84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
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import torch.nn as nn
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import captcha_settings
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class ConvNet(nn.Module):
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def __init__(self):
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super(ConvNet, self).__init__()
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self.layer1 = nn.Sequential(
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nn.Conv2d(1, 64, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2),
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)
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self.layer2 = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2),
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)
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self.layer3 = nn.Sequential(
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nn.Conv2d(128, 256, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2),
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)
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self.layer4 = nn.Sequential(
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nn.Conv2d(256, 512, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2),
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)
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self.layer5 = nn.Sequential(
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nn.Flatten(),
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nn.Linear(in_features=15360, out_features=4096),
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nn.Dropout(0.5),
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nn.ReLU(),
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nn.Linear(
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4096,
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captcha_settings.MAX_CAPTCHA * captcha_settings.ALL_CHAR_SET_LEN,
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),
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)
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def forward(self, x):
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.layer5(x)
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return x
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# class ConvNet(nn.Module):
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# def __init__(self):
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# super(ConvNet, self).__init__()
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# self.layer1 = nn.Sequential(
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# nn.Conv2d(1, 32, kernel_size=3, padding=1),
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# nn.BatchNorm2d(32),
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# nn.Dropout(0.5), # drop 50% of the neuron
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# nn.ReLU(),
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# nn.MaxPool2d(2))
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# self.layer2 = nn.Sequential(
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# nn.Conv2d(32, 64, kernel_size=3, padding=1),
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# nn.BatchNorm2d(64),
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# nn.Dropout(0.5), # drop 50% of the neuron
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# nn.ReLU(),
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# nn.MaxPool2d(2))
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# self.layer3 = nn.Sequential(
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# nn.Conv2d(64, 64, kernel_size=3, padding=1),
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# nn.BatchNorm2d(64),
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# nn.Dropout(0.5), # drop 50% of the neuron
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# nn.ReLU(),
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# nn.MaxPool2d(2))
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# self.fc = nn.Sequential(
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# nn.Linear((captcha_settings.IMAGE_WIDTH//8)*(captcha_settings.IMAGE_HEIGHT//8)*64, 1024),
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# nn.Dropout(0.5), # drop 50% of the neuron
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# nn.ReLU())
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# self.rfc = nn.Sequential(
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# nn.Linear(1024, captcha_settings.MAX_CAPTCHA*captcha_settings.ALL_CHAR_SET_LEN),
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# )
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# def forward(self, x):
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# out = self.layer1(x)
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# out = self.layer2(out)
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# out = self.layer3(out)
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# out = out.view(out.size(0), -1)
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# out = self.fc(out)
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# out = self.rfc(out)
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# return out
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