PyTorch学习（6）—快速搭建法_import torchfrom torch.autograd import variableimp

本篇博客主要介绍如何在PyTorch中更加快速便捷地搭建神经网络。

示例代码：

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
 
# 生成假数据
n_data = torch.ones(100, 2)
x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)
y0 = torch.zeros(100)               # class0 y data (tensor), shape=(100, 1)
x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)
y1 = torch.ones(100)                # class1 y data (tensor), shape=(100, 1)
x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floating
y = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,) LongTensor = 64-bit integer
 
# 将Tensor转换为torch
x, y = Variable(x), Variable(y)
 
# 打印数据散点图
# plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, l