tf.compat.v1.nn.rnn_cell.BasicLSTMCell

DEPRECATED: Please use tf.compat.v1.nn.rnn_cell.LSTMCell instead.

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.