how to define a recognition CNN using torch with the 28*28 input size
时间: 2024-05-05 21:17:48 浏览: 3
Here is an example of how to define a recognition CNN using torch with 28*28 input size:
```
import torch.nn as nn
class RecognitionCNN(nn.Module):
def __init__(self):
super(RecognitionCNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.fc1 = nn.Linear(64 * 7 * 7, 128)
self.fc2 = nn.Linear(128, 10)
self.relu = nn.ReLU()
self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = self.relu(self.conv1(x))
x = self.max_pool(x)
x = self.relu(self.conv2(x))
x = self.max_pool(x)
x = x.view(-1, 64 * 7 * 7)
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x
```
In this example, we define a CNN with two convolutional layers and two fully connected layers. The input size is 28*28 and the output size is 10 (for 10 classes). The first convolutional layer has 32 filters with a kernel size of 3x3 and padding of 1. The second convolutional layer has 64 filters with a kernel size of 3x3 and padding of 1. The first fully connected layer has 128 neurons. We use ReLU activation function for all layers except for the last one. We also use max pooling with a kernel size of 2x2 and stride of 2.