tf_agents.keras_layers.InnerReshape
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Returns a Keras layer that reshapes the inner dimensions of tensors.
tf_agents.keras_layers.InnerReshape(
current_shape: tf_agents.typing.types.Shape,
new_shape: tf_agents.typing.types.Shape,
**kwargs
) -> tf.keras.layers.Layer
Each tensor passed to an instance of InnerReshape, will be reshaped to:
shape(tensor)[:-len(current_shape)] + new_shape
(after its inner shape is validated against current_shape). Note:
The current_shape may contain None (unknown) dimension values.
This can be helpful when switching between Dense, ConvXd, and RNN layers
in TF-Agents networks, in ways that are agnostic to whether the input has
either [batch_size] or [batch_size, time] outer dimensions.
For example, to switch between Dense, Conv2D, and GRU layers:
net = tf_agents.networks.Sequential([
tf.keras.layers.Dense(32),
# Convert inner dim from [32] to [4, 4, 2] for Conv2D.
tf_agents.keras_layers.InnerReshape([None], new_shape=[4, 4, 2]),
tf.keras.layers.Conv2D(2, 3),
# Convert inner HWC dims [?, ?, 2] to [8] for Dense/RNN.
tf_agents.keras_layers.InnerReshape([None, None, 2], new_shape=[-1]),
tf.keras.layers.GRU(2, return_state=True, return_sequences=True)
])
Args |
|---|
current_shape
|
The current (partial) shape for the inner dims. This should
be a list, tuple, or tf.TensorShape with known rank. The given
current_shape must be compatible with the inner shape of the input.
Examples - [], [None], [None] * 3, [3, 3, 4], [3, None, 4].
|
new_shape
|
The new shape for the inner dims. The length of new_shape need
not match the length of current_shape, but if both shapes are fully
defined then the total number of elements must match. It may have up to
one flexible (-1) dimension. Examples - [3], [], [-1], [-1, 3].
|
**kwargs
|
Additionnal args to the Keras core layer constructor, e.g. name.
|
Returns |
|---|
A new Keras Layer that performs the requested reshape on incoming tensors.
|
Raises |
|---|
ValueError
|
If current_shape has unknown rank.
|
ValueError
|
If both shapes are fully defined and the number of elements
doesn't match.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf_agents.keras_layers.InnerReshape\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/agents/blob/v0.19.0/tf_agents/keras_layers/inner_reshape.py#L25-L99) |\n\nReturns a Keras layer that reshapes the inner dimensions of tensors.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf_agents.keras_layers.inner_reshape.InnerReshape`](https://www.tensorflow.org/agents/api_docs/python/tf_agents/keras_layers/InnerReshape)\n\n\u003cbr /\u003e\n\n tf_agents.keras_layers.InnerReshape(\n current_shape: ../../tf_agents/typing/types/Shape,\n new_shape: ../../tf_agents/typing/types/Shape,\n **kwargs\n ) -\u003e tf.keras.layers.Layer\n\nEach tensor passed to an instance of `InnerReshape`, will be reshaped to: \n\n shape(tensor)[:-len(current_shape)] + new_shape\n\n(after its inner shape is validated against `current_shape`). Note:\nThe `current_shape` may contain `None` (unknown) dimension values.\n\nThis can be helpful when switching between `Dense`, `ConvXd`, and `RNN` layers\nin TF-Agents networks, in ways that are agnostic to whether the input has\neither `[batch_size]` or `[batch_size, time]` outer dimensions.\n\nFor example, to switch between `Dense`, `Conv2D`, and `GRU` layers: \n\n net = tf_agents.networks.Sequential([\n tf.keras.layers.Dense(32),\n # Convert inner dim from [32] to [4, 4, 2] for Conv2D.\n tf_agents.keras_layers.InnerReshape([None], new_shape=[4, 4, 2]),\n tf.keras.layers.Conv2D(2, 3),\n # Convert inner HWC dims [?, ?, 2] to [8] for Dense/RNN.\n tf_agents.keras_layers.InnerReshape([None, None, 2], new_shape=[-1]),\n tf.keras.layers.GRU(2, return_state=True, return_sequences=True)\n ])\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `current_shape` | The current (partial) shape for the inner dims. This should be a `list`, `tuple`, or [`tf.TensorShape`](https://www.tensorflow.org/api_docs/python/tf/TensorShape) with known rank. The given current_shape must be compatible with the inner shape of the input. Examples - `[]`, `[None]`, `[None] * 3`, `[3, 3, 4]`, `[3, None, 4]`. |\n| `new_shape` | The new shape for the inner dims. The length of `new_shape` need not match the length of `current_shape`, but if both shapes are fully defined then the total number of elements must match. It may have up to one flexible (`-1`) dimension. Examples - `[3]`, `[]`, `[-1]`, `[-1, 3]`. |\n| `**kwargs` | Additionnal args to the Keras core layer constructor, e.g. `name`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A new Keras `Layer` that performs the requested reshape on incoming tensors. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|----------------------------------------------------------------------------|\n| `ValueError` | If `current_shape` has unknown rank. |\n| `ValueError` | If both shapes are fully defined and the number of elements doesn't match. |\n\n\u003cbr /\u003e"]]
View source on GitHub