LostTech.TensorFlow : API Documentation

Type GridLSTMCell

Namespace tensorflow.contrib.rnn

Parent RNNCell

Interfaces IGridLSTMCell

Grid Long short-term memory unit (LSTM) recurrent network cell.

The default is based on: Nal Kalchbrenner, Ivo Danihelka and Alex Graves "Grid Long Short-Term Memory," Proc. ICLR 2016. http://arxiv.org/abs/1507.01526

When peephole connections are used, the 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.

The code uses optional peephole connections, shared_weights and cell clipping.



Public static methods

GridLSTMCell NewDyn(object num_units, ImplicitContainer<T> use_peepholes, ImplicitContainer<T> share_time_frequency_weights, object cell_clip, object initializer, ImplicitContainer<T> num_unit_shards, ImplicitContainer<T> forget_bias, object feature_size, object frequency_skip, object num_frequency_blocks, object start_freqindex_list, object end_freqindex_list, ImplicitContainer<T> couple_input_forget_gates, ImplicitContainer<T> state_is_tuple, object reuse)

Initialize the parameters for an LSTM cell.
object num_units
int, The number of units in the LSTM cell
ImplicitContainer<T> use_peepholes
(optional) bool, default False. Set True to enable diagonal/peephole connections.
ImplicitContainer<T> share_time_frequency_weights
(optional) bool, default False. Set True to enable shared cell weights between time and frequency LSTMs.
object cell_clip
(optional) A float value, default None, 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, default None.
ImplicitContainer<T> num_unit_shards
(optional) int, default 1, How to split the weight matrix. If > 1, the weight matrix is stored across num_unit_shards.
ImplicitContainer<T> forget_bias
(optional) float, default 1.0, The initial bias of the forget gates, used to reduce the scale of forgetting at the beginning of the training.
object feature_size
(optional) int, default None, The size of the input feature the LSTM spans over.
object frequency_skip
(optional) int, default None, The amount the LSTM filter is shifted by in frequency.
object num_frequency_blocks
[required] A list of frequency blocks needed to cover the whole input feature splitting defined by start_freqindex_list and end_freqindex_list.
object start_freqindex_list
[optional], list of ints, default None, The starting frequency index for each frequency block.
object end_freqindex_list
[optional], list of ints, default None. The ending frequency index for each frequency block.
ImplicitContainer<T> couple_input_forget_gates
(optional) bool, default False, Whether to couple the input and forget gates, i.e. f_gate = 1.0 - i_gate, to reduce model parameters and computation cost.
ImplicitContainer<T> state_is_tuple
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;

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

object PythonObject get;

object rnncell_scope get; set;

string scope_name get;

object scope_name_dyn get;

object state_size get;

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.

object state_size_dyn get;

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.

object state_tuple_type get;

object state_tuple_type_dyn get;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

IList<object> updates get;

object updates_dyn get;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;