generator.samples / FLAGS.batch_size))
validation_steps = int(
np.ceil(val_data_generator.samples / FLAGS.batch_size))
model.fit_generator(train_data_generator,
epochs=FLAGS.epochs,
steps_per_epoch=steps_per_epoch,
callbacks=[writeBestModel, saveModelAndLoss],
validation_data=val_data_generator,
validation_steps=validation_steps,
initial_epoch=initial_epoch)
def trainModel(train_data_generator, val_data_generator, model, initial_epoch):
"""
Model training.
# Arguments
train_data_generator: Training data generated batch by batch.
val_data_generator: Validation data generated batch by batch.
model: A Model instance.
initial_epoch: Epoch from which training starts.
"""
# Configure training process
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=cifar10_resnet_mod.lr_schedule(0)),
metrics=['categorical_accuracy'])
# Save model with the lowest validation loss
weights_path = os.path.join(FLAGS.experiment_rootdir,
'weights_{epoch:03d}.h5')
writeBestModel = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
# Save training and validation losses.
logz.configure_output_dir(FLAGS.experiment_rootdir)
saveModelAndLoss = log_utils.MyCallback(filepath=FLAGS.experiment_rootdir)
# Train model
steps_per_epoch = int(
np.ceil(train_data_generator.samples / FLAGS.batch_size))
validation_steps = int(
np.ceil(val_data_generator.samples / FLAGS.batch_size)) - 1
lr_scheduler = LearningRateScheduler(cifar10_resnet_mod.lr_schedule)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
# TENSORBOARD SIRVE PARA VISUALIZAR LOS RESULTADOS DE LAS ITERACIONES
# HASTA AQUI SE HARÁ UN PROCESO ITERATIVO QUE GUARDARÁ DE TODOS LOS CASOS
# EL MODELO CON EL MEJOR RESULTADO SOBRE LOS DATOS DE VALIDACIÓN
strTime = strftime("%Y%b%d_%Hh%Mm%Ss", localtime(time()))
tensorboard = TensorBoard(log_dir="logs/{}".format(strTime),
histogram_freq=0)
callbacks = [
writeBestModel, saveModelAndLoss, lr_reducer, lr_scheduler, tensorboard
]
model.fit_generator(train_data_generator,
epochs=FLAGS.epochs,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
validation_data=val_data_generator,
validation_steps=validation_steps,
initial_epoch=initial_epoch)
def train_model(train_generator, val_generator, model, initial_epoch):
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=lr_schedule(0)),
metrics=['accuracy'])
# Save model with the lowest validation loss
weights_path = os.path.join(FLAGS.experiment_rootdir,
'weights_{epoch:03d}.h5')
write_best_model = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
# Save training and validation losses.
logz.configure_output_dir(FLAGS.experiment_rootdir)
save_model_and_loss = log_utils.MyCallback(
filepath=FLAGS.experiment_rootdir)
# Train model
lr_scheduler = LearningRateScheduler(lr_schedule, verbose=FLAGS.verbose)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
verbose=FLAGS.verbose,
min_lr=0.5e-6)
# earlystopping = EarlyStopping(monitor='val_loss', patience=3, verbose=FLAGS.verbose)
str_time = strftime("%Y%b%d_%Hh%Mm%Ss", localtime(time()))
tensorboard = TensorBoard(log_dir="logs/{}".format(str_time),
histogram_freq=0)
callbacks = [
write_best_model, save_model_and_loss, lr_reducer, lr_scheduler,
tensorboard
]
model.fit_generator(train_generator,
validation_data=val_generator,
epochs=FLAGS.epochs,
verbose=FLAGS.verbose,
callbacks=callbacks,
initial_epoch=initial_epoch,
use_multiprocessing=True)
def trainModel(train_data_generator, val_data_generator, model, initial_epoch):
"""
Model training.
# Arguments
train_data_generator: Training data generated batch by batch.
val_data_generator: Validation data generated batch by batch.
model: A Model instance.
initial_epoch: Epoch from which training starts.
"""
# Configure training process
optimizer = keras.optimizers.Adam(lr=FLAGS.initial_lr, decay=1e-6)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['binary_accuracy'],
loss_weights=np.ones((21, )).tolist())
# Save model with the lowest validation loss
weights_path = os.path.join(FLAGS.experiment_rootdir,
'weights_{epoch:03d}.h5')
writeBestModel = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
tensorboard = TensorBoard(log_dir="logs/{}".format(time()))
# Save training and validation losses.
logz.configure_output_dir(FLAGS.experiment_rootdir)
saveModelAndLoss = log_utils.MyCallback(filepath=FLAGS.experiment_rootdir)
# Train model
steps_per_epoch = int(
np.ceil(train_data_generator.samples / FLAGS.batch_size))
validation_steps = int(
np.ceil(val_data_generator.samples / FLAGS.batch_size)) - 1
model.fit_generator(
train_data_generator,
epochs=FLAGS.epochs,
steps_per_epoch=steps_per_epoch,
callbacks=[writeBestModel, saveModelAndLoss, tensorboard],
validation_data=val_data_generator,
validation_steps=validation_steps,
initial_epoch=initial_epoch)
def trainModel(train_data_generator, val_data_generator, model, initial_epoch):
"""
Model training.
# Arguments
train_data_generator: Training data generated batch by batch.
val_data_generator: Validation data generated batch by batch.
model: Target image channels.
initial_epoch: Dimension of model output.
"""
# Initialize loss weights
model.alpha = tf.Variable(1,
trainable=False,
name='alpha',
dtype=tf.float32)
model.beta = tf.Variable(1,
trainable=False,
name='betabeta',
dtype=tf.float32)
# Initialize number of samples for hard-mining
model.k_mse = tf.Variable(FLAGS.batch_size,
trainable=False,
name='k_mse',
dtype=tf.int32)
model.k_entropy = tf.Variable(FLAGS.batch_size,
trainable=False,
name='k_entropy',
dtype=tf.int32)
optimizer = optimizers.Adam(lr=0.00001, decay=1e-5)
# optimizer = optimizers.Nadam(lr=0.000002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004)
# optimizer = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-6)
# Configure training process
# model.compile(loss=[utils.mean_log_Gaussian_like_with_sigma_supress,
# utils.hard_mining_mse_translation(model.k_mse)],
# optimizer=optimizer, loss_weights=[model.alpha, model.beta])
model.compile(loss=[
utils.mean_log_Gaussian_like_with_sigma_supress,
utils.hard_mining_mse_translation(model.k_mse)
],
optimizer=optimizer,
loss_weights=[model.alpha, model.beta],
metrics={
'direct_output': utils.direction_acc,
'trans_output': utils.trans_acc
})
s_time = time.strftime("%Y%m%d%H%M%S", time.localtime()) # time stamp
# logs dir
logs_path = './logs/log_%s' % (s_time)
try:
os.makedirs(logs_path)
except:
pass
# 将loss ，acc， val_loss ,val_acc记录tensorboard
tensorboard = TensorBoard(log_dir=logs_path, write_graph=True)
# Save model with the lowest validation loss
weights_path = os.path.join(FLAGS.experiment_rootdir,
'weights_{epoch:03d}.h5')
writeBestModel = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
# Save model every 'log_rate' epochs.
# Save training and validation losses.
logz.configure_output_dir(FLAGS.experiment_rootdir)
saveModelAndLoss = log_utils.MyCallback(filepath=FLAGS.experiment_rootdir,
period=FLAGS.log_rate,
batch_size=FLAGS.batch_size)
# Train model
steps_per_epoch = int(
np.ceil(train_data_generator.samples / FLAGS.batch_size))
validation_steps = int(
np.ceil(val_data_generator.samples / FLAGS.batch_size))
model.fit_generator(
train_data_generator,
epochs=FLAGS.epochs,
steps_per_epoch=steps_per_epoch,
callbacks=[writeBestModel, saveModelAndLoss, tensorboard],
validation_data=val_data_generator,
validation_steps=validation_steps,
initial_epoch=initial_epoch)
model.k_mse = tf.Variable(32, trainable=False, name='k_mse', dtype=tf.int32)
#model.k_entropy = tf.Variable(32, trainable=False, name='k_entropy', dtype=tf.int32)
model.compile(
loss=[utils.hard_mining_mse(model.k_mse)],
#utils.hard_mining_entropy(model.k_entropy)],
optimizer=optimizer,
loss_weights=[model.alpha])
#loss_weights=[model.alpha, model.beta])
# Save model with the lowest validation loss
weights_path = os.path.join(OUTPUT_PATH, 'weights_{epoch:03d}.h5')
writeBestModel = ModelCheckpoint(filepath=weights_path,
monitor='val_loss',
save_best_only=True,
save_weights_only=True)
# Save model every 'log_rate' epochs.
# Save training and validation losses.
logz.configure_output_dir(OUTPUT_PATH)
saveModelAndLoss = log_utils.MyCallback(filepath=OUTPUT_PATH,
period=10,
batch_size=32)
model.fit_generator(train_data_generator,
steps_per_epoch=15,
epochs=50,
callbacks=[writeBestModel, saveModelAndLoss],
validation_data=test_data_generator,
validation_steps=5,
verbose=True)
