PyGAD 2.10.1 Documentation

1. In the `gene_space` parameter, any `None` value (regardless of its index or axis), is replaced by a randomly generated number based on the 3 parameters `init_range_low`, `init_range_high`, and `gene_type`. So, the `None` value in `[..., None, ...]` or `[..., [..., None, ...], ...]` are replaced with random values. This gives more freedom in building the space of values for the genes.
2. All the numbers passed to the `gene_space` parameter are casted to the type specified in the `gene_type` parameter.
3. The `numpy.uint` data type is supported for the parameters that accept integer values.
4. In the `pygad.kerasga` module, the `model_weights_as_vector()` function uses the `trainable` attribute of the model's layers to only return the trainable weights in the network. So, only the trainable layers with their `trainable` attribute set to `True` (`trainable=True`), which is the default value, have their weights evolved. All non-trainable layers with the `trainable` attribute set to `False` (`trainable=False`) will not be evolved. Thanks to [Prof. Tamer A. Farrag](https://github.com/tfarrag2000) for pointing about that at [GitHub](ahmedfgad/KerasGA#1).

Author: ahmedfgad

docs/source/Footer.rst

@@ -461,7 +461,7 @@ Release Date: 03 January 2021
 
 2.  Support of adaptive mutation where the mutation rate is determined
     by the fitness value of each solution. Read the `Adaptive
-    Mutation <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#adaptive-mutation>`__
+    Mutation <https://pygad.readthedocs.io/en/latest/README_pygad_torchga_ReadTheDocs.html#adaptive-mutation>`__
     section for more details. Also, read this paper: `Libelli, S.
     Marsili, and P. Alba. "Adaptive mutation in genetic algorithms."
     Soft computing 4.2 (2000):
@@ -509,6 +509,38 @@ Release Date: 03 January 2021
     ``cal_pop_fitness()`` method is called to calculate the fitness
     values of the population.
 
+.. _header-n698:
+
+PyGAD 2.10.1
+------------
+
+Release Date: 10 January 2021
+
+1. In the ``gene_space`` parameter, any ``None`` value (regardless of
+   its index or axis), is replaced by a randomly generated number based
+   on the 3 parameters ``init_range_low``, ``init_range_high``, and
+   ``gene_type``. So, the ``None`` value in ``[..., None, ...]`` or
+   ``[..., [..., None, ...], ...]`` are replaced with random values.
+   This gives more freedom in building the space of values for the
+   genes.
+
+2. All the numbers passed to the ``gene_space`` parameter are casted to
+   the type specified in the ``gene_type`` parameter.
+
+3. The ``numpy.uint`` data type is supported for the parameters that
+   accept integer values.
+
+4. In the ``pygad.kerasga`` module, the ``model_weights_as_vector()``
+   function uses the ``trainable`` attribute of the model's layers to
+   only return the trainable weights in the network. So, only the
+   trainable layers with their ``trainable`` attribute set to ``True``
+   (``trainable=True``), which is the default value, have their weights
+   evolved. All non-trainable layers with the ``trainable`` attribute
+   set to ``False`` (``trainable=False``) will not be evolved. Thanks to
+   `Prof. Tamer A. Farrag <https://github.com/tfarrag2000>`__ for
+   pointing about that at
+   `GitHub <https://github.com/ahmedfgad/KerasGA/issues/1>`__.
+
 .. _header-n554:
 
 PyGAD Projects at GitHub

docs/source/README_pygad_ReadTheDocs.rst

@@ -132,10 +132,10 @@ The ``pygad.GA`` class constructor supports the following parameters:
    be used unchanged in the next generation. ``Adaptive`` mutation is
    supported starting from PyGAD 2.10.0. For more information about
    adaptive mutation, go the the `Adaptive
-   Mutation <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#adaptive-mutation>`__
+   Mutation <https://pygad.readthedocs.io/en/latest/README_pygad_torchga_ReadTheDocs.html#adaptive-mutation>`__
    section. For example about using adaptive mutation, check the `Use
    Adaptive Mutation in
-   PyGAD <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-adaptive-mutation-in-pygad>`__
+   PyGAD <https://pygad.readthedocs.io/en/latest/README_pygad_torchga_ReadTheDocs.html#use-adaptive-mutation-in-pygad>`__
    section.
 
 -  ``mutation_probability=None``: The probability of selecting a gene

docs/source/README_pygad_kerasga_ReadTheDocs.rst

@@ -1,4 +1,4 @@
-.. _header-n0:
+.. _header-n194:
 
 ``pygad.kerasga`` Module
 ========================
@@ -25,7 +25,7 @@ The contents of this module are:
 
 More details are given in the next sections.
 
-.. _header-n13:
+.. _header-n207:
 
 Steps Summary
 =============
@@ -45,7 +45,7 @@ follows:
 
 6. Run the genetic algorithm.
 
-.. _header-n28:
+.. _header-n222:
 
 Create Keras Model
 ==================
@@ -93,7 +93,7 @@ This is the same model created using the Functional API.
 
 Feel free to add the layers of your choice.
 
-.. _header-n44:
+.. _header-n238:
 
 ``pygad.kerasga.KerasGA`` Class
 ===============================
@@ -103,7 +103,7 @@ an initial population for the genetic algorithm based on a Keras model.
 The constructor, methods, and attributes within the class are discussed
 in this section.
 
-.. _header-n46:
+.. _header-n240:
 
 ``__init__()``
 --------------
@@ -116,7 +116,7 @@ parameters:
 -  ``num_solutions``: Number of solutions in the population. Each
    solution has different parameters of the model.
 
-.. _header-n53:
+.. _header-n247:
 
 Instance Attributes
 -------------------
@@ -134,15 +134,15 @@ Here is a list of all instance attributes:
 -  ``population_weights``: A nested list holding the weights of all
    solutions in the population.
 
-.. _header-n63:
+.. _header-n257:
 
 Methods in the ``KerasGA`` Class
 --------------------------------
 
 This section discusses the methods available for instances of the
 ``pygad.kerasga.KerasGA`` class.
 
-.. _header-n65:
+.. _header-n259:
 
 ``create_population()``
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -152,14 +152,14 @@ genetic algorithm as a list of solutions where each solution represents
 different model parameters. The list of networks is assigned to the
 ``population_weights`` attribute of the instance.
 
-.. _header-n67:
+.. _header-n261:
 
 Functions in the ``pygad.kerasga`` Module
 =========================================
 
 This section discusses the functions in the ``pygad.kerasga`` module.
 
-.. _header-n69:
+.. _header-n263:
 
 ``pygad.kerasga.model_weights_as_vector()`` 
 --------------------------------------------
@@ -170,13 +170,19 @@ holding all model weights. The reason for representing the model weights
 as a vector is that the genetic algorithm expects all parameters of any
 solution to be in a 1D vector form.
 
+This function filters the layers based on the ``trainable`` attribute to
+see whether the layer weights are trained or not. For each layer, if its
+``trainable=False``, then its weights will not be evolved using the
+genetic algorithm. Otherwise, it will be represented in the chromosome
+and evolved.
+
 The function accepts the following parameters:
 
 -  ``model``: The Keras model.
 
 It returns a 1D vector holding the model weights.
 
-.. _header-n76:
+.. _header-n270:
 
 ``pygad.kerasga.model_weights_as_matrix()``
 -------------------------------------------
@@ -190,7 +196,7 @@ parameters:
 
 It returns the restored model weights after reshaping the vector.
 
-.. _header-n84:
+.. _header-n278:
 
 Examples
 ========
@@ -199,7 +205,7 @@ This section gives the complete code of some examples that build and
 train a Keras model using PyGAD. Each subsection builds a different
 network.
 
-.. _header-n86:
+.. _header-n280:
 
 Example 1: Regression Example
 -----------------------------
@@ -296,7 +302,7 @@ subsections discuss each part in the code.
    abs_error = mae(data_outputs, predictions).numpy()
    print("Absolute Error : ", abs_error)
 
-.. _header-n89:
+.. _header-n283:
 
 Create a Keras Model
 ~~~~~~~~~~~~~~~~~~~~
@@ -328,7 +334,7 @@ The model can also be build using the Keras Sequential Model API.
    model.add(dense_layer1)
    model.add(output_layer)
 
-.. _header-n94:
+.. _header-n288:
 
 Create an Instance of the ``pygad.kerasga.KerasGA`` Class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -344,7 +350,7 @@ Change this number according to your needs.
    keras_ga = pygad.kerasga.KerasGA(model=model,
                                     num_solutions=10)
 
-.. _header-n97:
+.. _header-n291:
 
 Prepare the Training Data
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -369,7 +375,7 @@ output.
                                [1.3],
                                [2.5]])
 
-.. _header-n100:
+.. _header-n294:
 
 Build the Fitness Function
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -400,7 +406,7 @@ function to calculate the fitness value.
 
        return solution_fitness
 
-.. _header-n104:
+.. _header-n298:
 
 Create an Instance of the ``pygad.GA`` Class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -435,7 +441,7 @@ accepts <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#in
                           keep_parents=keep_parents,
                           on_generation=callback_generation)
 
-.. _header-n108:
+.. _header-n302:
 
 Run the Genetic Algorithm
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -508,7 +514,7 @@ The next code measures the trained model error.
 
    Absolute Error :  0.013740465
 
-.. _header-n124:
+.. _header-n318:
 
 Example 2: XOR Binary Classification
 ------------------------------------
@@ -679,7 +685,7 @@ Here is some information about the trained model. Its fitness value is
 
    Accuracy :  1.0
 
-.. _header-n144:
+.. _header-n338:
 
 Example 3: Image Multi-Class Classification (Dense Layers)
 ----------------------------------------------------------
@@ -789,7 +795,7 @@ cross entropy.
    cce = tensorflow.keras.losses.CategoricalCrossentropy()
    solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001)
 
-.. _header-n149:
+.. _header-n343:
 
 Prepare the Training Data
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -839,7 +845,7 @@ Here are some statistics about the trained model.
    Categorical Crossentropy :  0.23823906
    Accuracy :  0.9852192
 
-.. _header-n164:
+.. _header-n358:
 
 Example 4: Image Multi-Class Classification (Conv Layers)
 ---------------------------------------------------------
@@ -974,7 +980,7 @@ each input sample is 100x100x3.
 
    model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer)
 
-.. _header-n170:
+.. _header-n364:
 
 Prepare the Training Data
 ~~~~~~~~~~~~~~~~~~~~~~~~~

docs/source/conf.py

@@ -22,7 +22,7 @@
 author = 'Ahmed Fawzy Gad'
 
 # The full version, including alpha/beta/rc tags
-release = '2.10.0'
+release = '2.10.1'
 
 master_doc = 'index'