《PyTorch深度学习实践》第12集

num_class = 4
input_size = 4
hidden_size = 8
embedding_size = 10
num_layers = 2
batch_size = 1
# seq_len = 5

idx2char = [‘e‘, ‘h‘, ‘l‘, ‘o‘]
x_data = [1, 0, 2, 2, 3]
y_data = [3, 1, 2, 3, 2]

inputs = torch.LongTensor(x_data)
labels = torch.LongTensor(y_data)

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        
        self.emb = torch.nn.Embedding(input_size, embedding_size)
        # If True, then the input and output tensors are provided as (batch, seq, feature). 
        self.rnn = torch.nn.RNN(input_size=embedding_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
        self.fc = torch.nn.Linear(hidden_size, num_class)
    
    def forward(self, x):
        hidden = torch.zeros(num_layers, batch_size, hidden_size)  # 这里也修改了
        x = self.emb(x)  # (seqlen, embedding_size)
        x = x.unsqueeze(0)  # 扩充一个维度batch：(batch, seqlen, embedding_size)
        x, _ = self.rnn(x, hidden)
        x = self.fc(x)
        return x.view(-1, num_class)


net = Model()

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.05)

for epoch in range(15):
    optimizer.zero_grad()
    outputs = net(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()
    
    _, idx = outputs.max(dim=1)
    idx = idx.data.numpy()
    print(‘Predicted: ‘, ‘‘.join([idx2char[x] for x in idx]), end=‘‘)
    print(‘, Epoch [%d/15] loss = %.4f‘ % (epoch+1, loss.item()))