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Contents
module Torch
module NN
class GRU < RNNBase
def initialize(*args, **options)
super("GRU", *args, **options)
end
def run_impl(input, hx, batch_sizes)
if batch_sizes.nil?
Torch.gru(input, hx, _get_flat_weights, @bias, @num_layers,
@dropout, @training, @bidirectional, @batch_first)
else
Torch.gru(input, batch_sizes, hx, _get_flat_weights, @bias,
@num_layers, @dropout, @training, @bidirectional)
end
end
def forward_impl(input, hx, batch_sizes, max_batch_size, sorted_indices)
if hx.nil?
num_directions = @bidirectional ? 2 : 1
hx = Torch.zeros(@num_layers * num_directions, max_batch_size, @hidden_size, dtype: input.dtype, device: input.device)
else
# Each batch of the hidden state should match the input sequence that
# the user believes he/she is passing in.
hx = permute_hidden(hx, sorted_indices)
end
check_forward_args(input, hx, batch_sizes)
result = run_impl(input, hx, batch_sizes)
output = result[0]
hidden = result[1]
[output, hidden]
end
def forward_tensor(input, hx: nil)
batch_sizes = nil
max_batch_size = @batch_first ? input.size(0) : input.size(1)
sorted_indices = nil
unsorted_indices = nil
output, hidden = forward_impl(input, hx, batch_sizes, max_batch_size, sorted_indices)
[output, permute_hidden(hidden, unsorted_indices)]
end
def forward(input, hx: nil)
forward_tensor(input, hx: hx)
end
end
end
end
Version data entries
54 entries across 54 versions & 1 rubygems