wehub-resource-sync
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
# %%
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# code by Tae Hwan Jung @graykode
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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def make_batch():
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input_batch = []
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target_batch = []
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for sen in sentences:
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word = sen.split() # space tokenizer
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input = [word_dict[n] for n in word[:-1]] # create (1~n-1) as input
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target = word_dict[word[-1]] # create (n) as target, We usually call this 'casual language model'
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input_batch.append(np.eye(n_class)[input])
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target_batch.append(target)
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return input_batch, target_batch
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class TextRNN(nn.Module):
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def __init__(self):
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super(TextRNN, self).__init__()
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self.rnn = nn.RNN(input_size=n_class, hidden_size=n_hidden)
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self.W = nn.Linear(n_hidden, n_class, bias=False)
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self.b = nn.Parameter(torch.ones([n_class]))
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def forward(self, hidden, X):
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X = X.transpose(0, 1) # X : [n_step, batch_size, n_class]
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outputs, hidden = self.rnn(X, hidden)
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# outputs : [n_step, batch_size, num_directions(=1) * n_hidden]
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# hidden : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
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outputs = outputs[-1] # [batch_size, num_directions(=1) * n_hidden]
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model = self.W(outputs) + self.b # model : [batch_size, n_class]
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return model
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if __name__ == '__main__':
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n_step = 2 # number of cells(= number of Step)
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n_hidden = 5 # number of hidden units in one cell
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sentences = ["i like dog", "i love coffee", "i hate milk"]
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word_list = " ".join(sentences).split()
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word_list = list(set(word_list))
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word_dict = {w: i for i, w in enumerate(word_list)}
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number_dict = {i: w for i, w in enumerate(word_list)}
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n_class = len(word_dict)
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batch_size = len(sentences)
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model = TextRNN()
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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input_batch, target_batch = make_batch()
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input_batch = torch.FloatTensor(input_batch)
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target_batch = torch.LongTensor(target_batch)
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# Training
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for epoch in range(5000):
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optimizer.zero_grad()
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# hidden : [num_layers * num_directions, batch, hidden_size]
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hidden = torch.zeros(1, batch_size, n_hidden)
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# input_batch : [batch_size, n_step, n_class]
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output = model(hidden, input_batch)
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# output : [batch_size, n_class], target_batch : [batch_size] (LongTensor, not one-hot)
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loss = criterion(output, target_batch)
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if (epoch + 1) % 1000 == 0:
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print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
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loss.backward()
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optimizer.step()
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input = [sen.split()[:2] for sen in sentences]
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# Predict
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hidden = torch.zeros(1, batch_size, n_hidden)
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predict = model(hidden, input_batch).data.max(1, keepdim=True)[1]
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print([sen.split()[:2] for sen in sentences], '->', [number_dict[n.item()] for n in predict.squeeze()]) |