def simulate_generations(self, num_generations, print_best):
file_path = "csv/ESN_Results.csv"
dw = DataWriter()
dr = DataReader()
dw.init_table(file_path)
p = Predictor()
mapping = dr.get_mapping()
images = dr.get_images(112800, 28, 28) # 112800 images in data set
scale_factor = 10
for i in range(num_generations):
sum = 0
best_score = -100
best_accuracy = -100
best_net = []
engines = []
for net in self.networks:
engine = [net, 0, 0]
engines.append(engine)
p.make_predictions(engines, mapping, images, scale_factor)
for j in range(len(engines)):
self.networks[j].fitness = engines[j][1]
if engines[j][2] > best_score:
best_score = engines[j][2]
best_net = self.networks[j]
if engines[j][1] > best_accuracy:
best_accuracy = engines[j][1]
avg_accuracy = self.avg_fitness(self.networks) # avg accuracy
for j in range(len(engines)):
self.networks[j].fitness = engines[j][2] # change fitness to score
avg_score = self.avg_fitness(self.networks) # avg accuracy
avg_size = self.avg_network_size()
if print_best:
best_net.show_net()
print("-----------------------------------\t\t\t\t\t\t\n Generation " + str(i+1) + " results\n-----------------------------------\n", end='\n')
print("Highest accuracy: " + str(best_accuracy*100) + "%\nHighest score: " + str(best_score**(1.0/scale_factor)) + "\nAverage accuracy: " + str(avg_accuracy*100) + "%\nAverage score: " + str(avg_score**(1.0/scale_factor)) + "\nNum species: " + str(len(self.species)) + "\nInnovs tried: " + str(self.networks[0].master_innov[0]) + "\nAverage connections per network: " + str(avg_size) + "\n")
non_jit = self.construct_non_jit(best_net)
pickle.dump(non_jit, open("neural_net.txt", "wb"))
dw.write_row(file_path, [i+1, best_accuracy*100, avg_accuracy*100, best_score**(1.0/scale_factor), avg_score**(1.0/scale_factor), avg_size])
if i != num_generations-1:
self.prepare_next_gen(math.ceil(self.pop_size/10))
print("\nStarting Generation " + str(i+2) + ": Species = " + str(len(self.species)) + ", Innovs = " + str(self.networks[0].master_innov[0]), end='\n')
print("Finished simulation!")
p = Population(784, 47, 1, master_connection_dict, innov_list, 0)
dr = DataReader()
mapping = dr.get_mapping()
images = dr.get_images(
112800, 28, 28) # 112800 images in data set, each image 28x28 pixels
mp = p.networks[0]
for q in range(200):
print("Adding connection " + str(q + 1), end='\r')
mp.add_connection()
#mp.randomize_all_bias()
print("\nStarting predictions...")
engines = [[mp, 0, 0]]
pr.make_predictions(engines, mapping, images, 1)
base = engines[0][2]
start_score = base
for i in range(1000):
print("\nEpoch " + str(i + 1))
pr.make_predictions(engines, mapping, images, 1)
non_jit = p.construct_non_jit(mp)
pickle.dump(non_jit, open("neural_net.txt", "wb"))
#end_score = engines[0][2]
#print("\nStart score = " + str(start_score) + ", End score = " + str(end_score))
#input("Done!")
##len(mp.connection_list)
#start = 5000 #next start = previous end = 5000
