| # Usage: python create_dummy_model.py <name_of_the_file> | |
| import sys | |
| import torch | |
| from torch import nn | |
| class NeuralNetwork(nn.Module): | |
| def __init__(self): | |
| super(NeuralNetwork, self).__init__() | |
| self.flatten = nn.Flatten() | |
| self.linear_relu_stack = nn.Sequential( | |
| nn.Linear(28 * 28, 512), | |
| nn.ReLU(), | |
| nn.Linear(512, 512), | |
| nn.ReLU(), | |
| nn.Linear(512, 10), | |
| ) | |
| def forward(self, x): | |
| x = self.flatten(x) | |
| logits = self.linear_relu_stack(x) | |
| return logits | |
| if __name__ == '__main__': | |
| jit_module = torch.jit.script(NeuralNetwork()) | |
| torch.jit.save(jit_module, sys.argv[1]) |