Python resnet18

ResNet-18 is a popular neural network architecture used for image classification tasks. It was introduced in the 2015 paper "Deep Residual Learning for Image Recognition" by Kaiming He et al.

To implement ResNet-18 in Python using a deep learning framework like PyTorch or TensorFlow, you can follow these steps:

1. Import the necessary libraries:

import torch
import torch.nn as nn

2. Define the ResNet-18 architecture:

class ResNet18(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(num_features=64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(num_features=64),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(num_features=64),
            nn.ReLU(inplace=True)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(num_features=128),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(num_features=128),
            nn.ReLU(inplace=True)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(inplace=True)
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(num_features=512),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(num_features=512),
            nn.ReLU(inplace=True)
        )
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        self.fc = nn.Linear(in_features=512, out_features=num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x

3. Instantiate the ResNet-18 model:

model = ResNet18(num_classes=10)

4. Train the model on your dataset using a suitable optimizer and loss function.

Note: This is just a basic implementation of ResNet-18 in Python using PyTorch. You can modify this architecture or use different deep learning frameworks as per your requirements.