def add_module(self, *args, **kwargs):
if 'input_spec' in kwargs:
layer = super().add_module(*args, modules=layer_modules, **kwargs)
self.output_spec = layer.output_spec
else:
if self.output_spec is None:
if util.is_atomic_values_spec(values_spec=self.inputs_spec):
self.output_spec = self.inputs_spec
elif len(self.inputs_spec) == 1:
self.output_spec = next(iter(self.inputs_spec.values()))
else:
self.output_spec = None
if self.output_spec is not None:
if 'input_spec' in kwargs:
kwargs['input_spec'] = util.unify_value_specs(
value_spec1=kwargs['input_spec'],
value_spec2=self.output_spec)
else:
kwargs['input_spec'] = self.output_spec
layer = super().add_module(*args, modules=layer_modules, **kwargs)
self.output_spec = layer.output_spec
if not isinstance(layer, (Layer, Parameter)):
raise TensorforceError.type(name='layer-based network',
argument='sub-module',
value=layer)
return layer
def __init__(self,
name,
input_spec=None,
summary_labels=None,
l2_regularization=None):
super().__init__(name=name,
summary_labels=summary_labels,
l2_regularization=l2_regularization)
self.input_spec = self.default_input_spec()
self.input_spec = util.valid_value_spec(value_spec=self.input_spec,
accept_underspecified=True,
return_normalized=True)
if input_spec is not None:
input_spec = util.valid_value_spec(value_spec=input_spec,
accept_underspecified=True,
return_normalized=True)
self.input_spec = util.unify_value_specs(
value_spec1=self.input_spec, value_spec2=input_spec)
# Copy so that spec can be modified
self.output_spec = self.get_output_spec(
input_spec=dict(self.input_spec))
self.output_spec = util.valid_value_spec(value_spec=self.output_spec,
accept_underspecified=True,
return_normalized=True)
# Register layer globally
if Layer.layers is None:
Layer.layers = OrderedDict()
# if self.name in Layer.layers:
# raise TensorforceError.unexpected()
Layer.layers[self.name] = self
def __init__(self,
name,
input_spec=None,
l2_regularization=None,
summary_labels=None):
"""
Layer constructor.
Args:
input_spec (specification): Input tensor specification.
"""
super().__init__(name=name,
l2_regularization=l2_regularization,
summary_labels=summary_labels)
self.input_spec = self.default_input_spec()
self.input_spec = util.valid_value_spec(value_spec=self.input_spec,
accept_underspecified=True,
return_normalized=True)
if input_spec is not None:
input_spec = util.valid_value_spec(value_spec=input_spec,
accept_underspecified=True,
return_normalized=True)
self.input_spec = util.unify_value_specs(
value_spec1=self.input_spec, value_spec2=input_spec)
# Copy so that spec can be modified
self.output_spec = self.get_output_spec(
input_spec=dict(self.input_spec))
self.output_spec = util.valid_value_spec(value_spec=self.output_spec,
accept_underspecified=True,
return_normalized=True)
