def plot_time_vs_epochs(train_data, test_data):
timer = Timer()
number_epochs = []
time = []
total_time = 0
# Build Neural Network
neural_network = build_network()
# 200 runs of 100 epochs each
for i in range(100):
number_epochs.append(i)
timer.start()
for j in range(1000):
for data in test_data:
neural_network.feed(data)
this_time = timer.stop()
time.append(this_time)
total_time += this_time
mean_time = total_time / len(number_epochs)
# Plot
plt.figure()
plt.title("Time Taken in 1000 Epochs", fontsize=20)
plt.xlabel('Number of Experiment')
plt.ylabel('Time (seconds)')
plt.scatter(number_epochs, time, color='blue')
plt.axhline(y=mean_time, color='r', linestyle='-')
plt.show()
def plot_genetic_algorithm_time():
timer = Timer()
experiments = []
time = []
total_time = 0
# Build Neural Network
genetic_algorithm = GeneticFixedTopology(100, 1000)
# 20 runs of 1000 generations each
for i in range(20):
experiments.append(i)
timer.start()
genetic_algorithm.run()
this_time = timer.stop()
time.append(this_time)
genetic_algorithm = GeneticFixedTopology(100, 1000)
total_time += this_time
mean_time = total_time / len(experiments)
# Plot
plt.figure()
plt.title("Time Taken in 1000 Generations", fontsize=20)
plt.xlabel('experimento')
plt.ylabel('tiempo (segundos)')
plt.scatter(experiments, time, color='blue')
plt.axhline(y=mean_time, color='r', linestyle='-')
plt.show()
