PyTorch PyTorch入門教程五

還是直接看**是如何寫的。

從numpy中建立輸入與輸出。

import torch

import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import variable
# hyper parameters
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001
# toy dataset 
x_train = np.array([[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.array([[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)

# linear regression model

class
linearregression
(nn.module):
def__init__
(self, input_size, output_size):
super(linearregression, self).__init__()
self.linear = nn.linear(input_size, output_size) 
defforward
(self, x):
out = self.linear(x)
return out
model = linearregression(input_size, output_size)

# loss and optimizer

criterion = nn.mseloss()
optimizer = torch.optim
.sgd(model.parameters(), lr=learning_rate)

# train the model 

for epoch in
range(num_epochs):
# convert numpy array
to torch variable
inputs = variable(torch.from_numpy(x_train))
targets = variable(torch.from_numpy(y_train))
# forward + backward + optimize
optimizer.zero_grad() 
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
if (epoch+1) % 5 == 0:
print ('epoch [%d/%d], loss: %.4f' 
%(epoch+1, num_epochs, loss.data[0]))

torch.from_numpy(ndarray)

>>> a = numpy.array([1, 2, 3])

>>> t = torch.from_numpy(a)
>>> t
torch.longtensor([1, 2, 3])
>>> t[0] = -1
>>> a
array([-1, 2, 3])

# plot the graph

predicted = model(variable(torch.from_numpy(x_train))).data.numpy()
plt.plot(x_train, y_train, 'ro', label='original
data')
plt.plot(x_train, predicted, label='fitted line')
plt.legend()
plt.show()

邏輯回歸可以看成是分類問題，線性回歸可以看成是擬合或者**問題。

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