View source on GitHub |
Basic LSTM recurrent network cell.
Inherits From: LayerRNNCell
tf.contrib.rnn.LSTMBlockCell(
num_units, forget_bias=1.0, cell_clip=None, use_peephole=False, dtype=None,
reuse=None, name='lstm_cell'
)
The implementation is based on: http://arxiv.org/abs/1409.2329
We add forget_bias
(default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
Unlike rnn_cell_impl.LSTMCell
, this is a monolithic op and should be much
faster. The weight and bias matrices should be compatible as long as the
variable scope matches.
Args | |
---|---|
num_units
|
int, The number of units in the LSTM cell. |
forget_bias
|
float, The bias added to forget gates (see above). |
cell_clip
|
An optional float . Defaults to -1 (no clipping).
|
use_peephole
|
Whether to use peephole connections or not. |
dtype
|
the variable dtype of this layer. Default to tf.float32. |
reuse
|
(optional) 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
|
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. By default this is "lstm_cell", for variable-name compatibility
with tf.compat.v1.nn.rnn_cell.LSTMCell .
When restoring from CudnnLSTM-trained checkpoints, must use CudnnCompatibleLSTMBlockCell instead. |
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. |
Methods
get_initial_state
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
zero_state
zero_state(
batch_size, dtype
)
Return zero-filled state tensor(s).
Args | |
---|---|
batch_size
|
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 |