DEPRECATED: Please use tf.compat.v1.nn.rnn_cell.LSTMCell instead.

Inherits From: RNNCell, Layer, Layer, Module

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