.. _pygadtorchga-module: ``pygad.torchga`` Module ======================== This section of the PyGAD's library documentation discusses the **pygad.torchga** module. The ``pygad.torchga`` module has a helper class and 2 functions to train PyTorch models using the genetic algorithm (PyGAD). The contents of this module are: 1. ``TorchGA``: A class for creating an initial population of all parameters in the PyTorch model. 2. ``model_weights_as_vector()``: A function to reshape the PyTorch model weights to a single vector. 3. ``model_weights_as_dict()``: A function to restore the PyTorch model weights from a vector. 4. ``predict()``: A function to make predictions based on the PyTorch model and a solution. More details are given in the next sections. Steps Summary ============= The summary of the steps used to train a PyTorch model using PyGAD is as follows: 1. Create a PyTorch model. 2. Create an instance of the ``pygad.torchga.TorchGA`` 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 PyTorch Model ==================== Before discussing training a PyTorch model using PyGAD, the first thing to do is to create the PyTorch model. To get started, please check the `PyTorch library documentation `__. Here is an example of a PyTorch model. .. code:: python import torch input_layer = torch.nn.Linear(3, 5) relu_layer = torch.nn.ReLU() output_layer = torch.nn.Linear(5, 1) model = torch.nn.Sequential(input_layer, relu_layer, output_layer) Feel free to add the layers of your choice. .. _pygadtorchgatorchga-class: ``pygad.torchga.TorchGA`` Class =============================== The ``pygad.torchga`` module has a class named ``TorchGA`` for creating an initial population for the genetic algorithm based on a PyTorch model. The constructor, methods, and attributes within the class are discussed in this section. .. _init: ``__init__()`` -------------- The ``pygad.torchga.TorchGA`` class constructor accepts the following parameters: - ``model``: An instance of the PyTorch model. - ``num_solutions``: Number of solutions in the population. Each solution has different parameters of the model. Instance Attributes ------------------- All parameters in the ``pygad.torchga.TorchGA`` 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 ``TorchGA`` Class -------------------------------- This section discusses the methods available for instances of the ``pygad.torchga.TorchGA`` 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-pygadtorchga-module: Functions in the ``pygad.torchga`` Module ========================================= This section discusses the functions in the ``pygad.torchga`` module. .. _pygadtorchgamodelweightsasvector: ``pygad.torchga.model_weights_as_vector()`` -------------------------------------------- The ``model_weights_as_vector()`` function accepts a single parameter named ``model`` representing the PyTorch 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. The function accepts the following parameters: - ``model``: The PyTorch model. It returns a 1D vector holding the model weights. .. _pygadtorchmodelweightsasdict: ``pygad.torch.model_weights_as_dict()`` --------------------------------------- The ``model_weights_as_dict()`` function accepts the following parameters: 1. ``model``: The PyTorch model. 2. ``weights_vector``: The model parameters as a vector. It returns the restored model weights in the same form used by the ``state_dict()`` method. The returned dictionary is ready to be passed to the ``load_state_dict()`` method for setting the PyTorch model's parameters. .. _pygadtorchgapredict: ``pygad.torchga.predict()`` --------------------------- The ``predict()`` function makes a prediction based on a solution. It accepts the following parameters: 1. ``model``: The PyTorch model. 2. ``solution``: The solution evolved. 3. ``data``: The test data inputs. It returns the predictions for the data samples. Examples ======== This section gives the complete code of some examples that build and train a PyTorch model using PyGAD. Each subsection builds a different network. Example 1: Regression Example ----------------------------- The next code builds a simple PyTorch model for regression. The next subsections discuss each part in the code. .. code:: python import torch import torchga import pygad def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, torch_ga, model, loss_function predictions = pygad.torchga.predict(model=model, solution=solution, data=data_inputs) abs_error = loss_function(predictions, data_outputs).detach().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]}") # Create the PyTorch model. input_layer = torch.nn.Linear(3, 5) relu_layer = torch.nn.ReLU() output_layer = torch.nn.Linear(5, 1) model = torch.nn.Sequential(input_layer, relu_layer, output_layer) # print(model) # Create an instance of the pygad.torchga.TorchGA class to build the initial population. torch_ga = torchga.TorchGA(model=model, num_solutions=10) loss_function = torch.nn.L1Loss() # Data inputs data_inputs = torch.tensor([[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 = torch.tensor([[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 = torch_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 & PyTorch - 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.torchga.predict(model=model, solution=solution, data=data_inputs) print("Predictions : \n", predictions.detach().numpy()) abs_error = loss_function(predictions, data_outputs) print("Absolute Error : ", abs_error.detach().numpy()) Create a PyTorch model ~~~~~~~~~~~~~~~~~~~~~~ According to the steps mentioned previously, the first step is to create a PyTorch model. Here is the code that builds the model using the Functional API. .. code:: python import torch input_layer = torch.nn.Linear(3, 5) relu_layer = torch.nn.ReLU() output_layer = torch.nn.Linear(5, 1) model = torch.nn.Sequential(input_layer, relu_layer, output_layer) .. _create-an-instance-of-the-pygadtorchgatorchga-class: Create an Instance of the ``pygad.torchga.TorchGA`` Class ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The second step is to create an instance of the ``pygad.torchga.TorchGA`` class. There are 10 solutions per population. Change this number according to your needs. .. code:: python import pygad.torchga torch_ga = torchga.TorchGA(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 calculates the mean absolute error (MAE) of the PyTorch 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 loss_function = torch.nn.L1Loss() def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, torch_ga, model, loss_function predictions = pygad.torchga.predict(model=model, solution=solution, data=data_inputs) abs_error = loss_function(predictions, data_outputs).detach().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 PyTorch 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 = torch_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 & PyTorch - Iteration vs. Fitness", linewidth=4) Here is the figure. .. image:: https://user-images.githubusercontent.com/16560492/103469779-22f5b480-4d37-11eb-80dc-95503065ebb1.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 = 145.42425295191546 Index of the best solution : 0 The next code restores the trained model weights using the ``model_weights_as_dict()`` function. The restored weights are used to calculate the predicted values. .. code:: python predictions = pygad.torchga.predict(model=model, solution=solution, data=data_inputs) print("Predictions : \n", predictions.detach().numpy()) .. code:: python Predictions : [[0.08401088] [0.60939324] [1.3010881 ] [2.5010352 ]] The next code measures the trained model error. .. code:: python abs_error = loss_function(predictions, data_outputs) print("Absolute Error : ", abs_error.detach().numpy()) .. code:: Absolute Error : 0.006876422 Example 2: XOR Binary Classification ------------------------------------ The next code creates a PyTorch model to build the XOR binary classification problem. Let's highlight the changes compared to the previous example. .. code:: python import torch import torchga import pygad def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, torch_ga, model, loss_function predictions = pygad.torchga.predict(model=model, solution=solution, data=data_inputs) solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().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]}") # Create the PyTorch model. input_layer = torch.nn.Linear(2, 4) relu_layer = torch.nn.ReLU() dense_layer = torch.nn.Linear(4, 2) output_layer = torch.nn.Softmax(1) model = torch.nn.Sequential(input_layer, relu_layer, dense_layer, output_layer) # print(model) # Create an instance of the pygad.torchga.TorchGA class to build the initial population. torch_ga = torchga.TorchGA(model=model, num_solutions=10) loss_function = torch.nn.BCELoss() # XOR problem inputs data_inputs = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) # XOR problem outputs data_outputs = torch.tensor([[1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0]]) # 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 = torch_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 & PyTorch - 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.torchga.predict(model=model, solution=solution, data=data_inputs) print("Predictions : \n", predictions.detach().numpy()) # Calculate the binary crossentropy for the trained model. print("Binary Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) # Calculate the classification accuracy of the trained model. a = torch.max(predictions, axis=1) b = torch.max(data_outputs, axis=1) accuracy = torch.sum(a.indices == b.indices) / len(data_outputs) print("Accuracy : ", accuracy.detach().numpy()) Compared to the previous regression example, here are the changes: - The PyTorch 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 input_layer = torch.nn.Linear(2, 4) relu_layer = torch.nn.ReLU() dense_layer = torch.nn.Linear(4, 2) output_layer = torch.nn.Softmax(1) model = torch.nn.Sequential(input_layer, relu_layer, dense_layer, 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 = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) # XOR problem outputs data_outputs = torch.tensor([[1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0]]) - The fitness value is calculated based on the binary cross entropy. .. code:: python loss_function = torch.nn.BCELoss() After the previous code completes, the next figure shows how the fitness value change by generation. .. image:: https://user-images.githubusercontent.com/16560492/103469818-c646c980-4d37-11eb-98c3-d9d591acd5e2.png :alt: Here is some information about the trained model. Its fitness value is ``100000000.0``, loss is ``0.0`` and accuracy is 100%. .. code:: python Fitness value of the best solution = 100000000.0 Index of the best solution : 0 Predictions : [[1.0000000e+00 1.3627675e-10] [3.8521746e-09 1.0000000e+00] [4.2789325e-10 1.0000000e+00] [1.0000000e+00 3.3668417e-09]] Binary Crossentropy : 0.0 Accuracy : 1.0 Example 3: Image Multi-Class Classification (Dense Layers) ---------------------------------------------------------- Here is the code. .. code:: python import torch import torchga import pygad import numpy def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, torch_ga, model, loss_function predictions = pygad.torchga.predict(model=model, solution=solution, data=data_inputs) solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().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 PyTorch model using the functional API. input_layer = torch.nn.Linear(360, 50) relu_layer = torch.nn.ReLU() dense_layer = torch.nn.Linear(50, 4) output_layer = torch.nn.Softmax(1) model = torch.nn.Sequential(input_layer, relu_layer, dense_layer, output_layer) # Create an instance of the pygad.torchga.TorchGA class to build the initial population. torch_ga = torchga.TorchGA(model=model, num_solutions=10) loss_function = torch.nn.CrossEntropyLoss() # Data inputs data_inputs = torch.from_numpy(numpy.load("dataset_features.npy")).float() # Data outputs data_outputs = torch.from_numpy(numpy.load("outputs.npy")).long() # The next 2 lines are equivelant to this Keras function to perform 1-hot encoding: tensorflow.keras.utils.to_categorical(data_outputs) # temp_outs = numpy.zeros((data_outputs.shape[0], numpy.unique(data_outputs).size), dtype=numpy.uint8) # temp_outs[numpy.arange(data_outputs.shape[0]), numpy.uint8(data_outputs)] = 1 # 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 = torch_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 & PyTorch - 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}") # Fetch the parameters of the best solution. best_solution_weights = torchga.model_weights_as_dict(model=model, weights_vector=solution) model.load_state_dict(best_solution_weights) predictions = model(data_inputs) # print("Predictions : \n", predictions) # Calculate the crossentropy loss of the trained model. print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) # Calculate the classification accuracy for the trained model. accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs) print("Accuracy : ", accuracy.detach().numpy()) Compared to the previous binary classification example, this example has multiple classes (4) and thus the loss is measured using cross entropy. .. code:: python loss_function = torch.nn.CrossEntropyLoss() .. _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. .. code:: python import numpy data_inputs = numpy.load("dataset_features.npy") data_outputs = numpy.load("outputs.npy") The next figure shows how the fitness value changes. .. image:: https://user-images.githubusercontent.com/16560492/103469855-5d138600-4d38-11eb-84b1-b5eff8faa7bc.png :alt: Here are some statistics about the trained model. .. code:: Fitness value of the best solution = 1.3446997034434534 Index of the best solution : 0 Crossentropy : 0.74366045 Accuracy : 1.0 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 torch import torchga import pygad import numpy def fitness_func(ga_instance, solution, sol_idx): global data_inputs, data_outputs, torch_ga, model, loss_function predictions = pygad.torchga.predict(model=model, solution=solution, data=data_inputs) solution_fitness = 1.0 / (loss_function(predictions, data_outputs).detach().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 PyTorch model. input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) relu_layer1 = torch.nn.ReLU() max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) relu_layer2 = torch.nn.ReLU() flatten_layer1 = torch.nn.Flatten() # The value 768 is pre-computed by tracing the sizes of the layers' outputs. dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) relu_layer3 = torch.nn.ReLU() dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) output_layer = torch.nn.Softmax(1) model = torch.nn.Sequential(input_layer, relu_layer1, max_pool1, conv_layer2, relu_layer2, flatten_layer1, dense_layer1, relu_layer3, dense_layer2, output_layer) # Create an instance of the pygad.torchga.TorchGA class to build the initial population. torch_ga = torchga.TorchGA(model=model, num_solutions=10) loss_function = torch.nn.CrossEntropyLoss() # Data inputs data_inputs = torch.from_numpy(numpy.load("dataset_inputs.npy")).float() data_inputs = data_inputs.reshape((data_inputs.shape[0], data_inputs.shape[3], data_inputs.shape[1], data_inputs.shape[2])) # Data outputs data_outputs = torch.from_numpy(numpy.load("dataset_outputs.npy")).long() # 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 = torch_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 & PyTorch - 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.torchga.predict(model=model, solution=solution, data=data_inputs) # print("Predictions : \n", predictions) # Calculate the crossentropy for the trained model. print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy()) # Calculate the classification accuracy for the trained model. accuracy = torch.sum(torch.max(predictions, axis=1).indices == data_outputs) / len(data_outputs) print("Accuracy : ", accuracy.detach().numpy()) 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 input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7) relu_layer1 = torch.nn.ReLU() max_pool1 = torch.nn.MaxPool2d(kernel_size=5, stride=5) conv_layer2 = torch.nn.Conv2d(in_channels=5, out_channels=3, kernel_size=3) relu_layer2 = torch.nn.ReLU() flatten_layer1 = torch.nn.Flatten() # The value 768 is pre-computed by tracing the sizes of the layers' outputs. dense_layer1 = torch.nn.Linear(in_features=768, out_features=15) relu_layer3 = torch.nn.ReLU() dense_layer2 = torch.nn.Linear(in_features=15, out_features=4) output_layer = torch.nn.Softmax(1) model = torch.nn.Sequential(input_layer, relu_layer1, max_pool1, conv_layer2, relu_layer2, flatten_layer1, dense_layer1, relu_layer3, dense_layer2, 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. .. code:: python import numpy data_inputs = numpy.load("dataset_inputs.npy") data_outputs = numpy.load("dataset_outputs.npy") The next figure shows how the fitness value changes. .. image:: https://user-images.githubusercontent.com/16560492/103469887-c7c4c180-4d38-11eb-98a7-1c5e73e918d0.png :alt: Here are some statistics about the trained model. The model accuracy is 97.5% after the 200 generations. Note that just running the code again may give different results. .. code:: Fitness value of the best solution = 1.3009520689219258 Index of the best solution : 0 Crossentropy : 0.7686678 Accuracy : 0.975