iprog4u
Machine Learning in Elixir: Chapter 1 - Unable to train model (page 19)
I am really enjoying the book so far but came across an issue in the first chapter. When running:
trained_model_state =
model
|> Axon.Loop.trainer(:categorical_cross_entropy, :sgd)
|> Axon.Loop.metric(:accuracy)
|> Axon.Loop.run(data_stream, %{}, iterations: 500, epochs: 10)
Still too new to debug this but it appears an issue with expecting either an f32 or f64 and getting the other and/or passing parameter maps rather than using Axon.ModelState{}:
13:42:29.641 [warning] passing parameter map to initialization is deprecated, use %Axon.ModelState{} instead
Epoch: 0, Batch: 0, accuracy: 0.4750000 loss: 0.0000000
** (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 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
Got value:
#Nx.Tensor<
f32[3]
>
"""
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 1.18.3) lib/enum.ex:1: Enumerable.impl_for!/1
(elixir 1.18.3) lib/enum.ex:166: Enumerable.reduce/3
(elixir 1.18.3) lib/enum.ex:4515: 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 6.2.2.1) 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 6.2.2.1) maps.erl:860: :maps.fold_1/4
(axon 0.7.0) lib/axon/model_state.ex:320: Inspect.Axon.ModelState.inspect/2
>, y_true: #Nx.Tensor<
u8[120][3]
>, y_pred: #Nx.Tensor<
f64[120][3]
>, loss:
<<<<< Expected <<<<<
#Nx.Tensor<
f32
>
==========
#Nx.Tensor<
f64
>
>>>>> Argument >>>>>
, optimizer_state: {%{scale: #Nx.Tensor<
f32
>}}, loss_scale_state: %{}}
(nx 0.10.0) lib/nx/defn.ex:342: anonymous fn/7 in Nx.Defn.compile_flatten/5
(nx 0.10.0) lib/nx/lazy_container.ex:73: anonymous fn/3 in Nx.LazyContainer.Map.traverse/3
(elixir 1.18.3) lib/enum.ex:1840: Enum."-map_reduce/3-lists^mapfoldl/2-0-"/3
(elixir 1.18.3) lib/enum.ex:1840: Enum."-map_reduce/3-lists^mapfoldl/2-0-"/3
(nx 0.10.0) lib/nx/lazy_container.ex:72: Nx.LazyContainer.Map.traverse/3
(nx 0.10.0) lib/nx/defn.ex:339: Nx.Defn.compile_flatten/5
(nx 0.10.0) lib/nx/defn.ex:331: anonymous fn/4 in Nx.Defn.compile/3
#cell:3r6bhsjthve53hp7:5: (file)
In my terminal running the livebook I get another warning:
[warning] passing parameter map to initialization is deprecated, use %Axon.ModelState{} instead
but I do not yet know how to do this. Please guide me in the right direction. Thank you.
Marked As Solved
iprog4u
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)
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