RNN回归
import torch from torch import nn import numpy as np import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # Hyper Parameters TIME_STEP = 10 # rnn time step INPUT_SIZE = 1 # rnn input size LR = 0.02 # learning rate # show data steps = np.linspace(0, np.pi*2, 100, dtype=np.float32) # float32 for converting torch FloatTensor x_np = np.sin(steps) y_np = np.cos(steps) plt.plot(steps, y_np, 'r-', label='target (cos)') plt.plot(steps, x_np, 'b-', label='input (sin)') plt.legend(loc='best') plt.show() class RNN(nn.Module): def __init__(self): super(RNN, self).__init__() self.rnn = nn.RNN( input_size=INPUT_SIZE, hidden_size=32, # rnn hidden unit num_layers=1, # number of rnn layer batch_first=True, # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size) ) self.out = nn.Linear(32, 1) def forward(self, x, h_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) outs = [] # save all predictions for time_step in range(r_out.size(1)): # calculate output for each time step 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) optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters 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()
运行结果为:
用正弦曲线去拟合余弦曲线