def test_linear_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
C = np.random.choice(range(1, 10))
x = np.random.randn(N, D)
dout = np.random.randn(N, C)
layer = LinearModule(D, C)
out = layer.forward(x)
dx = layer.backward(dout)
dw = layer.grads['weight']
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
dw_num = eval_numerical_gradient_array(lambda w: layer.forward(x), layer.params['weight'], dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
self.assertLess(rel_error(dw, dw_num), rel_error_max)
def test_linear_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20)) #batch size
D = np.random.choice(range(1, 100)) #num in_features
C = np.random.choice(range(1, 10)) #num classes = out_features
x = np.random.randn(N, D) #mini-batch
dout = np.random.randn(N, C) #cross-entropy loss?
layer = LinearModule(D, C)
out = layer.forward(x)
dx = layer.backward(dout)
dw = layer.grads['weight']
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
dw_num = eval_numerical_gradient_array(lambda w: layer.forward(x), layer.params['weight'], dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
self.assertLess(rel_error(dw, dw_num), rel_error_max)