
CoderDennis
Machine Learning in Elixir: chapter 7 CNN model accuracy no better than MLP (page 160)
When training the cnn_model, I get the following output:
Epoch: 0, Batch: 150, accuracy: 0.4985513 loss: 7.6424022
Epoch: 1, Batch: 163, accuracy: 0.4992854 loss: 7.6783161
Epoch: 2, Batch: 176, accuracy: 0.5000441 loss: 7.6865749
Epoch: 3, Batch: 139, accuracy: 0.4983259 loss: 7.6991839
Epoch: 4, Batch: 152, accuracy: 0.4988766 loss: 7.6995916
%{
"conv_0" => %{
"bias" => #Nx.Tensor<
f32[32]
EXLA.Backend<host:0, 0.1357844422.1979580433.82179>
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN]
>,
"kernel" => #Nx.Tensor<
f32[3][3][3][32]
EXLA.Backend<host:0, 0.1357844422.1979580433.82180>
[
[
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN],
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, ...],
...
],
...
],
...
]
>
},
"conv_1" => %{
"bias" => #Nx.Tensor<
f32[64]
EXLA.Backend<host:0, 0.1357844422.1979580433.82181>
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, -0.0071477023884654045, NaN, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, ...]
>,
"kernel" => #Nx.Tensor<
f32[3][3][32][64]
EXLA.Backend<host:0, 0.1357844422.1979580433.82182>
[
[
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, ...],
...
],
...
],
...
]
>
},
"conv_2" => %{
"bias" => #Nx.Tensor<
f32[128]
EXLA.Backend<host:0, 0.1357844422.1979580433.82183>
[0.0, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.005036031361669302, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, ...]
>,
"kernel" => #Nx.Tensor<
f32[3][3][64][128]
EXLA.Backend<host:0, 0.1357844422.1979580433.82184>
[
[
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, ...],
...
],
...
],
...
]
>
},
"dense_0" => %{
"bias" => #Nx.Tensor<
f32[128]
EXLA.Backend<host:0, 0.1357844422.1979580433.82185>
[NaN, -0.005992305930703878, -0.006005365401506424, -0.004664595704525709, NaN, NaN, NaN, -5.619042203761637e-4, 0.0, NaN, -0.005999671295285225, -6.131592726887902e-6, NaN, 0.0, NaN, 0.0, 0.0, NaN, NaN, -0.006002828478813171, -0.00600335793569684, 0.0, NaN, NaN, NaN, -0.006002923008054495, -0.006005282513797283, -0.00600528996437788, -0.0060048955492675304, -0.006004981696605682, NaN, -0.006004655733704567, -0.006005233619362116, NaN, -0.006004724185913801, -0.006005335133522749, -0.006005051080137491, -0.006004408933222294, NaN, -0.006005355156958103, 0.0, -0.006005344912409782, 0.0, NaN, -0.005991040728986263, ...]
>,
"kernel" => #Nx.Tensor<
f32[18432][128]
EXLA.Backend<host:0, 0.1357844422.1979580433.82186>
[
[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, ...],
...
]
>
},
"dense_1" => %{
"bias" => #Nx.Tensor<
f32[1]
EXLA.Backend<host:0, 0.1357844422.1979580433.82187>
[NaN]
>,
"kernel" => #Nx.Tensor<
f32[128][1]
EXLA.Backend<host:0, 0.1357844422.1979580433.82188>
[
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
[NaN],
...
]
>
}
}
The accuracy of the mlp_model was Batch: 6, accuracy: 0.5078125
and the accuracy of this cnn_model is Batch: 6, accuracy: 0.4944196
which was slightly worse instead of the expected “significantly better.”
I reviewed all the code to make sure I hadn’t missed anything, but I couldn’t find anything that didn’t match.
I’m guessing the NaN
s in the trained model state are a problem, but I’m not sure how to fix that.
Marked As Solved

CoderDennis
Switching to Axon 0.7 resolved the issue.
Popular Pragmatic Bookshelf topics

In Chapter 3, the source for index introduces Config on page 31, followed by more code including tests; Config isn’t introduced until pag...
New

Title: Web Development with Clojure, Third Edition - migrations/create not working: p159
When I execute the command:
user=> (create-...
New

Title: Hands-On Rust (Chap 8 (Adding a Heads Up Display)
It looks like
.with_simple_console_no_bg(SCREEN_WIDTH*2, SCREEN_HEIGHT*2...
New

Hi,
build fails on:
bracket-lib = “~0.8.1”
when running on Mac Mini M1 Rust version 1.5.0:
Compiling winit v0.22.2
error[E0308]: mi...
New

I am working on the “Your Turn” for chapter one and building out the restart button talked about on page 27. It recommends looking into ...
New

The book has the same “Problem space/Solution space” diagram on page 18 as is on page 17. The correct Problem/Solution space diagrams ar...
New

Hi,
I completed chapter 6 but am getting the following error when running:
thread 'main' panicked at 'Failed to load texture: IoError(O...
New

Modern Front-End Development for Rails - application does not start after run bin/setup (page xviii)
After some hassle, I was able to finally run bin/setup, now I have started the rails server but I get this error message right when I vis...
New

@mfazio23
I’m following the indications of the book and arriver ad chapter 10, but the app cannot be compiled due to an error in the Bas...
New

From page 13:
On Python 3.7, you can install the libraries with pip by running these commands inside a Python venv using Visual Studio ...
New
Other popular topics

I’ve been really enjoying obsidian.md:
It is very snappy (even though it is based on Electron). I love that it is all local by defaul...
New

You might be thinking we should just ask who’s not using VSCode :joy: however there are some new additions in the space that might give V...
New

Inspired by this post from @Carter, which languages, frameworks or other tech or tools do you think is killing it right now? :upside_down...
New

In case anyone else is wondering why Ruby 3 doesn’t show when you do asdf list-all ruby :man_facepalming: do this first:
asdf plugin-upd...
New

Seems like a lot of people caught it - just wondered whether any of you did?
As far as I know I didn’t, but it wouldn’t surprise me if I...
New

Intensively researching Erlang books and additional resources on it, I have found that the topic of using Regular Expressions is either c...
New

We’ve talked about his book briefly here but it is quickly becoming obsolete - so he’s decided to create a series of 7 podcasts, the firs...
New

Author Spotlight
Rebecca Skinner
@RebeccaSkinner
Welcome to our latest author spotlight, where we sit down with Rebecca Skinner, auth...
New

This is cool!
DEEPSEEK-V3 ON M4 MAC: BLAZING FAST INFERENCE ON APPLE SILICON
We just witnessed something incredible: the largest open-s...
New

Hair Salon Games for Girls Fun
Girls Hair Saloon game is mainly developed for kids. This game allows users to select virtual avatars to ...
New
Categories:
Sub Categories:
Popular Portals
- /elixir
- /rust
- /wasm
- /ruby
- /erlang
- /phoenix
- /keyboards
- /rails
- /js
- /python
- /security
- /go
- /swift
- /vim
- /clojure
- /emacs
- /haskell
- /java
- /onivim
- /typescript
- /svelte
- /kotlin
- /crystal
- /c-plus-plus
- /tailwind
- /react
- /gleam
- /ocaml
- /flutter
- /elm
- /vscode
- /ash
- /html
- /opensuse
- /centos
- /php
- /deepseek
- /zig
- /scala
- /textmate
- /sublime-text
- /lisp
- /debian
- /nixos
- /react-native
- /agda
- /kubuntu
- /arch-linux
- /django
- /ubuntu
- /revery
- /spring
- /manjaro
- /nodejs
- /diversity
- /deno
- /lua
- /julia
- /slackware
- /c