Main updates

.github/workflows/build_docs.yml

@@ -1,13 +1,16 @@
 name: Docs
 on: [push, pull_request, workflow_dispatch]
 permissions:
-    contents: write
+  contents: write
+
 jobs:
   docs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
+        with:
+          python-version: '3.8'  # Use Python 3.8
       - name: Install dependencies
         run: |
           pip install -r requirements.txt
@@ -20,4 +23,4 @@ jobs:
         uses: peaceiris/actions-gh-pages@v3
         with:
           personal_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: ./docs/_build/html
+          publish_dir: ./docs/_build/html

.github/workflows/run_tests.yml

@@ -10,24 +10,13 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+    - name: Set up Python 3.8  # Set Python version to 3.8 or 3.9
       uses: actions/setup-python@v3
       with:
-        python-version: '3.10'
-    - name: Add conda to system path
-      run: |
-        # $CONDA is an environment variable pointing to the root of the miniconda directory
-        echo $CONDA/bin >> $GITHUB_PATH
+        python-version: '3.8'  # Change to a version compatible with TensorFlow
     - name: Install dependencies
       run: |
         pip install -r requirements.txt
-    - name: Lint with flake8
-      run: |
-        conda install flake8
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
         python setup.py install

README.md

@@ -77,7 +77,7 @@ pip install -r requirements.txt
 To train a new **MNIST classifier** model from scratch, run the following command:
 
 ```bash
-python main.py --iterations 50 --particles 100 --save_dir "analysis_results"
+python taint_MNIST.py --iterations 50 --particles 100 --save_dir "analysis_results"
 ```
 
 This command will:
@@ -90,7 +90,7 @@ This command will:
 If you already have a pre-trained model, you can load it by providing the `--model_path` argument:
 
 ```bash
-python main.py --model_path "path_to_model/mnist_model.keras" --iterations 50 --particles 100 --save_dir "analysis_results"
+python taint_MNIST.py --model_path "path_to_model/mnist_model.keras" --iterations 50 --particles 100 --save_dir "analysis_results"
 ```
 
 This will load the provided pre-trained model, evaluate it on the test dataset, and then perform the adversarial attack.

docs/conf.py

@@ -13,12 +13,10 @@
 import os
 import sys
 sys.path.insert(0, os.path.abspath('../Adversarial_Observation'))
-sys.path.insert(1, os.path.abspath('../Swarm_Observer'))
 sys.path.insert(2, os.path.abspath('../'))
 from Adversarial_Observation.Attacks import *
 from Adversarial_Observation.utils import *
 from Adversarial_Observation.visualize import *
-import Swarm_Observer
 
 # -- Project information -----------------------------------------------------
 

docs/index.rst

@@ -11,7 +11,6 @@ Welcome to Adversarial_Observation's documentation!
    :caption: Contents:
 
    Adversarial_Observation
-   Swarm_Observer
    examples
 
 

docs/modules.rst

@@ -5,5 +5,4 @@ Adversarial_Observation
    :maxdepth: 4
 
    Adversarial_Observation
-   Swarm_Observer
    setup

requirements.txt

@@ -13,4 +13,5 @@ torch
 torchvision
 sphinx_rtd_theme
 tensorflow
-imageio[pyav]
+imageio[pyav]
+pytest