def _testSelu(self, np_features, use_gpu=False):
np_selu = self._npSelu(np_features)
with self.test_session(use_gpu=use_gpu):
selu = nn_ops.selu(np_features)
tf_selu = selu.eval()
self.assertAllClose(np_selu, tf_selu)
self.assertShapeEqual(np_selu, selu)
def _testSelu(self, np_features, use_gpu=False):
np_selu = self._npSelu(np_features)
with self.test_session(use_gpu=use_gpu):
selu = nn_ops.selu(np_features)
tf_selu = selu.eval()
self.assertAllClose(np_selu, tf_selu)
self.assertShapeEqual(np_selu, selu)
def testGradientFloat64(self):
with self.cached_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = constant_op.constant(x_val, dtype=dtypes.float64, name="x")
y = nn_ops.selu(x, name="selu")
x_init = np.asarray(x_val, dtype=np.float64, order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("selu (float64) gradient err = ", err)
self.assertLess(err, 1e-6)
def testGradientFloat64(self):
with self.test_session():
x_val = [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]]
x = constant_op.constant(x_val, dtype=dtypes.float64, name="x")
y = nn_ops.selu(x, name="selu")
x_init = np.asarray(x_val, dtype=np.float64, order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], y, [2, 5], x_init_value=x_init)
print("selu (float64) gradient err = ", err)
self.assertLess(err, 1e-6)
def testGradGradFloat32(self):
with self.cached_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.selu(x, name="selu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("selu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def testGradGradFloat32(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.selu(x, name="selu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(
x, [2, 5], z[0], [2, 5], x_init_value=x_init)
print("selu (float32) gradient of gradient err = ", err)
self.assertLess(err, 1e-4)
def f(x):
assert x.dtype == dtypes.float64
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.selu(x)
return tape.gradient(y, x)
def _testSelu(self, np_features):
np_selu = self._npSelu(np_features)
tf_selu = nn_ops.selu(np_features)
self.assertAllClose(np_selu, tf_selu)
self.assertShapeEqual(np_selu, tf_selu)
def f(x):
assert x.dtype == dtypes.float64
with backprop.GradientTape() as tape:
tape.watch(x)
y = nn_ops.selu(x)
return tape.gradient(y, x)
def _testSelu(self, np_features): np_selu = self._npSelu(np_features) tf_selu = nn_ops.selu(np_features) self.assertAllClose(np_selu, tf_selu) self.assertShapeEqual(np_selu, tf_selu)
