如何进行LSTM总结及sin与cos拟合应用

如何进行LSTM总结及sin与cos拟合应用，相信很多没有经验的人对此束手无策，为此本文总结了问题出现的原因和解决方法，通过这篇文章希望你能解决这个问题。

一、LSTM总结

RNN在实际应用中，无法处理无关的信息，很难处理长距离的依赖。LSTM思路，在原始RNN的隐藏层只有一个状态h，它对短期的输入非常敏感，那么，我们再增加一个状态c, 它来保存长期的状态。其结构如下：

与RNN比较，

定义LSTM类如下：

class RNN(nn.Module):    def __init__(self):        super(RNN, self).__init__()        self.rnn = nn.LSTM(            input_size=INPUT_SIZE,            hidden_size=32,            num_layers=1,            batch_first=True        )        self.out = nn.Linear(32, 1)    def forward(self, x, h_state, c_state):        r_out, (h_state, c_state) = self.rnn(x, (h_state, c_state))        out = self.out(r_out).squeeze()        return out, h_state, c_state

改进GRU版本： (Gated Recurrent Unit)

二、sin与cos拟合应用

import torchfrom torch import nnimport numpy as npimport matplotlib.pyplot as pltTIME_STEP = 10INPUT_SIZE = 1learning_rate = 0.001class RNN(nn.Module):    def __init__(self):        super(RNN, self).__init__()        self.rnn = nn.LSTM(            input_size=INPUT_SIZE,            hidden_size=32,            num_layers=1,            batch_first=True        )        self.out = nn.Linear(32, 1)    def forward(self, x, h_state, c_state):        r_out, (h_state, c_state) = self.rnn(x, (h_state, c_state))        out = self.out(r_out).squeeze()        return out, h_state, c_staternn = RNN()criterion = nn.MSELoss()optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate)h_state = torch.randn(1, 1, 32)c_state = torch.randn(1, 1, 32)plt.figure(1, figsize=(12, 5))plt.ion()for step in range(100):    start, end = step * np.pi, (step + 1) * np.pi    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32, endpoint=False)    x_np = np.sin(steps) # x_np.shape: 10    y_np = np.cos(steps) # y_np.shape: 10    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    y = torch.from_numpy(y_np)    prediction, h_state, c_state = rnn(x, h_state, c_state)    h_state = h_state.data    c_state = c_state.data    loss = criterion(prediction, y)    optimizer.zero_grad()    loss.backward()    optimizer.step()    plt.plot(steps, y_np.flatten(), 'r-')    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')    plt.draw()    plt.pause(.05)plt.ioff()plt.show()

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