这是之前的两篇：
这里给出一个完整的卷积神经网络，包含两个卷积层和两个全连接层。只是做了正向传播，并没有使用数据对该模型进行训练。
代码如下：
"""
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"""
def convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(in_channels=1, out_channels_1=10, out_channels_2=10, kernel_size_1=3, kernel_size_2=3, stride_1=1, stride_2=1, padding_1=0, padding_2=0, pooling=1, pooling_kernel_size=2, pooling_stride=2, input_size=1, hidden_size_1=10, hidden_size_2=10, output_size=1):
import torch
global model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers
class model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(torch.nn.Module):
def __init__(self):
super().__init__()
self.convolutional_layer_1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels_1, kernel_size=kernel_size_1, stride=stride_1, padding=padding_1)
self.convolutional_layer_2 = torch.nn.Conv2d(in_channels=out_channels_1, out_channels=out_channels_2, kernel_size=kernel_size_2, stride=stride_2, padding=padding_2)
self.pooling_layer = torch.nn.MaxPool2d(kernel_size=pooling_kernel_size, stride=pooling_stride)
self.hidden_layer_1 = torch.nn.Linear(input_size, hidden_size_1)
self.hidden_layer_2 = torch.nn.Linear(hidden_size_1, hidden_size_2)
self.output_layer = torch.nn.Linear(hidden_size_2, output_size)
def forward(self, x):
if pooling == 1:
channel_output_1 = torch.nn.functional.relu(self.pooling_layer(self.convolutional_layer_1(x)))
print('第一个卷积和池化后的维度：', channel_output_1.shape)
channel_output_2 = torch.nn.functional.relu(self.pooling_layer(self.convolutional_layer_2(channel_output_1)))
print('第二个卷积和池化后的维度：', channel_output_1.shape)
else:
channel_output_1 = torch.nn.functional.relu(self.convolutional_layer_1(x))
print('第一个卷积后的维度：', channel_output_1.shape)
channel_output_2 = torch.nn.functional.relu(self.convolutional_layer_2(channel_output_1))
print('第二个卷积后的维度：', channel_output_1.shape)
channel_output_2 = torch.flatten(channel_output_2, 1)
print('扁平后的维度：', channel_output_2.shape)
hidden_output_1 = torch.nn.functional.relu(self.hidden_layer_1(channel_output_2))
print('第一个全连接层后的维度：', hidden_output_1.shape)
hidden_output_2 = torch.nn.functional.relu(self.hidden_layer_2(hidden_output_1))
print('第二个全连接层后的维度：', hidden_output_1.shape)
output = self.output_layer(hidden_output_2)
print('输出数据的维度：', output.shape)
return output
model = model_class_of_convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers()
return model
import torch
input = torch.randn(15, 1, 28, 28)
print('【卷积和池化过程的维度变化】')
model = convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(in_channels=1, out_channels_1=10, out_channels_2=10, kernel_size_1=3, kernel_size_2=3, stride_1=1, stride_2=1, padding_1=1, padding_2=1, pooling=1, pooling_kernel_size=2, pooling_stride=2, input_size=10*7*7, hidden_size_1=10, hidden_size_2=10, output_size=1)
output = model(input)
print()
print('【卷积过程的维度变化（无池化）】')
model = convolutional_neural_network_with_two_convolutional_layers_and_two_fully_connected_layers(in_channels=1, out_channels_1=10, out_channels_2=10, kernel_size_1=3, kernel_size_2=3, stride_1=1, stride_2=1, padding_1=1, padding_2=1, pooling=0, pooling_kernel_size=2, pooling_stride=2, input_size=10*28*28, hidden_size_1=10, hidden_size_2=10, output_size=1)
output = model(input)运行结果：
【卷积和池化过程的维度变化】
第一个卷积和池化后的维度： torch.Size([15, 10, 14, 14])
第二个卷积和池化后的维度： torch.Size([15, 10, 14, 14])
扁平后的维度： torch.Size([15, 490])
第一个全连接层后的维度： torch.Size([15, 10])
第二个全连接层后的维度： torch.Size([15, 10])
输出数据的维度： torch.Size([15, 1])
【卷积过程的维度变化（无池化）】
第一个卷积后的维度： torch.Size([15, 10, 28, 28])
第二个卷积后的维度： torch.Size([15, 10, 28, 28])
扁平后的维度： torch.Size([15, 7840])
第一个全连接层后的维度： torch.Size([15, 10])
第二个全连接层后的维度： torch.Size([15, 10])
输出数据的维度： torch.Size([15, 1])【说明：本站主要是个人的一些笔记和代码分享，内容可能会不定期修改。为了使全网显示的始终是最新版本，这里的文章未经同意请勿转载。引用请注明出处：https://www.guanjihuan.com】
