Solution is found at:
https://devtalk.com/t/machine-learning-in-elixir-chapter-1-doesnt-work-with-axon-0-7-page-26/173984
Explicitly converting the training and test sets to :f32 corrects the issue and the simulation can run.
feature_columns = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width"
]
label_column = "species"
x_train = Nx.stack(train_df[feature_columns], axis: 1)
|> Nx.as_type(:f32)
y_train =
train_df
|> DF.pull(label_column)
|> Explorer.Series.to_list()
|> Enum.map(fn
"Iris-setosa" -> 0
"Iris-versicolor" -> 1
"Iris-virginica" -> 2
end)
|> Nx.tensor(type: :u8)
|> Nx.new_axis(-1)
|> Nx.equal(Nx.iota({1, 3}, axis: -1))
|> Nx.as_type(:f32)
x_test = Nx.stack(test_df[feature_columns], axis: 1)
|> Nx.as_type(:f32)
y_test =
test_df
|> DF.pull(label_column)
|> Explorer.Series.to_list()
|> Enum.map(fn
"Iris-setosa" -> 0
"Iris-versicolor" -> 1
"Iris-virginica" -> 2
end)
|> Nx.tensor(type: :u8)
|> Nx.new_axis(-1)
|> Nx.equal(Nx.iota({1, 3}, axis: -1))
|> Nx.as_type(:f32)
