tf.raw_ops.CudnnRNNParamsSize
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Computes size of weights that can be used by a Cudnn RNN model.
tf.raw_ops.CudnnRNNParamsSize(
num_layers,
num_units,
input_size,
T,
S,
rnn_mode='lstm',
input_mode='linear_input',
direction='unidirectional',
dropout=0,
seed=0,
seed2=0,
num_proj=0,
name=None
)
Return the params size that can be used by the Cudnn RNN model. Subsequent
weight allocation and initialization should use this size.
num_layers: Specifies the number of layers in the RNN model.
num_units: Specifies the size of the hidden state.
input_size: Specifies the size of the input state.
rnn_mode: Indicates the type of the RNN model.
input_mode: Indicate whether there is a linear projection between the input and
The actual computation before the first layer. 'skip_input' is only allowed
when input_size == num_units; 'auto_select' implies 'skip_input' when
input_size == num_units; otherwise, it implies 'linear_input'.
direction: Indicates whether a bidirectional model will be used.
dir = (direction == bidirectional) ? 2 : 1
dropout: dropout probability. When set to 0., dropout is disabled.
seed: the 1st part of a seed to initialize dropout.
seed2: the 2nd part of a seed to initialize dropout.
params_size: The size of the params buffer that should be allocated and
initialized for this RNN model. Note that this params buffer may not be
compatible across GPUs. Please use CudnnRNNParamsWeights and
CudnnRNNParamsBiases to save and restore them in a way that is compatible
across different runs.
Args |
|---|
num_layers
|
A Tensor of type int32.
|
num_units
|
A Tensor of type int32.
|
input_size
|
A Tensor of type int32.
|
T
|
A tf.DType from: tf.bfloat16, tf.half, tf.float32, tf.float64.
|
S
|
A tf.DType from: tf.int32, tf.int64.
|
rnn_mode
|
An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
|
input_mode
|
An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
|
direction
|
An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
|
dropout
|
An optional float. Defaults to 0.
|
seed
|
An optional int. Defaults to 0.
|
seed2
|
An optional int. Defaults to 0.
|
num_proj
|
An optional int. Defaults to 0.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type S.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.CudnnRNNParamsSize\n\n\u003cbr /\u003e\n\nComputes size of weights that can be used by a Cudnn RNN model.\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.raw_ops.CudnnRNNParamsSize`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/CudnnRNNParamsSize)\n\n\u003cbr /\u003e\n\n tf.raw_ops.CudnnRNNParamsSize(\n num_layers,\n num_units,\n input_size,\n T,\n S,\n rnn_mode='lstm',\n input_mode='linear_input',\n direction='unidirectional',\n dropout=0,\n seed=0,\n seed2=0,\n num_proj=0,\n name=None\n )\n\nReturn the params size that can be used by the Cudnn RNN model. Subsequent\nweight allocation and initialization should use this size.\n\nnum_layers: Specifies the number of layers in the RNN model.\nnum_units: Specifies the size of the hidden state.\ninput_size: Specifies the size of the input state.\nrnn_mode: Indicates the type of the RNN model.\ninput_mode: Indicate whether there is a linear projection between the input and\nThe actual computation before the first layer. 'skip_input' is only allowed\nwhen input_size == num_units; 'auto_select' implies 'skip_input' when\ninput_size == num_units; otherwise, it implies 'linear_input'.\ndirection: Indicates whether a bidirectional model will be used.\ndir = (direction == bidirectional) ? 2 : 1\ndropout: dropout probability. When set to 0., dropout is disabled.\nseed: the 1st part of a seed to initialize dropout.\nseed2: the 2nd part of a seed to initialize dropout.\nparams_size: The size of the params buffer that should be allocated and\ninitialized for this RNN model. Note that this params buffer may not be\ncompatible across GPUs. Please use CudnnRNNParamsWeights and\nCudnnRNNParamsBiases to save and restore them in a way that is compatible\nacross different runs.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|---------------------------------------------------------------------------------------------------------|\n| `num_layers` | A `Tensor` of type `int32`. |\n| `num_units` | A `Tensor` of type `int32`. |\n| `input_size` | A `Tensor` of type `int32`. |\n| `T` | A [`tf.DType`](../../tf/dtypes/DType) from: `tf.bfloat16, tf.half, tf.float32, tf.float64`. |\n| `S` | A [`tf.DType`](../../tf/dtypes/DType) from: `tf.int32, tf.int64`. |\n| `rnn_mode` | An optional `string` from: `\"rnn_relu\", \"rnn_tanh\", \"lstm\", \"gru\"`. Defaults to `\"lstm\"`. |\n| `input_mode` | An optional `string` from: `\"linear_input\", \"skip_input\", \"auto_select\"`. Defaults to `\"linear_input\"`. |\n| `direction` | An optional `string` from: `\"unidirectional\", \"bidirectional\"`. Defaults to `\"unidirectional\"`. |\n| `dropout` | An optional `float`. Defaults to `0`. |\n| `seed` | An optional `int`. Defaults to `0`. |\n| `seed2` | An optional `int`. Defaults to `0`. |\n| `num_proj` | An optional `int`. Defaults to `0`. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `S`. ||\n\n\u003cbr /\u003e"]]
