"""
Variable为tensor数据构建计算图，便于网络的运算
"""
import torch
from torch.autograd import Variable
tensor = torch.FloatTensor([[1,2],[3,4]]) # 创建一个tensor类型的数据
variable = Variable(tensor, requires_grad=True) # 创建一个variable类型的数据
print(tensor) # [torch.FloatTensor of size 2x2]
print(variable) # [torch.FloatTensor of size 2x2]
t_out = torch.mean(tensor*tensor)
v_out = torch.mean(variable*variable)
print(t_out)
print(v_out) # 7.5
v_out.backward() # 从v_out开始反向传播
# 计算谁的梯度，就让开始反向传播的变量对谁进行求导
# v_out = 1/4 * sum(variable*variable)
# the gradients w.r.t the variable, d(v_out)/d(variable) = 1/4*2*variable = variable/2
print(variable.grad)
'''
0.5000 1.0000
1.5000 2.0000
'''
print(variable) # variable格式
"""
Variable containing:
1 2
3 4
[torch.FloatTensor of size 2x2]
"""
print(variable.data) # tensor格式
"""
1 2
3 4
[torch.FloatTensor of size 2x2]
"""
print(variable.data.numpy()) # variable是Variable数据类型，variable.data是tensor类型，variable不可转换为numpy类型
"""
[[ 1. 2.]
[ 3. 4.]]
"""