tf.contrib.model_pruning.MaskedBasicLSTMCell

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Class MaskedBasicLSTMCell

Basic LSTM recurrent network cell with pruning.

Inherits From: BasicLSTMCell

Overrides the call method of tensorflow BasicLSTMCell and injects the weight masks

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.

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.

__init__

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__init__(
    num_units,
    forget_bias=1.0,
    state_is_tuple=True,
    activation=None,
    reuse=None,
    name=None
)

Initialize the basic LSTM cell with pruning.

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.
• 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.

When restoring from CudnnLSTM-trained checkpoints, must use CudnnCompatibleLSTMCell instead.

Properties

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

tf.contrib.model_pruning.MaskedBasicLSTMCell.get_initial_state

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

tf.contrib.model_pruning.MaskedBasicLSTMCell.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.