def test_reg_grad():
print('*'*5, 'Testing Gradient')
X = np.array([[1.0, 0.0], [1.0, 1.0], [1.0, -1.0]])
w = np.ones((2, 3))
y = np.eye(3, dtype='int64')
reg = 1.0
f = lambda z: soft_cost(X, y, W=z, reg=reg)
numerical_grad_check(f, w)
print('Test Success')
def test_grad():
print('*' * 5, 'Testing Gradient')
X = np.array([[1.0, 0.0], [1.0, 1.0], [1.0, -1.0]])
w = np.ones((2, 3))
y = np.array([0, 1, 2])
scl = SoftmaxClassifier(num_classes=3)
f = lambda z: scl.cost_grad(X, y, W=z)
numerical_grad_check(f, w)
print('Test Success')
def test_reg_grad():
print('*' * 5, 'Testing Gradient')
X = np.array([[1.0, 0.0], [1.0, 1.0]])
w = np.array([0.0, 0.0])
y = np.array([0, 0]).astype('int64')
reg = 1.0
f = lambda z: log_cost(X, y, w=z, reg=reg)
numerical_grad_check(f, w)
print('Test Success')
def test_grad():
print('*' * 5, 'Testing Gradient')
X = np.array([[1.0, 0.0], [1.0, 1.0], [2.0, 3.0]])
w = np.array([0.0, 0.0])
y = np.array([0, 0, 1]).astype('int64')
print('shapes', X.shape, w.shape, y.shape)
lr = LogisticRegressionClassifier()
f = lambda z: lr.cost_grad(X, y, w=z)
numerical_grad_check(f, w)
print('Test Success')
