import torch
from torch import nn
import numpy as np
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
# hyper parameters
batch_size =
64epoch =
1time_step =
28# 考慮多少個時間點的資料
input_size =
1# 每個時間點給rnn多少個資料點
lr =
0.01
class
rnn(nn.module)
:def
__init__
(self)
:super
(rnn, self)
.__init__(
) self.rnn = nn.rnn(
input_size=input_size,
hidden_size=32,
num_layers=1,
batch_first=
true,)
self.out = nn.linear(32,
1)defforward
(self, x, h_state)
:# x(batch, time_step, input_size)
# h_state(n_layers, batch, hidden_size)
# r_out(batch, time_step, output_size = hidden_size)
r_out, h_state = self.rnn(x, h_state)
outs =
for time_step in
range
(r_out.size(1)
):# size是tensor的形狀是乙個陣列，size(1)就是裡面的第二個值域，
# 就是time_step的值的個數 即第二個維度的大小
:, time_step,:]
))return torch.stack(outs, dim=1)
, h_state
rnn = rnn(
)print
(rnn)
optimizer = torch.optim.adam(rnn.parameters(
), lr=lr)
# optimize all cnn parameters
loss_func = nn.mseloss(
)h_state =
none
plt.figure(
1, figsize=(12
,5))
plt.ion(
)for step in
range(50
):start, end = step * np.pi,
(step +1)
* np.pi
# use sin pre cos
steps = np.linspace(start, end, time_step, dtype=np.float32)
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)
h_state = h_state.data # !!! this step is important
loss = loss_func(prediction, y)
optimizer.zero_grad(
)# clear gradient for next train
loss.backward(
)# back propagation, compute gradient
optimizer.step(
)# plot
plt.plot(steps, y_np.flatten(),
'r-'
) plt.plot(steps, prediction.data.numpy(
).flatten(),
'b-'
) plt.draw(
) plt.pause(
0.5)
plt.ioff(
)plt.show(
)
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