[人工智能]Pytorch——深度神经网络

1.卷积层（以Conv2d为例）

注：输入的参数包括batchsize，通道数，图像宽度和高度，对于一般的图像通常只有宽度和高度两个参数，所以可以使用reshape函数改变尺寸。

例1：以张量为例

input = torch.Tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]
                      ])
print(input.shape)
input = torch.reshape(input, [1, 1, 3, 3])
kernal = torch.Tensor([[1, 2],
                       [3, 4]])
kernal = torch.reshape(kernal, [1, 1, 2, 2])
output = torch.nn.functional.conv2d(input, kernal, stride=1, padding=0)
print(output)

输出：

例2 ：单层神经网络

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 6, 3, 1)

    def forward(self, x):
        x = self.conv1(x)
        return x

2.池化层（以最大池化（下采样）为例）

注：stride若不设置默认值为kernal的尺寸

例3 ：以张量为例

input = torch.Tensor([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]
                      ])
print(input.shape)
input = torch.reshape(input, [1, 1, 3, 3])
kernal = torch.Tensor([[1, 2],
                       [3, 4]])
kernal = torch.reshape(kernal, [1, 1, 2, 2])

class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.maxpool1 = torch.nn.MaxPool2d(2, stride=1)

    def forward(self, x):
        x = self.maxpool1(x)
        return x

model1 = model()
output = model1(input)
print(output)

输出：

3.激活层（以ReLU为例）

4.实战

4.1 Model框架

class model(nn.Module):
    def __init__(self):
        super(model,self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size = (5, 5), stride = (1, 1), padding=2)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 32, (5, 5), padding=2)
        self.pool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(32, 64, (5, 5), padding=2)
        self.pool3 = nn.MaxPool2d(2)
        self.flatten1 = nn.Flatten()
        self.linaer1 = nn.Linear(1024, 64)
        self.linaer2 = nn.Linear(64, 10)

    def forward(self, input):
        input = self.conv1(input)
        input = self.pool1(input)
        input = self.conv2(input)
        input = self.pool2(input)
        input = self.conv3(input)
        input = self.pool3(input)
        input = self.flatten1(input)
        input = self.linaer1(input)
        input = self.linaer2(input)
        return input

model1  = model()
input = torch.ones((64, 3, 32, 32))
output = model1(input)
print(output.shape)

输出：

torch.Size([64, 10])

4.2 Sequential框架

class model(nn.Module):
    def __init__(self):
        super(model,self).__init__()
        self.model1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, (5, 5), padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, (5, 5), padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024, 64),
            nn.Linear(64, 10)
        )

    def forward(self, input):
        input = self.model1(input)
        return input

model1  = model()
input = torch.ones((64, 3, 32, 32))
output = model1(input)
print(output.shape)

输出：

torch.Size([64, 10])