def train_and_score(self, genome):
"""
Train and score a single individual.
Score (fitness) is equal to a model's accuracy on test data predictions.
"""
trainer = Trainer(CNNModel.buildForEvolution(genome), self.config,
self.data)
trainer.train() # train individual using training data
score = trainer.model.evaluate(
self.data['testX'], self.data['testY'],
verbose=0) # score individual using test data
logging.info("Score : " + str(score[1])) # 1=accuracy, 0=loss.
genome.fitness = score[1] # set the individual's fitness variable
return score
if config['evolvingMode']:
resultsDir = ''
if config['intelligentSurvival']:
resultsDir = 'results/evolution-intelligent-survival/'
else:
resultsDir = 'results/evolution-plain/'
evolution = Evolution(config, data)
evolution.initialise_population()
for gen in range(numGenerations):
evolution.evolvePopulation(gen)
best_evolved_genome = evolution.hall_of_fame.getSolution(0)
trainer = Trainer(CNNModel.buildForEvolution(best_evolved_genome), config,
data)
generate_model_summary(trainer.model)
training_history = trainer.train()
generate_classification_report(trainer.model, data)
generate_training_stats_plot(config, training_history, resultsDir)
save_model(trainer.model, resultsDir)
score = trainer.model.evaluate(data['trainX'], data['trainY'], verbose=0)
print("Score: " + str(score[1]))
else:
resultsDir = 'results/no-evolution/'
trainer = Trainer(
CNNModel.buildNoEvolution(), config,
data) # initialise trainer with a newly initialised model
generate_model_summary(trainer.model) # summary of the model structure
training_history = trainer.train(
