
ahamez
Machine Learning in Elixir: Chapter 1 doesn't work with Axon 0.7 (page 26)
Hello @seanmor5 ,
When upgrading Axon to 0.7, evaluation fails with:
** (ArgumentError) argument at position 3 is not compatible with compiled function template.
%{i: #Nx.Tensor<
s32
>, model_state: #Inspect.Error<
got Protocol.UndefinedError with message:
"""
protocol Enumerable not implemented for #Nx.Tensor<
f32[3]
> of type Nx.Defn.TemplateDiff (a struct). This protocol is implemented for the following type(s): Date.Range, Explorer.Series.Iterator, File.Stream, Function, GenEvent.Stream, HashDict, HashSet, IO.Stream, Kino.Control, Kino.Input, Kino.JS.Live, List, Map, MapSet, Range, Stream, Table.Mapper, Table.Zipper
"""
while inspecting:
%{
data: %{
"dense_0" => %{
"bias" => #Nx.Tensor<
f32[3]
>,
"kernel" => #Nx.Tensor<
f32[4][3]
>
}
},
state: %{},
__struct__: Axon.ModelState,
parameters: %{"dense_0" => ["bias", "kernel"]},
frozen_parameters: %{}
}
Stacktrace:
(elixir 1.17.2) lib/enum.ex:1: Enumerable.impl_for!/1
(elixir 1.17.2) lib/enum.ex:166: Enumerable.reduce/3
(elixir 1.17.2) lib/enum.ex:4423: Enum.reduce/3
(axon 0.7.0) lib/axon/model_state.ex:359: anonymous fn/2 in Inspect.Axon.ModelState.get_param_info/1
(stdlib 6.0) maps.erl:860: :maps.fold_1/4
(axon 0.7.0) lib/axon/model_state.ex:359: anonymous fn/2 in Inspect.Axon.ModelState.get_param_info/1
(stdlib 6.0) maps.erl:860: :maps.fold_1/4
(axon 0.7.0) lib/axon/model_state.ex:320: Inspect.Axon.ModelState.inspect/2
>, loss:
<<<<< Expected <<<<<
#Nx.Tensor<
f32
>
==========
#Nx.Tensor<
f64
>
>>>>> Argument >>>>>
, optimizer_state: {%{scale: #Nx.Tensor<
f32
>}}, loss_scale_state: %{}, y_true: #Nx.Tensor<
u8[120][3]
>, y_pred: #Nx.Tensor<
f64[120][3]
>}
(nx 0.9.1) lib/nx/defn.ex:342: anonymous fn/7 in Nx.Defn.compile_flatten/5
(nx 0.9.1) lib/nx/lazy_container.ex:73: anonymous fn/3 in Nx.LazyContainer.Map.traverse/3
(elixir 1.17.2) lib/enum.ex:1829: Enum."-map_reduce/3-lists^mapfoldl/2-0-"/3
(elixir 1.17.2) lib/enum.ex:1829: Enum."-map_reduce/3-lists^mapfoldl/2-0-"/3
(nx 0.9.1) lib/nx/lazy_container.ex:72: Nx.LazyContainer.Map.traverse/3
(nx 0.9.1) lib/nx/defn.ex:339: Nx.Defn.compile_flatten/5
(nx 0.9.1) lib/nx/defn.ex:331: anonymous fn/4 in Nx.Defn.compile/3
#cell:75r7qhxms33esmsj:5: (file)
It seems it’s a problem between the type of the iris dataset (:f64
) whereas :f32
is expected.
Marked As Solved

kubafu
I stumbled upon it too and fixed it with explicit conversion to :f32, i.e.
x_train =
train_data[@columns]
|> Nx.stack(axis: 1)
|> Nx.as_type(:f32)
y_train =
train_data["species"]
|> Nx.stack(axis: -1)
|> Nx.equal(Nx.iota({1, 3}, axis: -1))
x_test =
test_data[@columns]
|> Nx.stack(axis: 1)
|> Nx.as_type(:f32)
y_test =
test_data["species"]
|> Nx.stack(axis: -1)
|> Nx.equal(Nx.iota({1, 3}, axis: -1))
6
Also Liked

nunez
Cool! I updated it at the dataframe level since Nx still scares me.
cols = ~w(sepal_width sepal_length petal_length petal_width)
normalized_iris =
DF.mutate(
iris,
for col <- across(^cols) do
{col.name, Explorer.Series.cast((col - mean(col)) / standard_deviation(col), :f32)}
end
)
2
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