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Basic LSTM recurrent network cell.

Inherits From: LayerRNNCell

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.

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.

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.


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.