def prepare_for_training(self, optimizer=None):
"""Prepare for neural network training.
Compiles the model and creates
`Keras Callbacks <https://keras.io/callbacks/>`_ to be used for training.
Note that this method will be implicitly called once by :func:`train`
(with default arguments) if not done so explicitly beforehand.
Parameters
----------
optimizer : obj or None
Instance of a `Keras Optimizer <https://keras.io/optimizers/>`_ to be used for training.
If ``None`` (default), uses ``Adam`` with the learning rate specified in ``config``.
"""
if optimizer is None:
optimizer = Adam(lr=self.config.train_learning_rate)
masked_dist_loss = {'mse': masked_loss_mse, 'mae': masked_loss_mae}[self.config.train_dist_loss]
prob_loss = 'binary_crossentropy'
def split_dist_true_mask(dist_true_mask):
return tf.split(dist_true_mask, num_or_size_splits=[self.config.n_rays,-1], axis=-1)
def dist_loss(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_dist_loss(dist_mask, reg_weight=self.config.train_background_reg)(dist_true, dist_pred)
def relevant_mae(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_metric_mae(dist_mask)(dist_true, dist_pred)
def relevant_mse(dist_true_mask, dist_pred):
dist_true, dist_mask = split_dist_true_mask(dist_true_mask)
return masked_metric_mse(dist_mask)(dist_true, dist_pred)
self.keras_model.compile(optimizer, loss=[prob_loss, dist_loss],
loss_weights = list(self.config.train_loss_weights),
metrics={'prob': kld, 'dist': [relevant_mae, relevant_mse]})
self.callbacks = []
if self.basedir is not None:
self.callbacks += self._checkpoint_callbacks()
if self.config.train_tensorboard:
if IS_TF_1:
self.callbacks.append(CARETensorBoard(log_dir=str(self.logdir), prefix_with_timestamp=False, n_images=3, write_images=True, prob_out=False))
else:
self.callbacks.append(TensorBoard(log_dir=str(self.logdir/'logs'), write_graph=False, profile_batch=0))
if self.config.train_reduce_lr is not None:
rlrop_params = self.config.train_reduce_lr
if 'verbose' not in rlrop_params:
rlrop_params['verbose'] = True
# TF2: add as first callback to put 'lr' in the logs for TensorBoard
self.callbacks.insert(0,ReduceLROnPlateau(**rlrop_params))
self._model_prepared = True
def prepare_for_training(self, optimizer=None):
"""Prepare for neural network training.
Compiles the model and creates
`Keras Callbacks <https://keras.io/callbacks/>`_ to be used for training.
Note that this method will be implicitly called once by :func:`train`
(with default arguments) if not done so explicitly beforehand.
Parameters
----------
optimizer : obj or None
Instance of a `Keras Optimizer <https://keras.io/optimizers/>`_ to be used for training.
If ``None`` (default), uses ``Adam`` with the learning rate specified in ``config``.
"""
if optimizer is None:
optimizer = Adam(lr=self.config.train_learning_rate)
input_mask = self.keras_model.inputs[
1] # second input layer is mask for dist loss
dist_loss = {
'mse': masked_loss_mse,
'mae': masked_loss_mae
}[self.config.train_dist_loss](
input_mask, reg_weight=self.config.train_background_reg)
prob_loss = 'binary_crossentropy'
self.keras_model.compile(
optimizer,
loss=[prob_loss, dist_loss],
loss_weights=list(self.config.train_loss_weights),
metrics={
'prob':
kld,
'dist':
[masked_metric_mae(input_mask),
masked_metric_mse(input_mask)]
})
self.callbacks = []
if self.basedir is not None:
self.callbacks += self._checkpoint_callbacks()
if self.config.train_tensorboard:
# self.callbacks.append(TensorBoard(log_dir=str(self.logdir), write_graph=False))
self.callbacks.append(
CARETensorBoard(log_dir=str(self.logdir),
prefix_with_timestamp=False,
n_images=3,
write_images=True,
prob_out=False))
if self.config.train_reduce_lr is not None:
rlrop_params = self.config.train_reduce_lr
if 'verbose' not in rlrop_params:
rlrop_params['verbose'] = True
self.callbacks.append(ReduceLROnPlateau(**rlrop_params))
self._model_prepared = True
def prepare_for_training(self, optimizer=None):
"""Prepare for neural network training.
Compiles the model and creates
`Keras Callbacks <https://keras.io/callbacks/>`_ to be used for training.
Note that this method will be implicitly called once by :func:`train`
(with default arguments) if not done so explicitly beforehand.
Parameters
----------
optimizer : obj or None
Instance of a `Keras Optimizer <https://keras.io/optimizers/>`_ to be used for training.
If ``None`` (default), uses ``Adam`` with the learning rate specified in ``config``.
"""
if optimizer is None:
optimizer = Adam(lr=self.config.train_learning_rate)
input_mask = self.keras_model.inputs[
1] # second input layer is mask for dist loss
## do a maxpooling(maximum filter) on the distance map (input_mask) to further emphasize pixel regions where we need to put more weights
#mask_pool = K.pool2d(input_mask, (5,5), strides=(1, 1), padding='same', pool_mode='max', data_format="channels_last")
dist_loss = {
'mse': masked_loss_mse,
'mae': masked_loss_mae
}[self.config.train_dist_loss](
input_mask,
reg_weight=self.config.train_background_reg,
norm_by_mask=self.config.norm_by_mask)
#prob_loss = {'huber':huber_loss(delta=self.config.train_huber_delta), 'mae':'mean_absolute_error', 'bce':'binary_crossentropy', 'mse':'mean_square_error'}[self.config.train_prob_loss]
dist_loss = masked_loss_mape(
input_mask,
reg_weight=self.config.train_background_reg,
norm_by_mask=self.config.norm_by_mask)
#prob_loss = 'binary_crossentropy'
#prob_loss = weighted_mse_loss(input_mask, weight=1)
#prob_loss = cosine_bce_loss(input_mask, offset=0.5)
prob_loss = cosine_bce_clipped_loss(input_mask, offset=0.5)
#prob_loss = weighted_bce_loss(input_mask, weight=1)
self.keras_model.compile(
optimizer,
loss=[prob_loss, dist_loss],
loss_weights=list(self.config.train_loss_weights),
metrics={
'prob':
kld,
'dist':
[masked_metric_mae(input_mask),
masked_metric_mse(input_mask)]
})
self.callbacks = []
if self.basedir is not None:
self.callbacks += self._checkpoint_callbacks()
if self.config.train_tensorboard:
# self.callbacks.append(TensorBoard(log_dir=str(self.logdir), write_graph=False))
self.callbacks.append(
CARETensorBoard(log_dir=str(self.logdir),
prefix_with_timestamp=True,
n_images=3,
write_images=True,
prob_out=False,
write_graph=True,
compute_histograms=True))
if self.config.train_reduce_lr is not None:
rlrop_params = self.config.train_reduce_lr
if 'verbose' not in rlrop_params:
rlrop_params['verbose'] = True
self.callbacks.append(ReduceLROnPlateau(**rlrop_params))
if self.config.train_one_cycle_lr_max is not None:
lrmax = self.config.train_one_cycle_lr_max
self.callbacks.append(OneCycleScheduler(max_lr=lrmax))
self._model_prepared = True