INNER CODE UNIT · Python

forward

maxpumperla/learning_ray · notebooks/gradio_demo.py:16

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1)  # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


from ray.train.torch import TorchCheckpoint, TorchPredictor

CHECKPOINT_PATH = "torch_checkpoint"
checkpoint = TorchCheckpoint.from_directory(CHECKPOINT_PATH)
predictor = TorchPredictor.from_checkpoint(
    checkpoint=checkpoint,
    model=Net()
)

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