Fix hpo keras

.readthedocs.yaml

@@ -20,4 +20,3 @@ sphinx:
 # python:
 #    install:
 #    - requirements: docs/requirements.txt
-

README.md

@@ -3,14 +3,14 @@
 ## Prune and Quantize ML models
 PQuant is a library for training compressed machine learning models, developed at CERN as part of the [Next Generation Triggers](https://nextgentriggers.web.cern.ch/t13/) project.
 
-Installation via pip: ```pip install pquant-ml```. 
+Installation via pip: ```pip install pquant-ml```.
 
-With TensorFlow ```pip install pquant-ml[tensorflow]```. 
+With TensorFlow ```pip install pquant-ml[tensorflow]```.
 
 With PyTorch ```pip install pquant-ml[torch]```.
 
-PQuant replaces the layers and activations it finds with a Compressed (in the case of layers) or Quantized (in the case of activations) variant. These automatically handle the quantization of the weights, biases and activations, and the pruning of the weights. 
-Both PyTorch and TensorFlow models are supported. 
+PQuant replaces the layers and activations it finds with a Compressed (in the case of layers) or Quantized (in the case of activations) variant. These automatically handle the quantization of the weights, biases and activations, and the pruning of the weights.
+Both PyTorch and TensorFlow models are supported.
 
 ### Layers that can be compressed
 

docs/Makefile

@@ -18,4 +18,4 @@ help:
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@sphinx-apidoc -f -o autodoc/ ../src/HGQ
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/requirements.txt

@@ -1,5 +1,5 @@
-sphinx
 furo
 myst-parser
-sphinx_rtd_theme
+sphinx
 sphinx-autodoc-typehints
+sphinx_rtd_theme

docs/source/_static/custom.css

@@ -51,12 +51,12 @@ html body nav.wy-nav-top,
 }
 
 .wy-body-for-nav {
-    background-color: #ffffff !important; 
+    background-color: #ffffff !important;
 }
 
 .wy-nav-content {
     background-color: #ffffff !important;
-    max-width: 1200px !important;  
+    max-width: 1200px !important;
 }
 
 .wy-side-nav-search {
@@ -71,19 +71,19 @@ html body nav.wy-nav-top,
 }
 
 .wy-nav-side {
-    background-color: #b30000 !important; 
+    background-color: #b30000 !important;
 }
 .wy-menu-vertical a {
-    color: #ffffff !important; 
+    color: #ffffff !important;
 }
 
 .wy-menu-vertical a:hover {
-    background-color: #990000 !important; 
+    background-color: #990000 !important;
 }
 
 .wy-menu-vertical li.current > a,
 .wy-menu-vertical li.toctree-l1.current > a {
-    background-color: #660000 !important; 
+    background-color: #660000 !important;
     color: #ffffff !important;
 }
 
@@ -110,7 +110,7 @@ h1, h2, h3, h4, h5, h6 {
   background-color: #ffffff !important;
 }
 
-.rst-content > .document > .toctree-wrapper, 
+.rst-content > .document > .toctree-wrapper,
 .rst-content > .document > .section {
   background-color: #ffffff !important;
 }
@@ -121,7 +121,7 @@ h1, h2, h3, h4, h5, h6 {
 }
 
 .rst-content table th {
-    background-color: #ffe6e6 !important;  
+    background-color: #ffe6e6 !important;
     color: #b30000 !important;
 }
 
@@ -158,7 +158,7 @@ h1, h2, h3, h4, h5, h6 {
     color: #000000 !important;
     padding: 8px !important;
     margin: 12px !important;
-    width: calc(100% - 24px) !important; 
+    width: calc(100% - 24px) !important;
     box-sizing: border-box !important;
 }
 
@@ -170,7 +170,7 @@ h1, h2, h3, h4, h5, h6 {
 
 .wy-side-nav-search {
     background-color: #ffffff !important;
-    padding: 20px 15px !important; 
+    padding: 20px 15px !important;
     border-bottom: 2px solid #b30000 !important;
 }
 
@@ -184,19 +184,19 @@ h1, h2, h3, h4, h5, h6 {
 }
 
 .wy-side-nav-search .version-switch  {
-    color: #b30000 !important;  
+    color: #b30000 !important;
     font-weight: 600 !important;
 }
 
 .wy-side-nav-search .version-switch :hover {
-    color: #990000 !important;  
+    color: #990000 !important;
 }
 
 .wy-side-nav-search .fa-caret-down {
     color: #b30000 !important;
 }
 .wy-side-nav-search select.version-switch {
-    color: #b30000 !important;     
+    color: #b30000 !important;
     background-color: #ffffff !important;
     border: 2px solid #b30000 !important;
     font-weight: 600 !important;
@@ -205,8 +205,8 @@ h1, h2, h3, h4, h5, h6 {
 }
 
 .wy-side-nav-search select.rtd-version-select {
-    color: #b30000 !important;             
-    background-color: #ffffff !important; 
+    color: #b30000 !important;
+    background-color: #ffffff !important;
     border: 2px solid #b30000 !important;
     font-weight: 600 !important;
     padding: 6px !important;
@@ -223,16 +223,16 @@ h1, h2, h3, h4, h5, h6 {
 }
 
 .wy-side-nav-search input[type="search"] {
-    height: 100% !important;         
-    width: 100% !important;          
-    font-size: 18px !important;      
-    padding: 10px 14px !important;    
+    height: 100% !important;
+    width: 100% !important;
+    font-size: 18px !important;
+    padding: 10px 14px !important;
     margin: 0 !important;
-    border-radius: 6px !important;  
-    border: none !important;        
-    box-shadow: none !important;   
-    background: #ffffff !important;  
-    color: #000000 !important;   
+    border-radius: 6px !important;
+    border: none !important;
+    box-shadow: none !important;
+    background: #ffffff !important;
+    color: #000000 !important;
     box-sizing: border-box !important;
 }
 

docs/source/conf.py

@@ -67,4 +67,4 @@
 
 html_css_files = [
     'custom.css',
-]
+]

docs/source/faq.md

@@ -12,12 +12,12 @@ An example to install PyTorch with CUDA 13.0:
 pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu130
 ```
 ## Can I use MLflow locally?
-Yes. 
+Yes.
 
 PQuantML integrates with MLflow for experiment tracking and model logging and local usage is fully supported.
 
 
-### Start local MLFlow UI: 
+### Start local MLFlow UI:
 ```python
 mlflow ui --host 0.0.0.0 --port 5000
 ```

docs/source/getting_started.md

@@ -39,13 +39,13 @@ def build_model(config):
     class Model(torch.nn.Module):
         def __init__(self):
             super().__init__()
-            self.dense1 = PQDense(config, 16, 64, 
+            self.dense1 = PQDense(config, 16, 64,
                                   in_quant_bits = (1, 3, 3))
             self.relu = PQActivation(config, "relu")
             self.dense2 = PQDense(config, 64, 32)
             self.dense3 = PQDense(config, 32, 32)
-            self.dense4 = PQDense(config, 32, 5, 
-                                  quantize_output=True, 
+            self.dense4 = PQDense(config, 32, 5,
+                                  quantize_output=True,
                                   out_quant_bits=(1, 3, 3))
 
         def forward(self, x):
@@ -78,18 +78,18 @@ def build_model():
             x = self.relu(self.dense3(x))
             x = self.dense4(x)
             return x
-    
+
 
     return Model()
 
 # Convert to PQuantML-compressed model
 model = add_compression_layers(model, config)
 ```
 
-### Fine-Tuning with PQuantML 
+### Fine-Tuning with PQuantML
 PQuantML provides an automated fine-tuning and hyperparameter-optimization workflow through the `TuningTask API`. This allows you to search for optimal pruning, quantization, and training parameters using your own training, validation, and objective functions.
 
-```python 
+```python
 from pquant.core.finetuning import TuningTask, TuningConfig
 
 # Convert defined yaml file into the object
@@ -142,13 +142,13 @@ Training is handled through the `train_model(...)` wrapper:
 ```python
 from pquant import train_model
 
-trained_model = train_model(model = model, 
-                                config = config, 
-                                train_func = ..., 
-                                valid_func = ..., 
-                                trainloader = ..., 
+trained_model = train_model(model = model,
+                                config = config,
+                                train_func = ...,
+                                valid_func = ...,
+                                trainloader = ...,
                                 device="cuda",
-                                testloader = ..., 
+                                testloader = ...,
                                 loss_func = loss_func,
                                 optimizer = optimizer,
                                 scheduler=scheduler
@@ -164,15 +164,15 @@ def build_model(config):
     class Model(torch.nn.Module):
         def __init__(self):
             super().__init__()
-            self.dense1 = PQDense(config, 16, 64, 
+            self.dense1 = PQDense(config, 16, 64,
                                   in_quant_bits = (1, 3, 3))
             self.relu1 = PQActivation(config, "relu")
             self.relu2 = PQActivation(config, "relu")
             self.relu3 = PQActivation(config, "relu")
             self.dense2 = PQDense(config, 64, 32)
             self.dense3 = PQDense(config, 32, 32)
-            self.dense4 = PQDense(config, 32, 5, 
-                                  quantize_output=True, 
+            self.dense4 = PQDense(config, 32, 5,
+                                  quantize_output=True,
                                   out_quant_bits=(1, 3, 3))
 
         def forward(self, x):

docs/source/index.rst

@@ -16,10 +16,10 @@ PQuantML
 
 Welcome to the official documentation for **PQuantML**, a hardware-aware model compression framework supporting:
 
-- Joint pruning + quantization  
-- Layer-wise precision configuration  
-- Flexible training pipelines  
-- PyTorch and TensorFlow backends 
+- Joint pruning + quantization
+- Layer-wise precision configuration
+- Flexible training pipelines
+- PyTorch and TensorFlow backends
 - Integration with hardware-friendly toolchains (e.g., hls4ml)
 
 PQuantML enables efficient deployment of compact neural networks on resource-constrained hardware such as FPGAs and embedded accelerators.
@@ -49,7 +49,7 @@ Contents
 
 .. toctree::
    :maxdepth: 2
-   
+
    status
    install
    getting_started

docs/source/install.md

@@ -1,6 +1,6 @@
 # Installation
 
-Use `pip install pquant-ml` to install the latest version from PyPI. You will need an environment with `python>=3.10,<=3.12` installed. 
+Use `pip install pquant-ml` to install the latest version from PyPI. You will need an environment with `python>=3.10,<=3.12` installed.
 
 
 ```{warning}

docs/source/status.md

@@ -1,6 +1,6 @@
 # PQuantML Status
 
-This page tracks the development status of PQuantML features  
+This page tracks the development status of PQuantML features
 
 ## Release: v1.0.0
 
@@ -13,5 +13,3 @@ This page tracks the development status of PQuantML features
 | hls4ml integration              | ✅ Complete   | Works in v1.0.0 |
 | FITCompress                     | 🚧 Partially implemented | Works through PyTorch only |
 | Documentation                   | 🚧 Improving    | Expanded daily |
-
-