.. _pygadkerasga-module: ``pygad.kerasga`` Module ======================== This section of the PyGAD's library documentation discusses the `pygad.kerasga `__ module. The ``pygad.kerarsga`` module has helper a class and 2 functions to train Keras models using the genetic algorithm (PyGAD). The Keras model can be built either using the `Sequential Model `__ or the `Functional API `__. The contents of this module are: 1. ``KerasGA``: A class for creating an initial population of all parameters in the Keras model. 2. ``model_weights_as_vector()``: A function to reshape the Keras model weights to a single vector. 3. ``model_weights_as_matrix()``: A function to restore the Keras model weights from a vector. 4. ``predict()``: A function to make predictions based on the Keras model and a solution. More details are given in the next sections. Steps Summary ============= The summary of the steps used to train a Keras model using PyGAD is as follows: 1. Create a Keras model. 2. Create an instance of the ``pygad.kerasga.KerasGA`` class. 3. Prepare the training data. 4. Build the fitness function. 5. Create an instance of the ``pygad.GA`` class. 6. Run the genetic algorithm. Create Keras Model ================== Before discussing training a Keras model using PyGAD, the first thing to do is to create the Keras model. According to the `Keras library documentation `__, there are 3 ways to build a Keras model: 1. `Sequential Model `__ 2. `Functional API `__ 3. `Model Subclassing `__ PyGAD supports training the models created either using the Sequential Model or the Functional API. Here is an example of a model created using the Sequential Model. .. code:: python import tensorflow.keras input_layer = tensorflow.keras.layers.Input(3) dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu") output_layer = tensorflow.keras.layers.Dense(1, activation="linear") model = tensorflow.keras.Sequential() model.add(input_layer) model.add(dense_layer1) model.add(output_layer) This is the same model created using the Functional API. .. code:: python input_layer = tensorflow.keras.layers.Input(3) dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) Feel free to add the layers of your choice. .. _pygadkerasgakerasga-class: ``pygad.kerasga.KerasGA`` Class =============================== The ``pygad.kerasga`` module has a class named ``KerasGA`` for creating 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. .. _init: ``__init__()`` -------------- The ``pygad.kerasga.KerasGA`` class constructor accepts the following parameters: - ``model``: An instance of the Keras model. - ``num_solutions``: Number of solutions in the population. Each solution has different parameters of the model. Instance Attributes ------------------- All parameters in the ``pygad.kerasga.KerasGA`` class constructor are used as instance attributes in addition to adding a new attribute called ``population_weights``. Here is a list of all instance attributes: - ``model`` - ``num_solutions`` - ``population_weights``: A nested list holding the weights of all solutions in the population. Methods in the ``KerasGA`` Class -------------------------------- This section discusses the methods available for instances of the ``pygad.kerasga.KerasGA`` class. .. _createpopulation: ``create_population()`` ~~~~~~~~~~~~~~~~~~~~~~~ The ``create_population()`` method creates the initial population of the 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. .. _functions-in-the-pygadkerasga-module: Functions in the ``pygad.kerasga`` Module ========================================= This section discusses the functions in the ``pygad.kerasga`` module. .. _pygadkerasgamodelweightsasvector: ``pygad.kerasga.model_weights_as_vector()`` -------------------------------------------- The ``model_weights_as_vector()`` function accepts a single parameter named ``model`` representing the Keras model. It returns a vector 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. .. _pygadkerasgamodelweightsasmatrix: ``pygad.kerasga.model_weights_as_matrix()`` ------------------------------------------- The ``model_weights_as_matrix()`` function accepts the following parameters: 1. ``model``: The Keras model. 2. ``weights_vector``: The model parameters as a vector. It returns the restored model weights after reshaping the vector. .. _pygadkerasgapredict: ``pygad.kerasga.predict()`` --------------------------- The ``predict()`` function makes a prediction based on a solution. It accepts the following parameters: 1. ``model``: The Keras model. 2. ``solution``: The solution evolved. 3. ``data``: The test data inputs. 4. ``batch_size=None``: The batch size (i.e. number of samples per step or batch). 5. ``verbose=None``: Verbosity mode. 6. ``steps=None``: The total number of steps (batches of samples). Check documentation of the `Keras Model.predict() `__ method for more information about the ``batch_size``, ``verbose``, and ``steps`` parameters. It returns the predictions of the data samples. Examples ======== This section gives the complete code of some examples that build and train a Keras model using PyGAD. Each subsection builds a different network. Example 1: Regression Example ----------------------------- The next code builds a simple Keras model for regression. The next subsections discuss each part in the code. .. code:: python import tensorflow.keras import pygad.kerasga import numpy import pygad def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, keras_ga, model predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) mae = tensorflow.keras.losses.MeanAbsoluteError() abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 solution_fitness = 1.0/abs_error return solution_fitness def on_generation(ga_instance): print(f"Generation = {ga_instance.generations_completed}") print(f"Fitness = {ga_instance.best_solution()[1]}") input_layer = tensorflow.keras.layers.Input(3) dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) # Data inputs data_inputs = numpy.array([[0.02, 0.1, 0.15], [0.7, 0.6, 0.8], [1.5, 1.2, 1.7], [3.2, 2.9, 3.1]]) # Data outputs data_outputs = numpy.array([[0.1], [0.6], [1.3], [2.5]]) # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, on_generation=on_generation) ga_instance.run() # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) # Returning the details of the best solution. solution, solution_fitness, solution_idx = ga_instance.best_solution() print(f"Fitness value of the best solution = {solution_fitness}") print(f"Index of the best solution : {solution_idx}") # Make prediction based on the best solution. predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) print(f"Predictions : \n{predictions}") mae = tensorflow.keras.losses.MeanAbsoluteError() abs_error = mae(data_outputs, predictions).numpy() print(f"Absolute Error : {abs_error}") Create a Keras Model ~~~~~~~~~~~~~~~~~~~~ According to the steps mentioned previously, the first step is to create a Keras model. Here is the code that builds the model using the Functional API. .. code:: python import tensorflow.keras input_layer = tensorflow.keras.layers.Input(3) dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu")(input_layer) output_layer = tensorflow.keras.layers.Dense(1, activation="linear")(dense_layer1) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) The model can also be build using the Keras Sequential Model API. .. code:: python input_layer = tensorflow.keras.layers.Input(3) dense_layer1 = tensorflow.keras.layers.Dense(5, activation="relu") output_layer = tensorflow.keras.layers.Dense(1, activation="linear") model = tensorflow.keras.Sequential() model.add(input_layer) model.add(dense_layer1) model.add(output_layer) .. _create-an-instance-of-the-pygadkerasgakerasga-class: Create an Instance of the ``pygad.kerasga.KerasGA`` Class ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The second step is to create an instance of the ``pygad.kerasga.KerasGA`` class. There are 10 solutions per population. Change this number according to your needs. .. code:: python import pygad.kerasga keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) Prepare the Training Data ~~~~~~~~~~~~~~~~~~~~~~~~~ The third step is to prepare the training data inputs and outputs. Here is an example where there are 4 samples. Each sample has 3 inputs and 1 output. .. code:: python import numpy # Data inputs data_inputs = numpy.array([[0.02, 0.1, 0.15], [0.7, 0.6, 0.8], [1.5, 1.2, 1.7], [3.2, 2.9, 3.1]]) # Data outputs data_outputs = numpy.array([[0.1], [0.6], [1.3], [2.5]]) Build the Fitness Function ~~~~~~~~~~~~~~~~~~~~~~~~~~ The fourth step is to build the fitness function. This function must accept 2 parameters representing the solution and its index within the population. The next fitness function returns the model predictions based on the current solution using the ``predict()`` function. Then, it calculates the mean absolute error (MAE) of the Keras model based on the parameters in the solution. The reciprocal of the MAE is used as the fitness value. Feel free to use any other loss function to calculate the fitness value. .. code:: python def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, keras_ga, model predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) mae = tensorflow.keras.losses.MeanAbsoluteError() abs_error = mae(data_outputs, predictions).numpy() + 0.00000001 solution_fitness = 1.0/abs_error return solution_fitness .. _create-an-instance-of-the-pygadga-class: Create an Instance of the ``pygad.GA`` Class ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The fifth step is to instantiate the ``pygad.GA`` class. Note how the ``initial_population`` parameter is assigned to the initial weights of the Keras models. For more information, please check the `parameters this class accepts `__. .. code:: python # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, on_generation=on_generation) Run the Genetic Algorithm ~~~~~~~~~~~~~~~~~~~~~~~~~ The sixth and last step is to run the genetic algorithm by calling the ``run()`` method. .. code:: python ga_instance.run() After the PyGAD completes its execution, then there is a figure that shows how the fitness value changes by generation. Call the ``plot_fitness()`` method to show the figure. .. code:: python ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) Here is the figure. .. image:: https://user-images.githubusercontent.com/16560492/93722638-ac261880-fb98-11ea-95d3-e773deb034f4.png :alt: To get information about the best solution found by PyGAD, use the ``best_solution()`` method. .. code:: python # Returning the details of the best solution. solution, solution_fitness, solution_idx = ga_instance.best_solution() print(f"Fitness value of the best solution = {solution_fitness}") print(f"Index of the best solution : {solution_idx}") .. code:: python Fitness value of the best solution = 72.77768757825352 Index of the best solution : 0 The next code makes prediction using the ``predict()`` function to return the model predictions based on the best solution. .. code:: python # Fetch the parameters of the best solution. predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) print(f"Predictions : \n{predictions}") .. code:: python Predictions : [[0.09935353] [0.63082725] [1.2765523 ] [2.4999595 ]] The next code measures the trained model error. .. code:: python mae = tensorflow.keras.losses.MeanAbsoluteError() abs_error = mae(data_outputs, predictions).numpy() print(f"Absolute Error : {abs_error}") .. code:: Absolute Error : 0.013740465 Example 2: XOR Binary Classification ------------------------------------ The next code creates a Keras model to build the XOR binary classification problem. Let's highlight the changes compared to the previous example. .. code:: python import tensorflow.keras import pygad.kerasga import numpy import pygad def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, keras_ga, model predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) bce = tensorflow.keras.losses.BinaryCrossentropy() solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) return solution_fitness def on_generation(ga_instance): print(f"Generation = {ga_instance.generations_completed}") print(f"Fitness = {ga_instance.best_solution()[1]}") # Build the keras model using the functional API. input_layer = tensorflow.keras.layers.Input(2) dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) # XOR problem inputs data_inputs = numpy.array([[0, 0], [0, 1], [1, 0], [1, 1]]) # XOR problem outputs data_outputs = numpy.array([[1, 0], [0, 1], [0, 1], [1, 0]]) # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 250 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights. # Create an instance of the pygad.GA class ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) # Returning the details of the best solution. solution, solution_fitness, solution_idx = ga_instance.best_solution() print(f"Fitness value of the best solution = {solution_fitness}") print(f"Index of the best solution : {solution_idx}") # Make predictions based on the best solution. predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) print(f"Predictions : \n{predictions}") # Calculate the binary crossentropy for the trained model. bce = tensorflow.keras.losses.BinaryCrossentropy() print("Binary Crossentropy : ", bce(data_outputs, predictions).numpy()) # Calculate the classification accuracy for the trained model. ba = tensorflow.keras.metrics.BinaryAccuracy() ba.update_state(data_outputs, predictions) accuracy = ba.result().numpy() print(f"Accuracy : {accuracy}") Compared to the previous regression example, here are the changes: - The Keras model is changed according to the nature of the problem. Now, it has 2 inputs and 2 outputs with an in-between hidden layer of 4 neurons. .. code:: python # Build the keras model using the functional API. input_layer = tensorflow.keras.layers.Input(2) dense_layer = tensorflow.keras.layers.Dense(4, activation="relu")(input_layer) output_layer = tensorflow.keras.layers.Dense(2, activation="softmax")(dense_layer) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) - The train data is changed. Note that the output of each sample is a 1D vector of 2 values, 1 for each class. .. code:: python # XOR problem inputs data_inputs = numpy.array([[0, 0], [0, 1], [1, 0], [1, 1]]) # XOR problem outputs data_outputs = numpy.array([[1, 0], [0, 1], [0, 1], [1, 0]]) - The fitness value is calculated based on the binary cross entropy. .. code:: python bce = tensorflow.keras.losses.BinaryCrossentropy() solution_fitness = 1.0 / (bce(data_outputs, predictions).numpy() + 0.00000001) After the previous code completes, the next figure shows how the fitness value change by generation. .. image:: https://user-images.githubusercontent.com/16560492/93722639-b811da80-fb98-11ea-8951-f13a7a266c04.png :alt: Here is some information about the trained model. Its fitness value is ``739.24``, loss is ``0.0013527311`` and accuracy is 100%. .. code:: python Fitness value of the best solution = 739.2397344644013 Index of the best solution : 7 Predictions : [[9.9694413e-01 3.0558957e-03] [5.0176249e-04 9.9949825e-01] [1.8470541e-03 9.9815291e-01] [9.9999976e-01 2.0538971e-07]] Binary Crossentropy : 0.0013527311 Accuracy : 1.0 Example 3: Image Multi-Class Classification (Dense Layers) ---------------------------------------------------------- Here is the code. .. code:: python import tensorflow.keras import pygad.kerasga import numpy import pygad def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, keras_ga, model predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) cce = tensorflow.keras.losses.CategoricalCrossentropy() solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) return solution_fitness def on_generation(ga_instance): print(f"Generation = {ga_instance.generations_completed}") print(f"Fitness = {ga_instance.best_solution()[1]}") # Build the keras model using the functional API. input_layer = tensorflow.keras.layers.Input(360) dense_layer = tensorflow.keras.layers.Dense(50, activation="relu")(input_layer) output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) # Data inputs data_inputs = numpy.load("../data/dataset_features.npy") # Data outputs data_outputs = numpy.load("../data/outputs.npy") data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 100 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights. # Create an instance of the pygad.GA class ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) # Returning the details of the best solution. solution, solution_fitness, solution_idx = ga_instance.best_solution() print(f"Fitness value of the best solution = {solution_fitness}") print(f"Index of the best solution : {solution_idx}") # Make predictions based on the best solution. predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) # print(f"Predictions : \n{predictions}") # Calculate the categorical crossentropy for the trained model. cce = tensorflow.keras.losses.CategoricalCrossentropy() print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}") # Calculate the classification accuracy for the trained model. ca = tensorflow.keras.metrics.CategoricalAccuracy() ca.update_state(data_outputs, predictions) accuracy = ca.result().numpy() print(f"Accuracy : {accuracy}") Compared to the previous binary classification example, this example has multiple classes (4) and thus the loss is measured using categorical cross entropy. .. code:: python cce = tensorflow.keras.losses.CategoricalCrossentropy() solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) .. _prepare-the-training-data-2: Prepare the Training Data ~~~~~~~~~~~~~~~~~~~~~~~~~ Before building and training neural networks, the training data (input and output) needs to be prepared. The inputs and the outputs of the training data are NumPy arrays. The data used in this example is available as 2 files: 1. `dataset_features.npy `__: Data inputs. https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy 2. `outputs.npy `__: Class labels. https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy The data consists of 4 classes of images. The image shape is ``(100, 100, 3)``. The number of training samples is 1962. The feature vector extracted from each image has a length 360. Simply download these 2 files and read them according to the next code. Note that the class labels are one-hot encoded using the ``tensorflow.keras.utils.to_categorical()`` function. .. code:: python import numpy data_inputs = numpy.load("../data/dataset_features.npy") data_outputs = numpy.load("../data/outputs.npy") data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) The next figure shows how the fitness value changes. .. image:: https://user-images.githubusercontent.com/16560492/93722649-c2cc6f80-fb98-11ea-96e7-3f6ce3cfe1cf.png :alt: Here are some statistics about the trained model. .. code:: Fitness value of the best solution = 4.197464252185969 Index of the best solution : 0 Categorical Crossentropy : 0.23823906 Accuracy : 0.9852192 Example 4: Image Multi-Class Classification (Conv Layers) --------------------------------------------------------- Compared to the previous example that uses only dense layers, this example uses convolutional layers to classify the same dataset. Here is the complete code. .. code:: python import tensorflow.keras import pygad.kerasga import numpy import pygad def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, keras_ga, model predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) cce = tensorflow.keras.losses.CategoricalCrossentropy() solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) return solution_fitness def on_generation(ga_instance): print(f"Generation = {ga_instance.generations_completed}") print(f"Fitness = {ga_instance.best_solution()[1]}") # Build the keras model using the functional API. input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, kernel_size=7, activation="relu")(input_layer) max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), strides=5)(conv_layer1) conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, kernel_size=3, activation="relu")(max_pool1) flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) # Data inputs data_inputs = numpy.load("../data/dataset_inputs.npy") # Data outputs data_outputs = numpy.load("../data/dataset_outputs.npy") data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) # Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class num_generations = 200 # Number of generations. num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool. initial_population = keras_ga.population_weights # Initial population of network weights. # Create an instance of the pygad.GA class ga_instance = pygad.GA(num_generations=num_generations, num_parents_mating=num_parents_mating, initial_population=initial_population, fitness_func=fitness_func, on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) # Returning the details of the best solution. solution, solution_fitness, solution_idx = ga_instance.best_solution() print(f"Fitness value of the best solution = {solution_fitness}") print(f"Index of the best solution : {solution_idx}") # Make predictions based on the best solution. predictions = pygad.kerasga.predict(model=model, solution=solution, data=data_inputs) # print(f"Predictions : \n{predictions}") # Calculate the categorical crossentropy for the trained model. cce = tensorflow.keras.losses.CategoricalCrossentropy() print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}") # Calculate the classification accuracy for the trained model. ca = tensorflow.keras.metrics.CategoricalAccuracy() ca.update_state(data_outputs, predictions) accuracy = ca.result().numpy() print(f"Accuracy : {accuracy}") Compared to the previous example, the only change is that the architecture uses convolutional and max-pooling layers. The shape of each input sample is 100x100x3. .. code:: python # Build the keras model using the functional API. input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3)) conv_layer1 = tensorflow.keras.layers.Conv2D(filters=5, kernel_size=7, activation="relu")(input_layer) max_pool1 = tensorflow.keras.layers.MaxPooling2D(pool_size=(5,5), strides=5)(conv_layer1) conv_layer2 = tensorflow.keras.layers.Conv2D(filters=3, kernel_size=3, activation="relu")(max_pool1) flatten_layer = tensorflow.keras.layers.Flatten()(conv_layer2) dense_layer = tensorflow.keras.layers.Dense(15, activation="relu")(flatten_layer) output_layer = tensorflow.keras.layers.Dense(4, activation="softmax")(dense_layer) model = tensorflow.keras.Model(inputs=input_layer, outputs=output_layer) .. _prepare-the-training-data-3: Prepare the Training Data ~~~~~~~~~~~~~~~~~~~~~~~~~ The data used in this example is available as 2 files: 1. `dataset_inputs.npy `__: Data inputs. https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_inputs.npy 2. `dataset_outputs.npy `__: Class labels. https://github.com/ahmedfgad/NumPyCNN/blob/master/dataset_outputs.npy The data consists of 4 classes of images. The image shape is ``(100, 100, 3)`` and there are 20 images per class for a total of 80 training samples. For more information about the dataset, check the `Reading the Data `__ section of the ``pygad.cnn`` module. Simply download these 2 files and read them according to the next code. Note that the class labels are one-hot encoded using the ``tensorflow.keras.utils.to_categorical()`` function. .. code:: python import numpy data_inputs = numpy.load("../data/dataset_inputs.npy") data_outputs = numpy.load("../data/dataset_outputs.npy") data_outputs = tensorflow.keras.utils.to_categorical(data_outputs) The next figure shows how the fitness value changes. .. image:: https://user-images.githubusercontent.com/16560492/93722654-cc55d780-fb98-11ea-8f95-7b65dc67f5c8.png :alt: Here are some statistics about the trained model. The model accuracy is 75% after the 200 generations. Note that just running the code again may give different results. .. code:: Fitness value of the best solution = 2.7462310258668805 Index of the best solution : 0 Categorical Crossentropy : 0.3641354 Accuracy : 0.75 To improve the model performance, you can do the following: - Add more layers - Modify the existing layers. - Use different parameters for the layers. - Use different parameters for the genetic algorithm (e.g. number of solution, number of generations, etc) Example 5: Image Classification using Data Generator ---------------------------------------------------- This example uses the image data generator ``tensorflow.keras.preprocessing.image.ImageDataGenerator`` to feed data to the model. Instead of reading all the data in the memory, the data generator generates the data needed by the model and only save it in the memory instead of saving all the data. This frees the memory but adds more computational time. .. code:: python import tensorflow as tf import tensorflow.keras import pygad.kerasga import pygad def fitness_func(ga_instanse, solution, sol_idx): global train_generator, data_outputs, keras_ga, model predictions = pygad.kerasga.predict(model=model, solution=solution, data=train_generator) cce = tensorflow.keras.losses.CategoricalCrossentropy() solution_fitness = 1.0 / (cce(data_outputs, predictions).numpy() + 0.00000001) return solution_fitness def on_generation(ga_instance): print("Generation = {ga_instance.generations_completed}") print("Fitness = {ga_instance.best_solution(ga_instance.last_generation_fitness)[1]}") # The dataset path. dataset_path = r'../data/Skin_Cancer_Dataset' num_classes = 2 img_size = 224 # Create a simple CNN. This does not gurantee high classification accuracy. model = tf.keras.models.Sequential() model.add(tf.keras.layers.Input(shape=(img_size, img_size, 3))) model.add(tf.keras.layers.Conv2D(32, (3,3), activation="relu", padding="same")) model.add(tf.keras.layers.MaxPooling2D((2, 2))) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dropout(rate=0.2)) model.add(tf.keras.layers.Dense(num_classes, activation="softmax")) # Create an instance of the pygad.kerasga.KerasGA class to build the initial population. keras_ga = pygad.kerasga.KerasGA(model=model, num_solutions=10) data_generator = tf.keras.preprocessing.image.ImageDataGenerator() train_generator = data_generator.flow_from_directory(dataset_path, class_mode='categorical', target_size=(224, 224), batch_size=32, shuffle=False) # train_generator.class_indices data_outputs = tf.keras.utils.to_categorical(train_generator.labels) # Check the documentation for more information about the parameters: https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class initial_population = keras_ga.population_weights # Initial population of network weights. # Create an instance of the pygad.GA class ga_instance = pygad.GA(num_generations=10, num_parents_mating=5, initial_population=initial_population, fitness_func=fitness_func, on_generation=on_generation) # Start the genetic algorithm evolution. ga_instance.run() # After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations. ga_instance.plot_fitness(title="PyGAD & Keras - Iteration vs. Fitness", linewidth=4) # Returning the details of the best solution. solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness) print(f"Fitness value of the best solution = {solution_fitness}") print(f"Index of the best solution : {solution_idx}") predictions = pygad.kerasga.predict(model=model, solution=solution, data=train_generator) # print(f"Predictions : \n{predictions}") # Calculate the categorical crossentropy for the trained model. cce = tensorflow.keras.losses.CategoricalCrossentropy() print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}") # Calculate the classification accuracy for the trained model. ca = tensorflow.keras.metrics.CategoricalAccuracy() ca.update_state(data_outputs, predictions) accuracy = ca.result().numpy() print(f"Accuracy : {accuracy}")