tf_agents.networks.network.create_variables
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Create variables in module
given input_spec
; return output_spec
.
tf_agents.networks.network.create_variables(
module: typing.Union[Network, tf.keras.layers.Layer],
input_spec: typing.Optional[types.NestedTensorSpec] = None,
**kwargs
) -> tf_agents.typing.types.NestedTensorSpec
Here module
can be a tf_agents.networks.Network
or Keras
layer.
Args |
module
|
The instance we would like to create layers on.
|
input_spec
|
The input spec (excluding batch dimensions).
|
**kwargs
|
Extra arguments to module.__call__ , e.g. training=True .
|
Raises |
ValueError
|
If module is a generic Keras layer but input_spec is None .
|
TypeError
|
If module is a tf.keras.layers.{RNN,LSTM,GRU,...} . These
must be wrapped in tf_agents.keras_layers.RNNWrapper .
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf_agents.networks.network.create_variables\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/agents/blob/v0.19.0/tf_agents/networks/network.py#L571-L670) |\n\nCreate variables in `module` given `input_spec`; return `output_spec`. \n\n tf_agents.networks.network.create_variables(\n module: typing.Union[Network, tf.keras.layers.Layer],\n input_spec: typing.Optional[types.NestedTensorSpec] = None,\n **kwargs\n ) -\u003e ../../../tf_agents/typing/types/NestedTensorSpec\n\nHere `module` can be a [`tf_agents.networks.Network`](../../../tf_agents/networks/Network) or `Keras` layer.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|-------------------------------------------------------------|\n| `module` | The instance we would like to create layers on. |\n| `input_spec` | The input spec (excluding batch dimensions). |\n| `**kwargs` | Extra arguments to `module.__call__`, e.g. `training=True`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Output specs, a nested [`tf.TypeSpec`](https://www.tensorflow.org/api_docs/python/tf/TypeSpec) describing the output signature. If `module` returns a `tfp.Distribution`, then the associated nested object is a `tf_agents.specs.DistributionSpecV2` (which is not a true [`tf.TypeSpec`](https://www.tensorflow.org/api_docs/python/tf/TypeSpec) but contains enough information to create a nested [`tf.TypeSpec`](https://www.tensorflow.org/api_docs/python/tf/TypeSpec) using [`tf_agents.distributions.utils.parameters_to_dict`](../../../tf_agents/distributions/utils/parameters_to_dict)). ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `ValueError` | If `module` is a generic Keras layer but `input_spec is None`. |\n| `TypeError` | If `module` is a `tf.keras.layers.{RNN,LSTM,GRU,...}`. These must be wrapped in [`tf_agents.keras_layers.RNNWrapper`](../../../tf_agents/keras_layers/RNNWrapper). |\n\n\u003cbr /\u003e"]]