import torch from torch import nn import numpy as np import matplotlib.pyplot as plt # 超参数 # Hyper Parameters TIME_STEP = 10 # rnn time step INPUT_SIZE = 1 # rnn input size LR = 0.02 # learning rate # 生成数据 # show data steps = np.linspace(0, np.pi * 2, 100, dtype=np.float32) # float32 for converting torch FloatTensor x_np = np.sin(steps) # 输入 y_np = np.cos(steps) # 目标 plt.plot(steps, y_np, 'r-', label='target (cos)') plt.plot(steps, x_np, 'b-', label='input (sin)') plt.legend(loc='best') plt.show() # 定义神经网络 # 对每一个 r_out 都得放到 Linear 中去计算出预测的 output, 所以能用一个 for loop 来循环计算. class RNN(nn.Module): def __init__(self): super(RNN, self).__init__() # 继承 __init__ 功能 self.rnn = nn.RNN( # 一个普通的 RNN input_size=INPUT_SIZE, hidden_size=32, # rnn hidden unit 32个神经元 num_layers=1, # number of rnn layer # 有几层 RNN layers batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size) # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size) ) self.out = nn.Linear(32, 1) def forward(self, x, h_state): # 因为 hidden state 是连续的, 所以要一直传递这一个 state # x (batch, time_step, input_size) # h_state (n_layers, batch, hidden_size) # r_out (batch, time_step, hidden_size) r_out, h_state = self.rnn(x, h_state) # h_state 也要作为 RNN 的一个输入 outs = [] # save all predictions # 保存所有时间点的预测值 for time_step in range(r_out.size(1)): # calculate output for each time step # 对每一个时间点计算 output outs.append(self.out(r_out[:, time_step, :])) return torch.stack(outs, dim=1), h_state # instead, for simplicity, you can replace above codes by follows # r_out = r_out.view(-1, 32) # outs = self.out(r_out) # outs = outs.view(-1, TIME_STEP, 1) # return outs, h_state # or even simpler, since nn.Linear can accept inputs of any dimension # and returns outputs with same dimension except for the last # outs = self.out(r_out) # return outs rnn = RNN() print(rnn) # 选择优化器 optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters # 选择损失函数 loss_func = nn.MSELoss() h_state = None # for initial hidden state plt.figure(1, figsize=(12, 5)) plt.ion() # continuously plot for step in range(100): start, end = step * np.pi, (step + 1) * np.pi # time range # use sin predicts cos steps = np.linspace(start, end, TIME_STEP, dtype=np.float32, endpoint=False) # float32 for converting torch FloatTensor x_np = np.sin(steps) y_np = np.cos(steps) x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis]) # shape (batch, time_step, input_size) y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis]) prediction, h_state = rnn(x, h_state) # rnn output # !! next step is important !! h_state = h_state.data # repack the hidden state, break the connection from last iteration loss = loss_func(prediction, y) # calculate loss optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients # plotting plt.plot(steps, y_np.flatten(), 'r-') plt.plot(steps, prediction.data.numpy().flatten(), 'b-') plt.draw(); plt.pause(0.05) plt.ioff() plt.show()
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