DL中的OurModule()

class OurModule(nn.Module):
    def __init__(self, num_inputs, num_classes, dropout_prob=0.3):  
        super(OurModule, self).__init__()
        self.pipe = nn.Sequential(
            nn.Linear(num_inputs, 5),
            nn.ReLU(),
            nn.Linear(5, 20),
            nn.ReLU(),
            nn.Linear(20, num_classes),
            nn.Dropout(p=dropout_prob),
            nn.Softmax(dim=1)
        )

    def forward(self, x):
        return self.pipe(x)

上面参数主要介绍下dropout_prob，模型训练时应用Dropout的流程，概况一下描述就是：
1.随机概率p随机dropout部分神经元，并前向传播
2.计算前向传播的损失，应用反向传播和梯度更新(对剩余的未被dropout的神经元)
3.恢复所有神经元的，并重复过程1

if __name__ == "__main__":
    net = OurModule(num_inputs=2, num_classes=3)
    print(net)
    v = torch.FloatTensor([[2, 3]])
    out = net(v)
    print(out)
    print("Cuda's availability is %s" % torch.cuda.is_available())
    if torch.cuda.is_available():
        print("Data from cuda: %s" % out.to('cuda'))

输入参数为Tensor[2,3]，经过三层NN，最后输出softmax。

import torch as t
from torch import nn
from torch.nn import functional as F

# 假定输入的图像形状为[3,64,64]
x = t.randn(10, 3, 64, 64)      # 10张 3个channel 大小为64x64的图片

x = nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0)(x)
print(x.shape)


# 之前的特征图尺寸为多少，只要设置为(1,1)，那么最终特征图大小都为(1,1) 
# x = F.adaptive_avg_pool2d(x, [1,1])    # [b, 64, h, w] => [b, 64, 1, 1]
# print(x.shape)

# 将四维张量转换为二维张量之后，才能作为全连接层的输入
x = x.view(x.size(0), -1)  #view()的作用相当于numpy中的reshape，重新定义矩阵的形状。
print(x.shape)

# in_features由输入张量的形状决定，out_features则决定了输出张量的形状 
connected_layer = nn.Linear(in_features = 64*21*21, out_features = 10)

# 调用全连接层
output = connected_layer(x) 
print(output.shape)