def forward_as_dict(self,
tensors: Dict[str, tf.Tensor],
mode: str = None) -> Dict[str, tf.Tensor]:
"""Forward method on a dictionary of Tensors.
The input ``tensors`` should contain all keys defined in
``self.inputs`` (but might contain more keys).
It returns a new dictionary (does not mutate the input
``tensors`` dictionary in-place), whose keys are exactly
``self.outputs``.
Parameters
----------
tensors : Dict[str, tf.Tensor]
Dictionary mapping self.inputs to tf.Tensors.
mode : str, optional
One of tf.estimator.ModeKeys
Returns
-------
Dict[str, tf.Tensor]
Dictionary mapping self.outputs to tf.Tensors
"""
try:
return item_to_dict(
self.forward(dict_to_item(tensors, self.inputs), mode),
self.outputs)
except Exception as e:
LOGGER.error(f"{self} error on {tensors}")
raise e
def forward(self,
tensors: Union[tf.Tensor, Tuple[tf.Tensor, ...]],
mode: str = None) -> Union[tf.Tensor, Tuple[tf.Tensor, ...]]:
"""Forward method on one Tensor or a tuple of Tensors.
Parameters
----------
tensors : Union[tf.Tensor, Tuple[tf.Tensor, ...]]
- n_in = 1: one tensor (NOT wrapped in a tuple)
- n_in > 1: a tuple of tensors
mode : str, optional
Description
Returns
-------
Union[tf.Tensor, Tuple[tf.Tensor, ...]]
- n_out = 1: one tensor (NOT wrapped in a tuple)
- n_out > 1: a tuple of tensors
"""
try:
return dict_to_item(
self.forward_as_dict(item_to_dict(tensors, self.inputs), mode),
self.outputs)
except Exception as e:
LOGGER.error(f"{self} error on {tensors}")
raise e
def forward_as_dict(self, tensors: Dict, mode: str = None) -> Dict:
"""Forward method of the layer on dictionaries of self.inputs"""
try:
return item_to_dict(
self.forward(dict_to_item(tensors, self.inputs), mode),
self.outputs)
except Exception as e:
LOGGER.error(f"{self} error on {tensors}")
raise e
def forward(self, tensors, mode: str = None):
"""Forward method of the layer on tuples"""
try:
return dict_to_item(
self.forward_as_dict(item_to_dict(tensors, self.inputs), mode),
self.outputs)
except Exception as e:
LOGGER.error(f"{self} error on {tensors}")
raise e