MaskedBasicLSTMCell - LostTech.TensorFlow Documentation

Type MaskedBasicLSTMCell

Namespace tensorflow.contrib.model_pruning

Basic LSTM recurrent network cell with pruning.

Overrides the call method of tensorflow BasicLSTMCell and injects the weight masks

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.

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.

Methods

NewDyn

Properties

Public static methods

MaskedBasicLSTMCell NewDyn(object num_units, ImplicitContainer<T> forget_bias, ImplicitContainer<T> state_is_tuple, object activation, object reuse, object name)

Initialize the basic LSTM cell with pruning.

Parameters

object num_units: int, The number of units in the LSTM cell.
ImplicitContainer<T> forget_bias: float, The bias added to forget gates (see above). Must set to `0.0` manually 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. The latter behavior will soon be deprecated.
object activation: Activation function of the inner states. Default: `tanh`.
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.
When restoring from CudnnLSTM-trained checkpoints, must 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;

InputSpec 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;

object output_size_dyn get;

object PythonObject get;

object rnncell_scope get; set;

string scope_name get;

object scope_name_dyn get;

object state_size get;

object state_size_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;