Quickstart Link to heading

Setup the model Link to heading

We basically start getting the training and test data from FashionMNIST which is one of the many available vision datasets for pytorch.

(dev) ~z➤ python 01.py
Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64

Then we create the model. Here we basically we use a lot nn.Module lib, it helps creating the NeuralNetwork and other parameters for itself. We are also using cuda (bcoz I have NVIDIA like the pros 💀) where the model will be processed. (dev) ~z➤ python 01.py

Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64
(dev) ~z➤ python 01.py
Using cuda device
NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear_relu_stack): Sequential(
    (0): Linear(in_features=784, out_features=512, bias=True)
    (1): ReLU()
    (2): Linear(in_features=512, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=10, bias=True)
  )
)

Train and Test Link to heading

With use of Loss Function and Optimizer we can train the model so it can make predictions and propagate results back in order to tune better the model’s params. We should also test the model to evaluate wether its learning or not.

The loss function gets the info on how well the model performed and the optimizer tunes the model to make it better the next time.

After training and testing the model during various epochs:

...
loss: 0.833402  [57664/60000]
Test Error:
 Accuracy: 70.0%, Avg loss: 0.819294

Epoch 10
-------------------------------
loss: 0.833612  [   64/60000]
loss: 0.903723  [ 6464/60000]
loss: 0.679648  [12864/60000]
loss: 0.873493  [19264/60000]
loss: 0.766314  [25664/60000]
loss: 0.765345  [32064/60000]
loss: 0.839947  [38464/60000]
loss: 0.817787  [44864/60000]
loss: 0.811614  [51264/60000]
loss: 0.804626  [57664/60000]
Test Error:
 Accuracy: 71.2%, Avg loss: 0.787830

Saving, Loading and Testing the model Link to heading

After training and validating the model, we can now save and load and manually test the model.

Done!
Predicted: "Ankle boot", Actual: "Ankle boot"

Check all of this examples in the file: 🔗 labs/01.py