tf.contrib.rnn.LSTMBlockCell

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

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.