加载资源和生成数据
import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inlineplt.figure(figsize=(8,5))# how many time steps/data pts are in one batch of data
seq_length = 20# generate evenly spaced data pts
time_steps = np.linspace(0, np.pi, seq_length + 1)
data = np.sin(time_steps)
data.resize((seq_length + 1, 1)) # size becomes (seq_length+1, 1), adds an input_size dimensionx = data[:-1] # all but the last piece of data
y = data[1:] # all but the first# display the data
plt.plot(time_steps[1:], x, 'r.', label='input, x') # x
plt.plot(time_steps[1:], y, 'b.', label='target, y') # yplt.legend(loc='best')
plt.show()定义RNN网络
class RNN(nn.Module):def __init__(self, input_size, output_size, hidden_dim, n_layers):super(RNN, self).__init__()self.hidden_dim=hidden_dim# define an RNN with specified parameters# batch_first means that the first dim of the input and output will be the batch_sizeself.rnn = nn.RNN(input_size, hidden_dim, n_layers, batch_first=True)# last, fully-connected layerself.fc = nn.Linear(hidden_dim, output_size)def forward(self, x, hidden):# x (batch_size, seq_length, input_size)# hidden (n_layers, batch_size, hidden_dim)# r_out (batch_size, time_step, hidden_size)batch_size = x.size(0)# get RNN outputsr_out, hidden = self.rnn(x, hidden)# shape output to be (batch_size*seq_length, hidden_dim)r_out = r_out.view(-1, self.hidden_dim) # get final output output = self.fc(r_out)return output, hidden
检查输入和输出维度
# test that dimensions are as expected
test_rnn = RNN(input_size=1, output_size=1, hidden_dim=10, n_layers=2)# generate evenly spaced, test data pts
time_steps = np.linspace(0, np.pi, seq_length)
data = np.sin(time_steps)
data.resize((seq_length, 1))test_input = torch.Tensor(data).unsqueeze(0) # give it a batch_size of 1 as first dimension
print('Input size: ', test_input.size())# test out rnn sizes
test_out, test_h = test_rnn(test_input, None)
print('Output size: ', test_out.size())
print('Hidden state size: ', test_h.size())训练网络
# decide on hyperparameters
input_size=1
output_size=1
hidden_dim=32
n_layers=1# instantiate an RNN
rnn = RNN(input_size, output_size, hidden_dim, n_layers)
print(rnn)损失函数和优化器
# MSE loss and Adam optimizer with a learning rate of 0.01
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(rnn.parameters(), lr=0.01) 定义训练函数
# train the RNN
def train(rnn, n_steps, print_every):# initialize the hidden statehidden = None for batch_i, step in enumerate(range(n_steps)):# defining the training data time_steps = np.linspace(step * np.pi, (step+1)*np.pi, seq_length + 1)data = np.sin(time_steps)data.resize((seq_length + 1, 1)) # input_size=1x = data[:-1]y = data[1:]# convert data into Tensorsx_tensor = torch.Tensor(x).unsqueeze(0) # unsqueeze gives a 1, batch_size dimensiony_tensor = torch.Tensor(y)# outputs from the rnnprediction, hidden = rnn(x_tensor, hidden)## Representing Memory ### make a new variable for hidden and detach the hidden state from its history# this way, we don't backpropagate through the entire historyhidden = hidden.data# calculate the lossloss = criterion(prediction, y_tensor)# zero gradientsoptimizer.zero_grad()# perform backprop and update weightsloss.backward()optimizer.step()# display loss and predictionsif batch_i%print_every == 0: print('Loss: ', loss.item())plt.plot(time_steps[1:], x, 'r.') # inputplt.plot(time_steps[1:], prediction.data.numpy().flatten(), 'b.') # predictionsplt.show()return rnn
# train the rnn and monitor results
n_steps = 75
print_every = 15trained_rnn = train(rnn, n_steps, print_every)