djaouen
[Book Question] Machine Learning in Elixir: Poor accuracy for Chapter 1's example
Hello,
I am working through the examples in the new book, Machine Learning in Elixir and I am having an issue with poor accuracy in Chapter 1’s example. You can find the Livebook I created here: machine-learning-in-elixir/machine-learning-in-elixir-chapter-1.livemd at main · danieljaouen/machine-learning-in-elixir · GitHub
And here is the accuracy I am getting on my machine:
Batch: 0, accuracy: 0.0666667
%{
0 => %{
"accuracy" => #Nx.Tensor<
f32
0.06666667014360428
>
}
}
However, the training accuracy seems fine:
Epoch: 0, Batch: 450, accuracy: 0.8331868 loss: 0.5048826
Epoch: 1, Batch: 450, accuracy: 0.8779556 loss: 0.4173653
Epoch: 2, Batch: 450, accuracy: 0.9101056 loss: 0.3732252
Epoch: 3, Batch: 450, accuracy: 0.9288760 loss: 0.3434850
Epoch: 4, Batch: 450, accuracy: 0.9367946 loss: 0.3209158
Epoch: 5, Batch: 450, accuracy: 0.9416718 loss: 0.3026979
Epoch: 6, Batch: 450, accuracy: 0.9494675 loss: 0.2874412
Epoch: 7, Batch: 450, accuracy: 0.9583363 loss: 0.2743504
Epoch: 8, Batch: 450, accuracy: 0.9583363 loss: 0.2629215
Epoch: 9, Batch: 450, accuracy: 0.9626405 loss: 0.2528131
Not sure what I am doing wrong here. Any help? Thanks in advance!
Marked As Solved
grossvogel
I had a few more minutes to play with this, and so far it looks like we can get better results by processing the x and y data into tensors before splitting up test and training sets.
feature_columns = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
label_column = "species"
x_all = Nx.stack(shuffled_normalized_iris[feature_columns], axis: 1)
y_all =
shuffled_normalized_iris[label_column]
|> Explorer.Series.cast(:category)
|> Nx.stack(axis: -1)
|> Nx.equal(Nx.iota({1, 3}, axis: -1))
x_train = x_all[0..119]
x_test = x_all[120..149]
y_train = y_all[0..119]
y_test = y_all[120..149]
Also Liked
grossvogel
You can see what the model is predicting for the test data with Axon.predict/4
Axon.predict(model, trained_model_state, x_test)
grossvogel
I ran into this also, and decided it had to be some kind of typo with how the test set is set up. After a lot of head scratching, I think there’s a more subtle error with the setup of the test data. I believe when the species are assigned their positions in the one-hot encoding vector, that order is determined by the order in which the species are encountered in the test and training data.
For instance, if the species of the first 3 rows of the training set are "Iris-virginica", "Iris-setosa", ""Iris-versicolor", then those entries in the train_y data will look like [1, 0, 0], [0, 1, 0], [0, 0, 1] and the model will learn to predict [1, 0, 0] if the features match what it’s learned about “Iris-virginica.”
If the species are encountered in a different order in the test data, then we may end up with “Iris-virginica” having the 2nd position instead of the first in the test_y data, so the model will predict [1, 0, 0] but the scoring logic will be comparing against [0, 1, 0]
bdarla
Your code is correct (in accordance with the book).
In some runs, I also noticed low accuracy. This is because of the small dataset (150 samples).
If you re-run the steps from the shuffle step and below, then you will receive different results every time. In some cases, it can easily be 96% accuracy. Just, rerun the experiment.
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