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)
Popular Pragmatic Bookshelf topics
Some minor things in the paper edition that says “3 2020” on the title page verso, not mentioned in the book’s errata online:
p. 186 But...
New
Following the steps described in Chapter 6 of the book, I’m stuck with running the migration as described on page 84:
bundle exec sequel...
New
The following is cross-posted from the original Ray Tracer Challenge forum, from a post by garfieldnate. I’m cross-posting it so that the...
New
I thought that there might be interest in using the book with Rails 6.1 and Ruby 2.7.2. I’ll note what I needed to do differently here.
...
New
#book-python-testing-with-pytest-second-edition
Hi. Thanks for writing the book. I am just learning so this might just of been an issue ...
New
When running tox for the first time, I got the following error:
ERROR: InterpreterNotFound: python3.10
I realised that I was running ...
New
Hi,
I am getting an error I cannot figure out on my test.
I have what I think is the exact code from the book, other than I changed “us...
New
Hi, I’ve got a question about the implementation of PubSub when using a Phoenix.Socket.Transport behaviour rather than channels.
Before ...
New
Book: Programming Phoenix LiveView, page 142 (157/378), file lib/pento_web/live/product_live/form_component.ex, in the function below:
d...
New
Docker-Machine became part of the Docker Toolbox, which was deprecated in 2020, long after Docker Desktop supported Docker Engine nativel...
New
Other popular topics
I know that these benchmarks might not be the exact picture of real-world scenario, but still I expect a Rust web framework performing a ...
New
Continuing the discussion from Thinking about learning Crystal, let’s discuss - I was wondering which languages don’t GC - maybe we can c...
New
Build efficient applications that exploit the unique benefits of a pure functional language, learning from an engineer who uses Haskell t...
New
Author Spotlight
Dmitry Zinoviev
@aqsaqal
Today we’re putting our spotlight on Dmitry Zinoviev, author of Data Science Essentials in ...
New
If you want a quick and easy way to block any website on your Mac using Little Snitch simply…
File > New Rule:
And select Deny, O...
New
Author Spotlight
Erin Dees
@undees
Welcome to our new author spotlight! We had the pleasure of chatting with Erin Dees, co-author of ...
New
Inside our android webview app, we are trying to paste the copied content from another app eg (notes) using navigator.clipboard.readtext ...
New
Author Spotlight:
VM Brasseur
@vmbrasseur
We have a treat for you today! We turn the spotlight onto Open Source as we sit down with V...
New
Jan | Rethink the Computer.
Jan turns your computer into an AI machine by running LLMs locally on your computer. It’s a privacy-focus, l...
New
Get the comprehensive, insider information you need for Rails 8 with the new edition of this award-winning classic.
Sam Ruby @rubys
...
New
Categories:
Sub Categories:
Popular Portals
- /elixir
- /rust
- /ruby
- /wasm
- /erlang
- /phoenix
- /keyboards
- /python
- /js
- /rails
- /security
- /go
- /swift
- /vim
- /clojure
- /java
- /emacs
- /haskell
- /svelte
- /onivim
- /typescript
- /kotlin
- /crystal
- /c-plus-plus
- /tailwind
- /react
- /gleam
- /ocaml
- /elm
- /flutter
- /vscode
- /ash
- /html
- /opensuse
- /zig
- /centos
- /deepseek
- /php
- /scala
- /react-native
- /sublime-text
- /textmate
- /lisp
- /debian
- /nixos
- /agda
- /django
- /kubuntu
- /arch-linux
- /deno
- /nodejs
- /revery
- /ubuntu
- /manjaro
- /spring
- /lua
- /diversity
- /markdown
- /julia
- /c









