def test_backward(self):
x = np.random.randn(10, 10)
dout = np.random.randn(*x.shape)
dx_num = eval_numerical_gradient_array(lambda x: self._relu_forward(x),
x, dout)
relu = ReLU()
out = relu.forward(x)
relu.backward(dout)
dx = relu.dx
self.assertAlmostEquals(rel_error(dx_num, dx), 0, places=7)
def test_forward(self):
x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)
relu = ReLU()
out = relu.forward(x)
correct_out = np.array([[
0.,
0.,
0.,
0.,
], [
0.,
0.,
0.04545455,
0.13636364,
], [
0.22727273,
0.31818182,
0.40909091,
0.5,
]])
diff = rel_error(out, correct_out)
self.assertAlmostEquals(diff, 0, places=7)
def _relu_forward(self, x):
relu = ReLU()
return relu.forward(x)