import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.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)
x = 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') # y
plt.legend(loc='best')
plt.show()势力范围
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_size
< = nn.RNN(input_size, hidden_dim, n_layers, batch_first=True)
# last, fully-connected layer
self.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 outputs
r_out, hidden = (x, hidden)
# shape output to be (batch_size*seq_length, hidden_dim)
r_out = r_out.view(-1, self.hidden_dim)
# get final output雪白血红张正隆
output = self.fc(r_out)
return output, hidden
# 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)
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
坚守与变通
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
麦克白夫人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)
# 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 state
hidden = 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)
x = data[:-1]
y = data[1:]
# convert data into Tensors
x_tensor = torch.Tensor(x).unsqueeze(0) # unsqueeze gives a 1, batch_size dimension y_tensor = torch.Tensor(y)
# outputs from the rnn
prediction, 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 history
hidden = hidden.data
# calculate the loss
loss = criterion(prediction, y_tensor)
# zero gradients
温敏<_grad()
# perform backprop and update weights
loss.backward()
optimizer.step()
mh370写给2014的一封信# display loss and predictions
if batch_i%print_every == 0:
print('Loss: ', loss.item())
plt.plot(time_steps[1:], x, 'r.') # input
plt.plot(time_steps[1:], prediction.data.numpy().flatten(), 'b.') # predictions
plt.show()
return rnn
# train the rnn and monitor results
n_steps = 75
print_every = 15
trained_rnn = train(rnn, n_steps, print_every)
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