def test_backprop(self):
X = np.array([[1, 2, 3], [2, 3, 4]])
y = np.array([[0, 0, 1], [0, 0, 1]])
res, a = neur.forward_prop(X, [self.theta, self.theta2])
grads = neur.backprop(a, y, [self.theta, self.theta2], 0)
grads_check = neur.gradient_check(X, y, [self.theta, self.theta2], neur.logistic_squared_cost_function)
self.equalish(grads[0], grads_check[0])
self.equalish(grads[1], grads_check[1])
def test_backprop(self):
X = np.array([[1, 2, 3], [2, 3, 4]])
y = np.array([[0, 0, 1], [0, 0, 1]])
res, a = neur.forward_prop(X, [self.theta, self.theta2])
grads = neur.backprop(a, y, [self.theta, self.theta2], 0)
grads_check = neur.gradient_check(X, y, [self.theta, self.theta2],
neur.logistic_squared_cost_function)
self.equalish(grads[0], grads_check[0])
self.equalish(grads[1], grads_check[1])
def test_gradient_check(self):
X = np.array([[1, 2, 3], [2, 3, 4]])
y = np.array([[0, 0, 1], [0, 0, 1]])
res = neur.gradient_check(X, y, [self.theta, self.theta2],
neur.logistic_squared_cost_function)
self.equalish(
res[0],
np.array([[0.00562904, 0.00841451, 0.01404355, 0.01967259],
[-0.02588239, -0.03870536, -0.06458776, -0.09047015]]))
self.equalish(
res[1],
np.array([[0.5341706, 0.3233084, 0.30720236],
[0.4745306, 0.28720531, 0.27289721],
[-0.45907194, -0.2778482, -0.26400618]]))
def test_contractive_reg_gradient(self):
X = np.array([[1, 0, 1], [1, 1, 0]])
def cost_func(x, y, thetas, lamb):
self.enc.encode_weights = thetas[0]
out, a = self.enc.forward_prop(x)
return self.enc.contractive_reg(a[1][:, 1:])
self.enc.encode_weights = self.theta
gradients = neur.gradient_check(X, X, [self.enc.encode_weights], cost_func)
self.enc.encode_weights = self.theta
out, a = self.enc.forward_prop(X)
grad_new = self.enc.contractive_reg_gradient(a[1][:,1:]) # get rid of ones from argument
self.assertTrue(((grad_new - gradients) < 0.00035).all())
def test_gradient_check(self):
X = np.array([[1, 2, 3], [2, 3, 4]])
y = np.array([[0, 0, 1], [0, 0, 1]])
res = neur.gradient_check(X, y, [self.theta, self.theta2], neur.logistic_squared_cost_function)
self.equalish(
res[0],
np.array(
[[0.00562904, 0.00841451, 0.01404355, 0.01967259], [-0.02588239, -0.03870536, -0.06458776, -0.09047015]]
),
)
self.equalish(
res[1],
np.array(
[
[0.5341706, 0.3233084, 0.30720236],
[0.4745306, 0.28720531, 0.27289721],
[-0.45907194, -0.2778482, -0.26400618],
]
),
)
