# Image Classification

In the documentation of the `pygad.nn` module, a neural network is created for classifying images from the Fruits360 dataset without being trained using an optimization algorithm. This section discusses how to train such a classifier using the genetic algorithm with the help of the `pygad.gann` module.

Please make sure that the training data files [dataset_features.npy](https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy) and [outputs.npy](https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy) are available. For downloading them, use these links:

1. [dataset_features.npy](https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy): The features https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy
2. [outputs.npy](https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy): The class labels https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy

After the data is available, here is the complete code that builds and trains a neural network using the genetic algorithm for classifying images from 4 classes of the Fruits360 dataset. 

Because there are 4 classes, the output layer is assigned has 4 neurons according to the `num_neurons_output` parameter of the `pygad.gann.GANN` class constructor.

```python
import numpy
import pygad
import pygad.nn
import pygad.gann

def fitness_func(ga_instance, solution, sol_idx):
    global GANN_instance, data_inputs, data_outputs

    predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx],
                                   data_inputs=data_inputs)
    correct_predictions = numpy.where(predictions == data_outputs)[0].size
    solution_fitness = (correct_predictions/data_outputs.size)*100

    return solution_fitness

def callback_generation(ga_instance):
    global GANN_instance, last_fitness

    population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, 
                                                            population_vectors=ga_instance.population)

    GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices)

    print(f"Generation = {ga_instance.generations_completed}")
    print(f"Fitness    = {ga_instance.best_solution()[1]}")
    print(f"Change     = {ga_instance.best_solution()[1] - last_fitness}")

    last_fitness = ga_instance.best_solution()[1].copy()

# Holds the fitness value of the previous generation.
last_fitness = 0

# Reading the input data.
data_inputs = numpy.load("dataset_features.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/dataset_features.npy

# Optional step of filtering the input data using the standard deviation.
features_STDs = numpy.std(a=data_inputs, axis=0)
data_inputs = data_inputs[:, features_STDs>50]

# Reading the output data.
data_outputs = numpy.load("outputs.npy") # Download from https://github.com/ahmedfgad/NumPyANN/blob/master/outputs.npy

# The length of the input vector for each sample (i.e. number of neurons in the input layer).
num_inputs = data_inputs.shape[1]
# The number of neurons in the output layer (i.e. number of classes).
num_classes = 4

# Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched.
num_solutions = 8 # A solution or a network can be used interchangeably.
GANN_instance = pygad.gann.GANN(num_solutions=num_solutions,
                                num_neurons_input=num_inputs,
                                num_neurons_hidden_layers=[150, 50],
                                num_neurons_output=num_classes,
                                hidden_activations=["relu", "relu"],
                                output_activation="softmax")

# population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably.
# If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed.
population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks)

# To prepare the initial population, there are 2 ways:
# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.
# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.
initial_population = population_vectors.copy()

num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool.

num_generations = 500 # Number of generations.

mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.

parent_selection_type = "sss" # Type of parent selection.

crossover_type = "single_point" # Type of the crossover operator.

mutation_type = "random" # Type of the mutation operator.

keep_parents = -1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.

ga_instance = pygad.GA(num_generations=num_generations, 
                       num_parents_mating=num_parents_mating, 
                       initial_population=initial_population,
                       fitness_func=fitness_func,
                       mutation_percent_genes=mutation_percent_genes,
                       parent_selection_type=parent_selection_type,
                       crossover_type=crossover_type,
                       mutation_type=mutation_type,
                       keep_parents=keep_parents,
                       on_generation=callback_generation)

ga_instance.run()

# After the generations complete, a plot is shown that summarizes how the fitness values evolve over the generations.
ga_instance.plot_fitness()

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print(f"Parameters of the best solution : {solution}")
print(f"Fitness value of the best solution = {solution_fitness}")
print(f"Index of the best solution : {solution_idx}")

if ga_instance.best_solution_generation != -1:
    print(f"Best fitness value reached after {ga_instance.best_solution_generation} generations.")

# Predicting the outputs of the data using the best solution.
predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx],
                               data_inputs=data_inputs)
print(f"Predictions of the trained network : {predictions}")

# Calculating some statistics
num_wrong = numpy.where(predictions != data_outputs)[0]
num_correct = data_outputs.size - num_wrong.size
accuracy = 100 * (num_correct/data_outputs.size)
print(f"Number of correct classifications : {num_correct}.")
print(f"Number of wrong classifications : {num_wrong.size}.")
print(f"Classification accuracy : {accuracy}.")
```

After training completes, here are the outputs of the print statements. The number of wrong classifications is only 1 and the accuracy is 99.949%. This accuracy is reached after 482 generations.

```
Fitness value of the best solution = 99.94903160040775
Index of the best solution : 0
Best fitness value reached after 482 generations.
Number of correct classifications : 1961.
Number of wrong classifications : 1.
Classification accuracy : 99.94903160040775.
```

The next figure shows how fitness value evolves by generation.

![Training Neural Networks using Genetic Algorithm](https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png)