CoderDennis

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 NaNs in the trained model state are a problem, but I’m not sure how to fix that.

Marked As Solved

CoderDennis

CoderDennis

Switching to Axon 0.7 resolved the issue.

Where Next?

Popular Pragmatic Bookshelf topics Top

telemachus
Python Testing With Pytest - Chapter 2, warnings for “unregistered custom marks” While running the smoke tests in Chapter 2, I get these...
New
GilWright
Working through the steps (checking that the Info,plist matches exactly), run the demo game and what appears is grey but does not fill th...
New
herminiotorres
Hi @Margaret , On page VII the book tells us the example and snippets will be all using Elixir version 1.11 But on page 3 almost the en...
New
rmurray10127
Title: Intuitive Python: docker run… denied error (page 2) Attempted to run the docker command in both CLI and Powershell PS C:\Users\r...
New
adamwoolhether
When trying to generate the protobuf .go file, I receive this error: Unknown flag: --go_opt libprotoc 3.12.3 MacOS 11.3.1 Googling ...
New
patoncrispy
I’m new to Rust and am using this book to learn more as well as to feed my interest in game dev. I’ve just finished the flappy dragon exa...
New
brunogirin
When I run the coverage example to report on missing lines, I get: pytest --cov=cards --report=term-missing ch7 ERROR: usage: pytest [op...
New
taguniversalmachine
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
Henrai
Hi, I’m working on the Chapter 8 of the book. After I add add the point_offset, I’m still able to see acne: In the image above, I re...
New
gorkaio
root_layout: {PentoWeb.LayoutView, :root}, This results in the following following error: no “root” html template defined for PentoWeb...
New

Other popular topics Top

DevotionGeo
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
PragmaticBookshelf
Design and develop sophisticated 2D games that are as much fun to make as they are to play. From particle effects and pathfinding to soci...
New
AstonJ
I have seen the keycaps I want - they are due for a group-buy this week but won’t be delivered until October next year!!! :rofl: The Ser...
New
AstonJ
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
DevotionGeo
The V Programming Language Simple language for building maintainable programs V is already mentioned couple of times in the forum, but I...
New
AstonJ
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
rustkas
Intensively researching Erlang books and additional resources on it, I have found that the topic of using Regular Expressions is either c...
New
AstonJ
Saw this on TikTok of all places! :lol: Anyone heard of them before? Lite:
New
PragmaticBookshelf
Build efficient applications that exploit the unique benefits of a pure functional language, learning from an engineer who uses Haskell t...
New
New

Sub Categories: