tf.keras.layers.CuDNNGRU
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Fast GRU implementation backed by cuDNN.
tf.keras.layers.CuDNNGRU(
units, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal',
bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None,
bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,
recurrent_constraint=None, bias_constraint=None, return_sequences=False,
return_state=False, go_backwards=False, stateful=False, **kwargs
)
More information about cuDNN can be found on the NVIDIA
developer website.
Can only be run on GPU.
Arguments |
|---|
units
|
Positive integer, dimensionality of the output space.
|
kernel_initializer
|
Initializer for the kernel weights matrix, used for
the linear transformation of the inputs.
|
recurrent_initializer
|
Initializer for the recurrent_kernel weights
matrix, used for the linear transformation of the recurrent state.
|
bias_initializer
|
Initializer for the bias vector.
|
kernel_regularizer
|
Regularizer function applied to the kernel weights
matrix.
|
recurrent_regularizer
|
Regularizer function applied to the
recurrent_kernel weights matrix.
|
bias_regularizer
|
Regularizer function applied to the bias vector.
|
activity_regularizer
|
Regularizer function applied to the output of the
layer (its "activation").
|
kernel_constraint
|
Constraint function applied to the kernel weights
matrix.
|
recurrent_constraint
|
Constraint function applied to the
recurrent_kernel weights matrix.
|
bias_constraint
|
Constraint function applied to the bias vector.
|
return_sequences
|
Boolean. Whether to return the last output in the output
sequence, or the full sequence.
|
return_state
|
Boolean. Whether to return the last state in addition to the
output.
|
go_backwards
|
Boolean (default False). If True, process the input sequence
backwards and return the reversed sequence.
|
stateful
|
Boolean (default False). If True, the last state for each sample
at index i in a batch will be used as initial state for the sample of
index i in the following batch.
|
Methods
get_initial_state
View source
get_initial_state(
inputs
)
reset_states
View source
reset_states(
states=None
)
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.layers.CuDNNGRU\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/cudnn_recurrent.py#L161-L337) |\n\nFast GRU implementation backed by cuDNN.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.layers.CuDNNGRU`](/api_docs/python/tf/compat/v1/keras/layers/CuDNNGRU)\n\n\u003cbr /\u003e\n\n tf.keras.layers.CuDNNGRU(\n units, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal',\n bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None,\n bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,\n recurrent_constraint=None, bias_constraint=None, return_sequences=False,\n return_state=False, go_backwards=False, stateful=False, **kwargs\n )\n\nMore information about cuDNN can be found on the [NVIDIA\ndeveloper website](https://developer.nvidia.com/cudnn).\nCan only be run on GPU.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `units` | Positive integer, dimensionality of the output space. |\n| `kernel_initializer` | Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. |\n| `recurrent_initializer` | Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. |\n| `bias_initializer` | Initializer for the bias vector. |\n| `kernel_regularizer` | Regularizer function applied to the `kernel` weights matrix. |\n| `recurrent_regularizer` | Regularizer function applied to the `recurrent_kernel` weights matrix. |\n| `bias_regularizer` | Regularizer function applied to the bias vector. |\n| `activity_regularizer` | Regularizer function applied to the output of the layer (its \"activation\"). |\n| `kernel_constraint` | Constraint function applied to the `kernel` weights matrix. |\n| `recurrent_constraint` | Constraint function applied to the `recurrent_kernel` weights matrix. |\n| `bias_constraint` | Constraint function applied to the bias vector. |\n| `return_sequences` | Boolean. Whether to return the last output in the output sequence, or the full sequence. |\n| `return_state` | Boolean. Whether to return the last state in addition to the output. |\n| `go_backwards` | Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. |\n| `stateful` | Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|----------|---------------|\n| `cell` | \u003cbr /\u003e \u003cbr /\u003e |\n| `states` | \u003cbr /\u003e \u003cbr /\u003e |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `get_initial_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/recurrent.py#L593-L614) \n\n get_initial_state(\n inputs\n )\n\n### `reset_states`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/keras/layers/recurrent.py#L806-L858) \n\n reset_states(\n states=None\n )"]]
View source on GitHub