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。

PyTorch PyTorch高階教程一

前面介紹了pytorch的一些基本用法，從這一節開始介紹pytorch在深度學習中的應用。在開始介紹之前，首先熟悉一下常用的概念和層。class torch.nn.module 舉例 import torch.nn as nn import torch.nn.functional as f clas...

PyTorch PyTorch高階教程三

前面介紹了使用pytorch構造cnn網路，這一節介紹點高階的東西lstm。以及我之前的一篇中文翻譯部落格 class torch.nn.lstm args,kwargs class rnn nn.module def init self,input size,hidden size,num lay...

Pytorch pytorch中的LSTM模型

pytorch中lstm的公式表示為 pytorch中lstm的定義如下 輸入資料格式 input seq len,batch,input size h0 num layers num directions,batch,hidden size c0 num layers num directions...