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LSTMCell with pruning.

Inherits From: LSTMCell

Overrides the call method of tensorflow LSTMCell and injects the weight masks. Masks are applied to only the weight matrix of the LSTM and not the projection matrix.

num_units int, The number of units in the LSTM cell
use_peepholes bool, set True to enable diagonal/peephole connections.
cell_clip (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation.
initializer (optional) The initializer to use for the weight and projection matrices.
num_proj (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
proj_clip (optional) A float value. If num_proj > 0 and proj_clip is provided, then the projected values are clipped elementwise to within [-proj_clip, proj_clip].
num_unit_shards Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
num_proj_shards Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
forget_bias Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training. Must set it manually to 0.0 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. This 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.

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


output_size Integer or TensorShape: size of outputs produced by this cell.

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.



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Return zero-filled state tensor(s).

batch_size int, float, or unit Tensor representing the batch size.
dtype the data type to use for the state.

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.