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import torch | |
from torch import nn | |
from torch.autograd import Variable | |
import torch.nn.functional as F | |
class RNN(nn.Module): | |
def __init__(self, input_size, hidden_size, output_size, n_layers=1): | |
super(RNN, self).__init__() | |
self.input_size = input_size | |
self.hidden_size = hidden_size | |
self.output_size = output_size | |
self.n_layers = n_layers | |
self.c1 = nn.Conv1d(input_size, hidden_size, 2) | |
self.p1 = nn.AvgPool1d(2) | |
self.c2 = nn.Conv1d(hidden_size, hidden_size, 1) | |
self.p2 = nn.AvgPool1d(2) | |
self.gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=0.01) | |
self.out = nn.Linear(hidden_size, output_size) | |
def forward(self, inputs, hidden): | |
batch_size = inputs.size(1) | |
# Turn (seq_len x batch_size x input_size) into (batch_size x input_size x seq_len) for CNN | |
inputs = inputs.transpose(0, 1).transpose(1, 2) | |
# Run through Conv1d and Pool1d layers | |
c = self.c1(inputs) | |
p = self.p1(c) | |
c = self.c2(p) | |
p = self.p2(c) | |
# Turn (batch_size x hidden_size x seq_len) back into (seq_len x batch_size x hidden_size) for RNN | |
p = p.transpose(1, 2).transpose(0, 1) | |
p = F.tanh(p) | |
output, hidden = self.gru(p, hidden) | |
conv_seq_len = output.size(0) | |
output = output.view(conv_seq_len * batch_size, self.hidden_size) # Treating (conv_seq_len x batch_size) as batch_size for linear layer | |
output = F.tanh(self.out(output)) | |
output = output.view(conv_seq_len, -1, self.output_size) | |
return output, hidden | |
input_size = 20 | |
hidden_size = 50 | |
output_size = 7 | |
batch_size = 5 | |
n_layers = 2 | |
seq_len = 15 | |
rnn = RNN(input_size, hidden_size, output_size, n_layers=n_layers) | |
inputs = Variable(torch.rand(seq_len, batch_size, input_size)) # seq_len x batch_size x | |
outputs, hidden = rnn(inputs, None) | |
print('outputs', outputs.size()) # conv_seq_len x batch_size x output_size | |
print('hidden', hidden.size()) # n_layers x batch_size x hidden_size |
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Hi could you provide an e2e example with training for this