def sine_training():
n = NeuralNet([1, 5, 1])
sines = get_sines(10)
testing_data = next(sines)
for i in range(1000):
x, y = next(sines)
n.train(x, y, 10, 1, testing_data=testing_data)
# validation
x, y = next(sines)
hx = n.predict(x)
errors = []
print('doing validation---------------------------')
for x, y, y_predicted in zip(x, y, hx):
errors.append(abs(y[0] - y_predicted[0]) / y[0])
print(x[0], y[0], y_predicted[0])
print(sum(errors) / len(errors))
x = np.reshape([2 * math.pi * i/1000 for i in range(1000)], (1000, 1))
y = [(i[0] - 0.5)*2 for i in n.predict(x)]
y_actual = [i[0] for i in np.sin(x)]
plt.plot(x, y_actual)
plt.hold(True)
plt.plot(x, y)
plt.show()
def sine_training():
n = NeuralNet([1, 5, 1])
sines = get_sines(10)
testing_data = next(sines)
for i in range(1000):
x, y = next(sines)
n.train(x, y, 10, 1, testing_data=testing_data)
# validation
x, y = next(sines)
hx = n.predict(x)
errors = []
print('doing validation---------------------------')
for x, y, y_predicted in zip(x, y, hx):
errors.append(abs(y[0] - y_predicted[0]) / y[0])
print(x[0], y[0], y_predicted[0])
print(sum(errors) / len(errors))
x = np.reshape([2 * math.pi * i / 1000 for i in range(1000)], (1000, 1))
y = [(i[0] - 0.5) * 2 for i in n.predict(x)]
y_actual = [i[0] for i in np.sin(x)]
plt.plot(x, y_actual)
plt.hold(True)
plt.plot(x, y)
plt.show()
def run_training(self, metadata, predict_test_data=False):
network = NeuralNet(layers=metadata['layers'],
lmda=metadata['lambda'])
generator = self.reader.get(metadata['batch_size'], 'training')
for i in range(metadata['epochs']):
try:
x, y = next(generator)
except StopIteration:
return network
network.train(x, y, iterations=metadata['iterations'],
learning_rate=metadata['learning_rate'])
return network
