This variation of RNN cell is characterized by the simplified data
dependence
between hidden states of two consecutive time steps. Traditionally, hidden
states from a cell at time step t-1 needs to be multiplied with a matrix
Whh before being fed into the ensuing cell at time step t.
This flavor of RNN replaces the matrix multiplication between h{t-1}
and W_hh with a pointwise multiplication, resulting in performance
gain.
Args
num_units
int, The number of units in the SRU cell.
activation
Nonlinearity to use. Default: tanh.
reuse
(optional) Python boolean describing whether to reuse variables
in an existing scope. If not True, and the existing scope already has
the given variables, an error is raised.
name
(optional) String, the name of the layer. Layers with the same name
will share weights, but to avoid mistakes we require reuse=True in such
cases.
**kwargs
Additional keyword arguments.
Attributes
graph
DEPRECATED FUNCTION
output_size
Integer or TensorShape: size of outputs produced by this cell.
scope_name
state_size
size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers
or TensorShapes.
int, float, or unit Tensor representing the batch size.
dtype
the data type to use for the state.
Returns
If state_size is an int or TensorShape, then the return value is a
N-D tensor of shape [batch_size, state_size] filled with zeros.
If state_size is a nested list or tuple, then the return value is
a nested list or tuple (of the same structure) of 2-D tensors with
the shapes [batch_size, s] for each s in state_size.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.rnn.SRUCell\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/rnn/python/ops/rnn_cell.py#L2738-L2816) |\n\nSRU, Simple Recurrent Unit.\n\nInherits From: [`LayerRNNCell`](../../../tf/contrib/rnn/LayerRNNCell) \n\n tf.contrib.rnn.SRUCell(\n num_units, activation=None, reuse=None, name=None, **kwargs\n )\n\nImplementation based on\nTraining RNNs as Fast as CNNs (cf. \u003chttps://arxiv.org/abs/1709.02755\u003e).\n\nThis variation of RNN cell is characterized by the simplified data\ndependence\nbetween hidden states of two consecutive time steps. Traditionally, hidden\nstates from a cell at time step t-1 needs to be multiplied with a matrix\nW*hh before being fed into the ensuing cell at time step t.\nThis flavor of RNN replaces the matrix multiplication between h*{t-1}\nand W_hh with a pointwise multiplication, resulting in performance\ngain.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `num_units` | int, The number of units in the SRU cell. |\n| `activation` | Nonlinearity to use. Default: `tanh`. |\n| `reuse` | (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. |\n| `name` | (optional) String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. |\n| `**kwargs` | Additional keyword arguments. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `graph` | DEPRECATED FUNCTION \u003cbr /\u003e | **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph. |\n| `output_size` | Integer or TensorShape: size of outputs produced by this cell. |\n| `scope_name` | \u003cbr /\u003e |\n| `state_size` | size(s) of state(s) used by this cell. \u003cbr /\u003e It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. |\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/ops/rnn_cell_impl.py#L281-L309) \n\n get_initial_state(\n inputs=None, batch_size=None, dtype=None\n )\n\n### `zero_state`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/rnn_cell_impl.py#L311-L340) \n\n zero_state(\n batch_size, dtype\n )\n\nReturn zero-filled state tensor(s).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|--------------|---------------------------------------------------------|\n| `batch_size` | int, float, or unit Tensor representing the batch size. |\n| `dtype` | the data type to use for the state. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| If `state_size` is an int or TensorShape, then the return value is a `N-D` tensor of shape `[batch_size, state_size]` filled with zeros. \u003cbr /\u003e If `state_size` is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of `2-D` tensors with the shapes `[batch_size, s]` for each s in `state_size`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub