an RNNCell, an embedding will be put before its inputs.
embedding_classes
integer, how many symbols will be embedded.
embedding_size
integer, the size of the vectors we embed into.
initializer
an initializer to use when creating the embedding;
if None, the initializer from variable scope or a default one is used.
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.
Raises
TypeError
if cell is not an RNNCell.
ValueError
if embedding_classes is not positive.
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.EmbeddingWrapper\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/core_rnn_cell.py#L191-L268) |\n\nOperator adding input embedding to the given cell.\n\nInherits From: [`RNNCell`](../../../tf/nn/rnn_cell/RNNCell) \n\n tf.contrib.rnn.EmbeddingWrapper(\n cell, embedding_classes, embedding_size, initializer=None, reuse=None\n )\n\n| **Note:** in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the embedding on this batch-concatenated sequence, then split it and feed into your RNN.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `cell` | an RNNCell, an embedding will be put before its inputs. |\n| `embedding_classes` | integer, how many symbols will be embedded. |\n| `embedding_size` | integer, the size of the vectors we embed into. |\n| `initializer` | an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used. |\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\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------|\n| `TypeError` | if cell is not an RNNCell. |\n| `ValueError` | if embedding_classes is not positive. |\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/contrib/rnn/python/ops/core_rnn_cell.py#L240-L242) \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