def test_softmax_layer():
x_train = np.array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3.0, 1.4, 0.2],
[7.0, 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.3, 3.3, 6.0, 2.5],
[5.8, 2.7, 5.1, 1.9]])
y_train = np.array([0, 0, 1, 1, 2, 2])
softmax = SoftmaxLayer()
W = 0.001 * np.random.randn(4, 3)
b = np.zeros((1, 3))
reg_parameter = 0.001
g_numerical_W = eval_numerical_gradient(softmax.forward_pass, x_train, y_train, W, b, reg_parameter)
g_analytical_W = eval_analytical_gradient(softmax, x_train, y_train, W, b, reg_parameter)
assert check_gradient(g_numerical_W, g_analytical_W) <= 1e-7, "Error in calculating gradient of the SoftmaxLayer"
def test_softmax_layer():
x_train = np.array([[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2],
[7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5],
[6.3, 3.3, 6.0, 2.5], [5.8, 2.7, 5.1, 1.9]])
y_train = np.array([0, 0, 1, 1, 2, 2])
softmax = SoftmaxLayer()
W = 0.001 * np.random.randn(4, 3)
b = np.zeros((1, 3))
reg_parameter = 0.001
g_numerical_W = eval_numerical_gradient(softmax.forward_pass, x_train,
y_train, W, b, reg_parameter)
g_analytical_W = eval_analytical_gradient(softmax, x_train, y_train, W, b,
reg_parameter)
assert check_gradient(
g_numerical_W, g_analytical_W
) <= 1e-7, "Error in calculating gradient of the SoftmaxLayer"
def test_tanh_layer():
x_train = np.array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3.0, 1.4, 0.2],
[7.0, 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.3, 3.3, 6.0, 2.5],
[5.8, 2.7, 5.1, 1.9]])
y_train = np.array([0, 0, 1, 1, 2, 2])
W1 = np.random.randn(4, 10) * 0.001
b1 = np.zeros((1, 10))
W2 = np.random.randn(10, 6) * 0.001
b2 = np.zeros((1, 6))
softmax = SoftmaxLayer()
tanh = TanhLayer()
reg_parameter = 0.001
g_numerical_W = eval_hidden_numerical_gradient(tanh, softmax, x_train, y_train, W1, b1, W2, b2, reg_parameter)
g_analytical_W = eval_hidden_analytical_gradient(tanh, softmax, x_train, y_train, W1, b1, W2, b2, reg_parameter)
assert check_gradient(g_numerical_W, g_analytical_W) <= 1e-7, "Error in calculating gradient of the TanhLayer"
def test_tanh_layer():
x_train = np.array([[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2],
[7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5],
[6.3, 3.3, 6.0, 2.5], [5.8, 2.7, 5.1, 1.9]])
y_train = np.array([0, 0, 1, 1, 2, 2])
W1 = np.random.randn(4, 10) * 0.001
b1 = np.zeros((1, 10))
W2 = np.random.randn(10, 6) * 0.001
b2 = np.zeros((1, 6))
softmax = SoftmaxLayer()
tanh = TanhLayer()
reg_parameter = 0.001
g_numerical_W = eval_hidden_numerical_gradient(tanh, softmax, x_train,
y_train, W1, b1, W2, b2,
reg_parameter)
g_analytical_W = eval_hidden_analytical_gradient(tanh, softmax, x_train,
y_train, W1, b1, W2, b2,
reg_parameter)
assert check_gradient(
g_numerical_W, g_analytical_W
) <= 1e-7, "Error in calculating gradient of the TanhLayer"
