def f(x):
return 2 * d2l.sin(x) + x**0.8
from d2l import torch as d2l
import matplotlib.pyplot as plt
import torch
from torch import nn
#@tab mxnet, pytorch
T = 1000 # Generate a total of 1000 points
time = d2l.arange(1, T + 1, dtype=d2l.float32)
x = d2l.sin(0.01 * time) + d2l.normal(0, 0.2, (T, ))
d2l.plot(time, [x], 'time', 'x', xlim=[1, 1000], figsize=(6, 3))
plt.show()
#@tab mxnet, pytorch
tau = 4
features = d2l.zeros((T - tau, tau))
for i in range(tau):
features[:, i] = x[i:T - tau + i]
labels = d2l.reshape(x[tau:], (-1, 1))
batch_size, n_train = 16, 600
# Only the first `n_train` examples are used for training
train_iter = d2l.load_array((features[:n_train], labels[:n_train]),
batch_size,
is_train=True)
# Function for initializing the weights of the network
def init_weights(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)