tf.contrib.rnn.TimeFreqLSTMCell.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 x 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 s

tf.contrib.rnn.TimeFreqLSTMCell.state_size

tf.contrib.rnn.TimeFreqLSTMCell.output_size

class tf.contrib.rnn.TimeFreqLSTMCell Time-Frequency Long short-term memory unit (LSTM) recurrent network cell. This implementation is based on: Tara N. Sainath and Bo Li "Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks." submitted to INTERSPEECH, 2016. It uses peep-hole connections and optional cell clipping.

tf.contrib.rnn.LSTMBlockCell.__init__(num_units, forget_bias=1.0, use_peephole=False) Initialize the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). use_peephole: Whether to use peephole connections or not.

tf.contrib.rnn.LSTMBlockCell.__call__(x, states_prev, scope=None) Long short-term memory cell (LSTM).

tf.contrib.rnn.LSTMBlockCell.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 x 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 shap

tf.contrib.rnn.LSTMBlockCell.state_size

tf.contrib.rnn.LSTMBlockCell.output_size

class tf.contrib.rnn.LSTMBlockCell Basic LSTM recurrent network cell. 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. Unlike BasicLSTMCell, this is a monolithic op and should be much faster. The weight and bias matrixes should be compatible as long as the variabel scope matches.