TimeFreqLSTMCell - LostTech.TensorFlow Documentation

Type TimeFreqLSTMCell

Namespace tensorflow.contrib.rnn

Parent RNNCell

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.

Methods

NewDyn

Properties

Public static methods

TimeFreqLSTMCell NewDyn(object num_units, ImplicitContainer<T> use_peepholes, object cell_clip, object initializer, ImplicitContainer<T> num_unit_shards, ImplicitContainer<T> forget_bias, object feature_size, ImplicitContainer<T> frequency_skip, object reuse)

Initialize the parameters for an LSTM cell.

Parameters

object num_units: int, The number of units in the LSTM cell
ImplicitContainer<T> use_peepholes: bool, set True to enable diagonal/peephole connections.
object cell_clip: (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation.
object initializer: (optional) The initializer to use for the weight and projection matrices.
ImplicitContainer<T> num_unit_shards: int, How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards.
ImplicitContainer<T> forget_bias: float, 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.
object feature_size: int, The size of the input feature the LSTM spans over.
ImplicitContainer<T> frequency_skip: int, The amount the LSTM filter is shifted by in frequency.
object 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.

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

object graph get;

object graph_dyn get;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object output_size get;

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

object output_size_dyn get;