def testGradients(self):
shape = [5, 3, 4]
sigma = 5
input_values = np.random.randn(*shape) * sigma
x_tf = constant_op.constant(input_values)
y_tf = nn_impl.swish(x_tf)
with self.test_session():
err = gradient_checker.compute_gradient_error(x_tf, shape, y_tf, shape)
self.assertLess(err, 1e-4)
def testValues(self):
np_values = np.array(
[np.linspace(-10.0, 0.0, 100),
np.linspace(0.0, 10.0, 100)],
dtype=np.float32)
tf_values = constant_op.constant(np_values)
actual_tf_outputs = nn_impl.swish(tf_values)
expected_tf_outputs = tf_values * math_ops.sigmoid(tf_values)
with self.test_session() as sess:
actual_outputs, expected_outputs = sess.run(
[actual_tf_outputs, expected_tf_outputs])
self.assertAllClose(actual_outputs, expected_outputs)
