In the previous article, we defined the forward pass for our neural network. Now, we will provide...
PyTorch Part 4 teaches neural network testing: creating inputs, instantiating models, and visualizing outputs with matplotlib. Forward pass validation is essential for ML engineers—testing before deployment ensures models behave correctly in production.
In the previous article, we defined the forward pass for our neural network.
Now, we will provide inputs to the network so that we can test whether it works correctly.
Creating Input Values
To create a sequence of input values, we can define them like this:
input_doses = torch.linspace(start=0, end=1, steps=11)
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