Inherits From: RNNCell
, Layer
, Layer
, Module
tf.compat.v1.nn.rnn_cell.BasicLSTMCell(
num_units,
forget_bias=1.0,
state_is_tuple=True,
activation=None,
reuse=None,
name=None,
dtype=None,
**kwargs
)
Basic LSTM recurrent network cell.
The implementation is based on
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.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full tf.compat.v1.nn.rnn_cell.LSTMCell
that follows.
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnLSTM
for better performance on GPU, or
tf.contrib.rnn.LSTMBlockCell
and tf.contrib.rnn.LSTMBlockFusedCell
for
better performance on CPU.
Args |
num_units
|
int, The number of units in the LSTM cell.
|
forget_bias
|
float, The bias added to forget gates (see above). Must set
to 0.0 manually when restoring from CudnnLSTM-trained checkpoints.
|
state_is_tuple
|
If True, accepted and returned states are 2-tuples of the
c_state and m_state . If False, they are concatenated along the
column axis. The latter behavior will soon be deprecated.
|
activation
|
Activation function of the inner states. Default: tanh . It
could also be string that is within Keras activation function names.
|
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
|
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.
|
dtype
|
Default dtype of the layer (default of None means use the type of
the first input). Required when build is called before call .
|
**kwargs
|
Dict, keyword named properties for common layer attributes, like
trainable etc when constructing the cell from configs of get_config().
When restoring from CudnnLSTM-trained checkpoints, must use
CudnnCompatibleLSTMCell instead.
|
Attributes |
graph
|
|
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
View source
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
zero_state
View source
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 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 .
|