LSTMBlockCell - LostTech.TensorFlow Documentation

Type LSTMBlockCell

Namespace tensorflow.contrib.rnn

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 `rnn_cell_impl.LSTMCell`, this is a monolithic op and should be much faster. The weight and bias matrices should be compatible as long as the variable scope matches.

Methods

NewDyn

Properties

Public static methods

LSTMBlockCell NewDyn(object num_units, ImplicitContainer<T> forget_bias, object cell_clip, ImplicitContainer<T> use_peephole, object dtype, object reuse, ImplicitContainer<T> name)

Initialize the LSTM cell.

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).
object cell_clip: clip the cell to this value. Defaults is no cell clipping.
ImplicitContainer<T> use_peephole: Whether to use peephole connections or not.
object dtype: the dtype of variables of this layer.
object reuse: (optional) 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.
ImplicitContainer<T> 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. By default this is "lstm_cell", for variable-name compatibility with `tf.compat.v1.nn.rnn_cell.LSTMCell`.

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;

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

object output_size_dyn get;