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Variable Length Sequence for RNN in pytorch Example
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import torch | |
import torch.nn as nn | |
from torch.autograd import Variable | |
batch_size = 3 | |
max_length = 3 | |
hidden_size = 2 | |
n_layers =1 | |
# container | |
batch_in = torch.zeros((batch_size, max_length, 1)) | |
#data | |
vec_1 = torch.FloatTensor([[1, 2, 3]]) | |
vec_2 = torch.FloatTensor([[1, 2, 0]]) | |
vec_3 = torch.FloatTensor([[1, 0, 0]]) | |
batch_in[0] = vec_1 | |
batch_in[1] = vec_2 | |
batch_in[2] = vec_3 | |
batch_in = Variable(batch_in) | |
seq_lengths = [3,2,1] | |
pack = torch.nn.utils.rnn.pack_padded_sequence(batch_in, seq_lengths, batch_first=True) | |
print(batch_in.size()) # >>> torch.Size([3, 3, 1]) | |
rnn = nn.RNN(1, hidden_size, n_layers, batch_first=True) | |
h0 = Variable(torch.randn(n_layers, batch_size, hidden_size)) | |
out, _ = rnn(pack, h0) | |
unpacked, unpacked_len = torch.nn.utils.rnn.pad_packed_sequence(out) | |
print(unpacked.size()) # >>> torch.Size([3, 3, 2]) | |
print(unpacked_len) # >>> [3, 2, 1] | |
print(unpacked[2, ...]) | |
# >>> | |
# Variable containing: | |
# -0.0818 -0.4678 | |
# 0.0000 0.0000 | |
# 0.0000 0.0000 | |
# [torch.FloatTensor of size 3x2] |
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