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Table Of Contents

rnn.graph

Description

Generate a RNN symbolic model - requires CUDA

Usage

rnn.graph(num_rnn_layer, input_size = NULL, num_embed = NULL, num_hidden,

  num_decode, dropout = 0, ignore_label = -1, bidirectional = F,

  loss_output = NULL, config, cell_type, masking = F,

  output_last_state = F, rnn.state = NULL, rnn.state.cell = NULL,

  prefix = "")

Arguments

Argument

Description

num_rnn_layer

int, number of stacked layers

input_size

int, number of levels in the data - only used for embedding

num_embed

int, default = NULL - no embedding. Dimension of the embedding vectors

num_hidden

int, size of the state in each RNN layer

num_decode

int, number of output variables in the decoding layer

dropout

config

Either seq-to-one or one-to-one

cell_type

Type of RNN cell: either gru or lstm

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