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import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import matplotlib.pyplot as plt
def gen_data():
x_train = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
7.042, 10.791, 5.313, 7.997, 3.1], dtype=np.float32)
y_train = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827,
3.465, 1.65, 2.904, 1.3], dtype=np.float32)
assert x_train.shape == y_train.shape
n = x_train.shape[0]
x_train = x_train.reshape((n, -1))
y_train = y_train.reshape((n, -1))
return x_train, y_train
class LinearRegression(nn.Module):
def __init__(self, in_size, out_size):
super(LinearRegression, self).__init__()
self.linear_fn = nn.Linear(in_size, out_size)
def forward(self, x):
return self.linear_fn(x)
if __name__ == '__main__':
x_train, y_train = gen_data()
lr = 1e-3
n_epoch = 500
# build
in_size, out_size = x_train.shape[-1], y_train.shape[-1]
model = LinearRegression(in_size, out_size)
criterion = nn.MSELoss()
opt = optim.SGD(model.parameters(), lr=lr)
# train
for epoch in range(n_epoch):
inputs = Variable(torch.from_numpy(x_train))
outputs = Variable(torch.from_numpy(y_train))
opt.zero_grad()
preds = model(inputs)
loss = criterion(outputs, preds)
loss.backward()
opt.step()
if (epoch + 1) % 50 == 0:
# print(loss.data)
print('{}/{}, loss:{}'.format(epoch, n_epoch, loss.data))
print(model.state_dict())
y_pred = model(Variable(torch.from_numpy(x_train))).data.numpy()
plt.plot(x_train, y_train, 'ro')
plt.plot(x_train, y_pred)
plt.show()
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