def fitness_func(solution, sol_idx):
global data_inputs, data_outputs, torch_ga, model, loss_function
model_weights_dict = torchga.model_weights_as_dict(model=model,
weights_vector=solution)
model.load_state_dict(model_weights_dict)
predictions = model(data_inputs)
solution_fitness = 1.0 / (
loss_function(predictions, data_outputs).detach().numpy() + 0.00000001)
return solution_fitness
def fitness_func(solution, sol_idx):
global data_inputs, data_outputs, torch_ga, model, loss_function
model_weights_dict = torchga.model_weights_as_dict(model=model,
weights_vector=solution)
# Use the current solution as the model parameters.
model.load_state_dict(model_weights_dict)
predictions = model(data_inputs)
abs_error = loss_function(predictions,
data_outputs).detach().numpy() + 0.00000001
solution_fitness = 1.0 / abs_error
return solution_fitness
fitness_func=fitness_func,
parent_selection_type=parent_selection_type,
crossover_type=crossover_type,
mutation_type=mutation_type,
mutation_percent_genes=mutation_percent_genes,
keep_parents=keep_parents,
on_generation=callback_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_result(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("Fitness value of the best solution = {solution_fitness}".format(
solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(
solution_idx=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.detach().numpy())
abs_error = loss_function(predictions, data_outputs)
print("Absolute Error : ", abs_error.detach().numpy())