def learn_test(self):
net = NeuralNet([1, 3, 1], -1, 1)
x = [[[-3]], [[2]], [[0]], [[-2]]]
y = [[[1]], [[1]], [[0]], [[0]]]
training_set = [x, y]
J = net.learn(training_set, 5000, 0.5)
plt.plot(J)
plt.show()
res = net.forward_prop([[-3]])
res_a = res[0]
a = res_a[len(res_a) - 1]
print(a)
print('-----------')
res = net.forward_prop([[2]])
res_a = res[0]
a = res_a[len(res_a) - 1]
print(a)
print('-----------')
res = net.forward_prop([[0]])
res_a = res[0]
a = res_a[len(res_a) - 1]
print(a)
print('-----------')
res = net.forward_prop([[-2]])
res_a = res[0]
a = res_a[len(res_a) - 1]
print(a)
print('-----------')
def back_prop_test(self):
net = NeuralNet([1, 3, 1], -1, 1)
net_layers = net.return_net()
net_layers[0].set_matrix(numpy.array([[1, 1], [2, 2], [3, 3]]))
net_layers[1].set_matrix(numpy.array([1, 2, 3, 4]))
net_layers[1].set_matrix(
numpy.reshape(net_layers[1].return_matrix(), (1, 4)))
net.set_net(net_layers)
x = numpy.array([[-3]])
forward_res = net.forward_prop(x)
res = net.back_prop(1, forward_res)
def forward_prop_test(self):
net = NeuralNet([3, 3, 3], -1, 1)
net_layers = net.return_net()
net_layers[0].set_matrix(
numpy.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]))
net_layers[1].set_matrix(
numpy.array([[-1, 1, -1, 1], [-2, 2, -2, 2], [3, -3, 3, -3]]))
net.set_net(net_layers)
x = numpy.array([[0.5], [-0.5], [-0.7]])
res = net.forward_prop(x)
res_a = res[0]
a = res_a[len(res_a) - 1]
# a = a[1:]
expected_res = numpy.array([[0.41089559], [0.3272766], [0.746644]])
self.assertEqual(a.all(), expected_res.all())
