def compile_model(self, optimizer, optimizer_kwargs, loss, metrics,
**kwargs):
"""
Compile the stored tf.keras Model instance stored in self.model
Sets the loss function, optimizer and metrics
Args:
optimizer: (string) The name of a tf.keras.optimizers Optimizer
optimizer_kwargs: (dict) Key-word arguments passed to the Optimizer
loss: (string) The name of a tf.keras.losses or
MultiPlanarUnet loss function
metrics: (list) List of tf.keras.metrics or
MultiPlanarUNet metrics.
**kwargs: (dict) Key-word arguments passed to losses
and/or metrics that accept such.
"""
# Make sure sparse metrics and loss are specified as sparse
metrics = ensure_list_or_tuple(metrics)
losses = ensure_list_or_tuple(loss)
ensure_sparse(metrics + losses)
# Initialize optimizer
optimizer = optimizers.__dict__[optimizer]
optimizer = optimizer(**optimizer_kwargs)
# Initialize loss(es) and metrics from tf.keras or MultiPlanarUNet
losses = init_losses(losses, self.logger, **kwargs)
metrics = init_metrics(metrics, self.logger, **kwargs)
# Compile the model
self.model.compile(optimizer=optimizer, loss=losses, metrics=metrics)
self.logger("Optimizer: %s" % optimizer)
self.logger("Loss funcs: %s" % losses)
self.logger("Metrics: %s" % init_metrics)
return self
def compile_model(self,
optimizer,
loss,
metrics,
reduction,
check_sparse=False,
optimizer_kwargs={},
loss_kwargs={},
**kwargs):
"""
Compile the stored tf.keras Model instance stored in self.model
Sets the loss function, optimizer and metrics
Args:
optimizer: (string) The name of a tf.keras.optimizers Optimizer
optimizer_kwargs: (dict) Key-word arguments passed to the Optimizer
loss: (string) The name of a tf.keras.losses or
MultiPlanarUnet loss function
metrics: (list) List of tf.keras.metrics or
mpunet metrics.
reduction: TODO
check_sparse: TODO
**kwargs: (dict) Key-word arguments passed to losses
and/or metrics that accept such.
"""
# Make sure sparse metrics and loss are specified as sparse
metrics = ensure_list_or_tuple(metrics)
losses = ensure_list_or_tuple(loss)
if check_sparse:
ensure_sparse(metrics + losses)
# Initialize optimizer, loss(es) and metric(s) from tf.keras or
# mpunet
optimizer = init_optimizer(optimizer, self.logger, **optimizer_kwargs)
losses = init_losses(losses, self.logger, **kwargs)
for i, loss in enumerate(losses):
try:
losses[i] = loss(reduction=reduction, **loss_kwargs)
except (ValueError, TypeError):
raise TypeError("All loss functions must currently be "
"callable and accept the 'reduction' "
"parameter specifying a "
"tf.keras.losses.Reduction type. If you "
"specified a keras loss function such as "
"'sparse_categorical_crossentropy', change "
"this to its corresponding loss class "
"'SparseCategoricalCrossentropy'. If "
"you implemented a custom loss function, "
"please raise an issue on GitHub.")
metrics = init_metrics(metrics, self.logger, **kwargs)
# Compile the model
self.model.compile(optimizer=optimizer, loss=losses, metrics=metrics)
self.logger("Optimizer: %s" % optimizer)
self.logger("Loss funcs: %s" % losses)
self.logger("Metrics: %s" % init_metrics)
return self