From 85f2ba57683846cdbf8d1a259ff5969679283734 Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Thu, 4 Apr 2024 00:52:29 +0800 Subject: [PATCH] Create example_of_convolutional_neural_network.py --- ...example_of_convolutional_neural_network.py | 52 +++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 2024.04.04_example_of_convolutional_neural_network/example_of_convolutional_neural_network.py diff --git a/2024.04.04_example_of_convolutional_neural_network/example_of_convolutional_neural_network.py b/2024.04.04_example_of_convolutional_neural_network/example_of_convolutional_neural_network.py new file mode 100644 index 0000000..c5d9a83 --- /dev/null +++ b/2024.04.04_example_of_convolutional_neural_network/example_of_convolutional_neural_network.py @@ -0,0 +1,52 @@ +""" +This code is supported by the website: https://www.guanjihuan.com +The newest version of this code is on the web page: https://www.guanjihuan.com/archives/39368 +""" + +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) \ No newline at end of file