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# how to use |
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```python |
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# !pip install transformers |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from huggingface_hub import PyTorchModelHubMixin |
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class Net(nn.Module,PyTorchModelHubMixin): |
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def __init__(self): |
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super().__init__() |
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self.conv1 = nn.Conv2d(3, 6, 5) |
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self.pool = nn.MaxPool2d(2, 2) |
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self.conv2 = nn.Conv2d(6, 16, 5) |
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self.fc1 = nn.Linear(16 * 5 * 5, 120) |
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self.fc2 = nn.Linear(120, 84) |
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self.fc3 = nn.Linear(84, 10) |
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def forward(self, x): |
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x = self.pool(F.relu(self.conv1(x))) |
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x = self.pool(F.relu(self.conv2(x))) |
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x = torch.flatten(x, 1) # flatten all dimensions except batch |
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x = F.relu(self.fc1(x)) |
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x = F.relu(self.fc2(x)) |
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x = self.fc3(x) |
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return x |
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net = Net.from_pretrained('Adapting/cifar10-image-classification') |
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``` |
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example codes for testing the model: [link](https://colab.research.google.com/drive/10xjbgSzw-U1Y4vCot5aqqdOi7AhmIkC3?usp=sharing) |