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README.md
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# Usage
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Register the model
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```python
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AutoConfig.register("mnist_classifier", MNISTConfig)
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AutoModel.register(MNISTConfig, MNISTClassifier)
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```
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```python
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from transformers import AutoConfig, AutoModel
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import torch
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# Usage
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Define the model and config
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```python
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from transformers import PreTrainedModel, PretrainedConfig
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import torch.nn as nn
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import torch.nn.functional as F
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class MNISTConfig(PretrainedConfig):
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model_type = "mnist_classifier"
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def __init__(self, input_size=784, hidden_size1=1024, hidden_size2=512, num_labels=10, **kwargs):
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super().__init__(**kwargs)
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self.input_size = input_size
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self.hidden_size1 = hidden_size1
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self.hidden_size2 = hidden_size2
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self.num_labels = num_labels
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class MNISTClassifier(PreTrainedModel):
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config_class = MNISTConfig
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def __init__(self, config):
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super().__init__(config)
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self.layer1 = nn.Linear(config.input_size, config.hidden_size1)
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self.layer2 = nn.Linear(config.hidden_size1, config.hidden_size2)
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self.layer3 = nn.Linear(config.hidden_size2, config.num_labels)
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def forward(self, pixel_values):
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inputs = pixel_values.view(-1, self.config.input_size)
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outputs = self.layer1(inputs)
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outputs = F.leaky_relu(outputs)
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outputs = self.layer2(outputs)
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outputs = F.leaky_relu(outputs)
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outputs = self.layer3(outputs)
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return outputs
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```
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Register the model
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```python
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AutoConfig.register("mnist_classifier", MNISTConfig)
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AutoModel.register(MNISTConfig, MNISTClassifier)
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```
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Run Inference
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```python
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from transformers import AutoConfig, AutoModel
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import torch
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