深度学习之文本图像化与数据预处理

            in_channels=1,  # 输入卷积层的图片通道数
            out_channels=20,  # 输出的通道数
            kernelSize=3,  # 卷积核的大小，长宽相等,3*3
            stride=1,  # 滑动步长为1
            padding=2  # 在输入张量周围补的边
            poolKernelSize=2 # 池化层的filters的大小2*2

from torch.nn import Module
from torch import nn


class Model(Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=3, stride=2, padding=2)    # w0 = 9
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2)  # w1 = 6
        self.conv2 = nn.Conv2d(6, 16, 2, padding=1)     # w2 = 3
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2)    # w3 = 4
        self.fc1 = nn.Linear(64, 120)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(120, 84)
        self.relu4 = nn.ReLU()
        self.fc3 = nn.Linear(84, 10)
        self.relu5 = nn.ReLU()

    def forward(self, x):
        y = self.conv1(x)
        y = self.relu1(y)
        y = self.pool1(y)
        y = self.conv2(y)
        y = self.relu2(y)
        y = self.pool2(y)
        y = y.view(y.shape[0], -1)
        y = self.fc1(y)
        y = self.relu3(y)
        y = self.fc2(y)
        y = self.relu4(y)
        y = self.fc3(y)
        y = self.relu5(y)
        return y