AlexNet

In [ ]:

import torch
import torch.nn as nn
import torch.nn.functional as F

import torch import torch.nn as nn import torch.nn.functional as F

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class AlexNet(nn.Module):
    def __init__(self, dropout_p=0.5, num_label=10):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            # conv1
            nn.Conv2d(in_channels=1,out_channels=96,kernel_size=11,stride=4,padding=0),
            # input_size 224*224 padding=2
            # input_size 227*227 padding=0
            nn.ReLU(),
            # pool1
            nn.MaxPool2d(kernel_size=3, stride=2),
            # conv2
            nn.Conv2d(in_channels=96,out_channels=256,kernel_size=5,stride=1,padding=2),
            nn.ReLU(),
            # pool2
            nn.MaxPool2d(kernel_size=3, stride=2),
            # conv3
            nn.Conv2d(in_channels=256,out_channels=384,kernel_size=3,stride=1,padding=1),
            nn.ReLU(),
            # conv4
            nn.Conv2d(in_channels=384,out_channels=384,kernel_size=3,stride=1,padding=1),
            nn.ReLU(),
            # conv5
            nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,stride=1,padding=1),
            nn.ReLU(),
            # pool3
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Linear(in_features=256*6*6, out_features=4096),
            nn.ReLU(),
            nn.Dropout(dropout_p),
            nn.Linear(in_features=4096, out_features=4096),
            nn.ReLU(),
            nn.Dropout(dropout_p),
            nn.Linear(in_features=4096, out_features=num_label)
        )
    def forward(self, x):
        x = self.features(x)
        x = self.fc(x)
        return x

class AlexNet(nn.Module): def __init__(self, dropout_p=0.5, num_label=10): super(AlexNet, self).__init__() self.features = nn.Sequential( # conv1 nn.Conv2d(in_channels=1,out_channels=96,kernel_size=11,stride=4,padding=0), # input_size 224*224 padding=2 # input_size 227*227 padding=0 nn.ReLU(), # pool1 nn.MaxPool2d(kernel_size=3, stride=2), # conv2 nn.Conv2d(in_channels=96,out_channels=256,kernel_size=5,stride=1,padding=2), nn.ReLU(), # pool2 nn.MaxPool2d(kernel_size=3, stride=2), # conv3 nn.Conv2d(in_channels=256,out_channels=384,kernel_size=3,stride=1,padding=1), nn.ReLU(), # conv4 nn.Conv2d(in_channels=384,out_channels=384,kernel_size=3,stride=1,padding=1), nn.ReLU(), # conv5 nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,stride=1,padding=1), nn.ReLU(), # pool3 nn.MaxPool2d(kernel_size=3, stride=2), ) self.fc = nn.Sequential( nn.Flatten(), nn.Linear(in_features=256*6*6, out_features=4096), nn.ReLU(), nn.Dropout(dropout_p), nn.Linear(in_features=4096, out_features=4096), nn.ReLU(), nn.Dropout(dropout_p), nn.Linear(in_features=4096, out_features=num_label) ) def forward(self, x): x = self.features(x) x = self.fc(x) return x