LostTech.TensorFlow : API Documentation

Type LSTMCell

Namespace tensorflow.nn.rnn_cell

Parent LayerRNNCell

Interfaces ILSTMCell

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

The default non-peephole implementation is based on:


Felix Gers, Jurgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999.

The peephole implementation is based on:


Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.

The class uses optional peep-hole connections, optional cell clipping, and an optional projection layer.

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.



Public static methods

LSTMCell NewDyn(object num_units, ImplicitContainer<T> use_peepholes, object cell_clip, object initializer, object num_proj, object proj_clip, object num_unit_shards, object num_proj_shards, ImplicitContainer<T> forget_bias, ImplicitContainer<T> state_is_tuple, object activation, object reuse, object name, object dtype, IDictionary<string, object> kwargs)

Initialize the parameters for an LSTM cell. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
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.
object num_proj
(optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
object proj_clip
(optional) A float value. If `num_proj > 0` and `proj_clip` is provided, then the projected values are clipped elementwise to within `[-proj_clip, proj_clip]`.
object num_unit_shards
Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
object num_proj_shards
Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
ImplicitContainer<T> forget_bias
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. Must set it manually to `0.0` when restoring from CudnnLSTM trained checkpoints.
ImplicitContainer<T> state_is_tuple
If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. This latter behavior will soon be deprecated.
object activation
Activation function of the inner states. Default: `tanh`. It could also be string that is within Keras activation function names.
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.
object 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.
object dtype
Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`.
IDictionary<string, object> kwargs
Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, use `CudnnCompatibleLSTMCell` instead.

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.

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;