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class WordLSTM(nn.Module):
def __init__(self, n_hidden=256, n_layers=4, drop_prob=0.3, lr=0.001):
self.drop_prob = drop_prob
self.n_layers = n_layers
self.n_hidden = n_hidden = lr
self.emb_layer = nn.Embedding(vocab_size, 200)
## define the LSTM
self.lstm = nn.LSTM(200, n_hidden, n_layers,
dropout=drop_prob, batch_first=True)
## define a dropout layer
self.dropout = nn.Dropout(drop_prob)
## define the fully-connected layer
self.fc = nn.Linear(n_hidden, vocab_size)
def forward(self, x, hidden):
''' Forward pass through the network.
These inputs are x, and the hidden/cell state `hidden`. '''
## pass input through embedding layer
embedded = self.emb_layer(x)
## Get the outputs and the new hidden state from the lstm
lstm_output, hidden = self.lstm(embedded, hidden)
## pass through a dropout layer
out = self.dropout(lstm_output)
#out = out.contiguous().view(-1, self.n_hidden)
out = out.reshape(-1, self.n_hidden)
## put "out" through the fully-connected layer
out = self.fc(out)
# return the final output and the hidden state
return out, hidden
def init_hidden(self, batch_size):
''' initializes hidden state '''
# Create two new tensors with sizes n_layers x batch_size x n_hidden,
# initialized to zero, for hidden state and cell state of LSTM
weight = next(self.parameters()).data
# if GPU is available
if (torch.cuda.is_available()):
hidden = (, batch_size, self.n_hidden).zero_().cuda(),, batch_size, self.n_hidden).zero_().cuda())
# if GPU is not available
hidden = (, batch_size, self.n_hidden).zero_(),, batch_size, self.n_hidden).zero_())
return hidden
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