<filename>zoo/alexnet.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
date  : 2019/01/17
author: lujie
"""

import torch

class AlexNet(torch.nn.Module):

    def __init__(self, num_classes = 10):

        super(AlexNet, self).__init__()

        self.features = torch.nn.Sequential(

            torch.nn.Conv2d(3, 64, kernel_size=5, stride=2, padding=2),
            # torch.nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            torch.nn.ReLU(inplace=True),
            torch.nn.MaxPool2d(kernel_size=3, stride=2),

            torch.nn.Conv2d(64, 192, kernel_size=5, padding=2),
            torch.nn.ReLU(inplace=True),
            torch.nn.MaxPool2d(kernel_size=3, stride=2),

            torch.nn.Conv2d(192, 384, kernel_size=3, padding=1),
            torch.nn.ReLU(inplace=True),

            torch.nn.Conv2d(384, 256, kernel_size=3, padding=1),
            torch.nn.ReLU(inplace=True),

            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),
            torch.nn.ReLU(inplace=True),
            torch.nn.MaxPool2d(kernel_size=3, stride=2),
        )

        self.avgpool = torch.nn.AdaptiveAvgPool2d((6, 6))

        self.classifier = torch.nn.Sequential(

            torch.nn.Dropout(),
            torch.nn.Linear(256 * 6 * 6, 4096),
            torch.nn.ReLU(inplace=True),

            torch.nn.Dropout(),
            torch.nn.Linear(4096, 4096),
            torch.nn.ReLU(inplace=True),

            torch.nn.Linear(4096, num_classes),
        )


    def forward(self, x):

        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)

        return x

<filename>zoo/alexnet.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
date  : 2019/01/17
author: lujie
"""

import torch

class AlexNet(torch.nn.Module):

    def __init__(self, num_classes = 10):

        super(AlexNet, self).__init__()

        self.features = torch.nn.Sequential(

            torch.nn.Conv2d(3, 64, kernel_size=5, stride=2, padding=2),
            # torch.nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            torch.nn.ReLU(inplace=True),
            torch.nn.MaxPool2d(kernel_size=3, stride=2),

            torch.nn.Conv2d(64, 192, kernel_size=5, padding=2),
            torch.nn.ReLU(inplace=True),
            torch.nn.MaxPool2d(kernel_size=3, stride=2),

            torch.nn.Conv2d(192, 384, kernel_size=3, padding=1),
            torch.nn.ReLU(inplace=True),

            torch.nn.Conv2d(384, 256, kernel_size=3, padding=1),
            torch.nn.ReLU(inplace=True),

            torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),
            torch.nn.ReLU(inplace=True),
            torch.nn.MaxPool2d(kernel_size=3, stride=2),
        )

        self.avgpool = torch.nn.AdaptiveAvgPool2d((6, 6))

        self.classifier = torch.nn.Sequential(

            torch.nn.Dropout(),
            torch.nn.Linear(256 * 6 * 6, 4096),
            torch.nn.ReLU(inplace=True),

            torch.nn.Dropout(),
            torch.nn.Linear(4096, 4096),
            torch.nn.ReLU(inplace=True),

            torch.nn.Linear(4096, num_classes),
        )


    def forward(self, x):

        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)

        return x

#!/usr/bin/env python3 # -*- coding: utf-8 -*- date  : 2019/01/17 author: lujie # torch.nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),