tf.compat.v1.nn.rnn_cell.BasicLSTMCell

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.

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.

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

apply

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apply(
    *args, **kwargs
)

get_initial_state

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get_initial_state(
    inputs=None, batch_size=None, dtype=None
)

get_losses_for

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get_losses_for(
    inputs
)

Retrieves losses relevant to a specific set of inputs.

Args
inputs Input tensor or list/tuple of input tensors.

Returns
List of loss tensors of the layer that depend on inputs.

get_updates_for

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get_updates_for(
    inputs
)

Retrieves updates relevant to a specific set of inputs.

Args
inputs Input tensor or list/tuple of input tensors.

Returns
List of update ops of the layer that depend on inputs.

zero_state

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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.