def test_matmul_two_vars(self): x2 = ad.Variable(name="x2") x3 = ad.Variable(name="x3") y = ad.matmul_op(x2, x3) grad_x2, grad_x3 = ad.gradients(y, [x2, x3]) executor = ad.Executor([y, grad_x2, grad_x3]) x2_val = np.array([[1, 2], [3, 4], [5, 6]]) # 3x2 x3_val = np.array([[7, 8, 9], [10, 11, 12]]) # 2x3 y_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={ x2: x2_val, x3: x3_val }) expected_yval = np.matmul(x2_val, x3_val) expected_grad_x2_val = np.matmul(np.ones_like(expected_yval), np.transpose(x3_val)) expected_grad_x3_val = np.matmul(np.transpose(x2_val), np.ones_like(expected_yval)) assert isinstance(y, ad.Node) assert np.array_equal(y_val, expected_yval) assert np.array_equal(grad_x2_val, expected_grad_x2_val) assert np.array_equal(grad_x3_val, expected_grad_x3_val)
def test_grad_of_grad(self): x2 = ad.Variable(name="x2") x3 = ad.Variable(name="x3") y = x2 * x2 + x2 * x3 grad_x2, grad_x3 = ad.gradients(y, [x2, x3]) grad_x2_x2, grad_x2_x3 = ad.gradients(grad_x2, [x2, x3]) executor = ad.Executor([y, grad_x2, grad_x3, grad_x2_x2, grad_x2_x3]) x2_val = 2 * np.ones(3) x3_val = 3 * np.ones(3) y_val, grad_x2_val, grad_x3_val, grad_x2_x2_val, grad_x2_x3_val = executor.run( feed_dict={ x2: x2_val, x3: x3_val }) expected_yval = x2_val * x2_val + x2_val * x3_val expected_grad_x2_val = 2 * x2_val + x3_val expected_grad_x3_val = x2_val expected_grad_x2_x2_val = 2 * np.ones_like(x2_val) expected_grad_x2_x3_val = 1 * np.ones_like(x2_val) assert isinstance(y, ad.Node) assert np.array_equal(y_val, expected_yval) assert np.array_equal(grad_x2_val, expected_grad_x2_val) assert np.array_equal(grad_x3_val, expected_grad_x3_val) assert np.array_equal(grad_x2_x2_val, expected_grad_x2_x2_val) assert np.array_equal(grad_x2_x3_val, expected_grad_x2_x3_val)
def test_add_mul_mix_3(self): x2 = ad.Variable(name="x2") x3 = ad.Variable(name="x3") z = x2 * x2 + x2 + x3 + 3 y = z * z + x3 grad_x2, grad_x3 = ad.gradients(y, [x2, x3]) executor = ad.Executor([y, grad_x2, grad_x3]) x2_val = 2 * np.ones(3) x3_val = 3 * np.ones(3) y_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={ x2: x2_val, x3: x3_val }) z_val = x2_val * x2_val + x2_val + x3_val + 3 expected_yval = z_val * z_val + x3_val expected_grad_x2_val = 2 * (x2_val * x2_val + x2_val + x3_val + 3) * (2 * x2_val + 1) expected_grad_x3_val = 2 * (x2_val * x2_val + x2_val + x3_val + 3) + 1 assert isinstance(y, ad.Node) assert np.array_equal(y_val, expected_yval) assert np.array_equal(grad_x2_val, expected_grad_x2_val) assert np.array_equal(grad_x3_val, expected_grad_x3_val)
def test_add_mul_mix_2(self): x1 = ad.Variable(name="x1") x2 = ad.Variable(name="x2") x3 = ad.Variable(name="x3") x4 = ad.Variable(name="x4") y = x1 + x2 * x3 * x4 grad_x1, grad_x2, grad_x3, grad_x4 = ad.gradients(y, [x1, x2, x3, x4]) executor = ad.Executor([y, grad_x1, grad_x2, grad_x3, grad_x4]) x1_val = 1 * np.ones(3) x2_val = 2 * np.ones(3) x3_val = 3 * np.ones(3) x4_val = 4 * np.ones(3) y_val, grad_x1_val, grad_x2_val, grad_x3_val, grad_x4_val = executor.run( feed_dict={ x1: x1_val, x2: x2_val, x3: x3_val, x4: x4_val }) assert isinstance(y, ad.Node) assert np.array_equal(y_val, x1_val + x2_val * x3_val * x4_val) assert np.array_equal(grad_x1_val, np.ones_like(x1_val)) assert np.array_equal(grad_x2_val, x3_val * x4_val) assert np.array_equal(grad_x3_val, x2_val * x4_val) assert np.array_equal(grad_x4_val, x2_val * x3_val)
def test_identity(self): x2 = ad.Variable(name="x2") y = x2 grad_x2, = ad.gradients(y, [x2]) executor = ad.Executor([y, grad_x2]) x2_val = 2 * np.ones(3) y_val, grad_x2_val = executor.run(feed_dict={x2: x2_val}) assert isinstance(y, ad.Node) assert np.array_equal(y_val, x2_val) assert np.array_equal(grad_x2_val, np.ones_like(x2_val))
def test_mul_two_vars(self): x2 = ad.Variable(name="x2") x3 = ad.Variable(name="x3") y = x2 * x3 grad_x2, grad_x3 = ad.gradients(y, [x2, x3]) executor = ad.Executor([y, grad_x2, grad_x3]) x2_val = 2 * np.ones(3) x3_val = 3 * np.ones(3) y_val, grad_x2_val, grad_x3_val = executor.run(feed_dict={ x2: x2_val, x3: x3_val }) assert isinstance(y, ad.Node) assert np.array_equal(y_val, x2_val * x3_val) assert np.array_equal(grad_x2_val, x3_val) assert np.array_equal(grad_x3_val, x2_val)