def testSecondGradient(self):
images_placeholder = array_ops.placeholder(dtypes.float32, shape=(3, 2))
labels_placeholder = array_ops.placeholder(dtypes.int32, shape=(3))
weights = variables.Variable(random_ops.truncated_normal([2], stddev=1.0))
weights_with_zeros = array_ops.stack([array_ops.zeros([2]), weights],
axis=1)
logits = math_ops.matmul(images_placeholder, weights_with_zeros)
cross_entropy = nn_ops.sparse_softmax_cross_entropy_with_logits(
labels=labels_placeholder, logits=logits)
loss = math_ops.reduce_mean(cross_entropy)
# Taking ths second gradient should fail, since it is not
# yet supported.
with self.assertRaisesRegexp(LookupError,
"explicitly disabled"):
_ = gradients_impl.hessians(loss, [weights])
def testSecondGradient(self):
images_placeholder = array_ops.placeholder(dtypes.float32, shape=(3, 2))
labels_placeholder = array_ops.placeholder(dtypes.int32, shape=(3))
weights = variables.Variable(random_ops.truncated_normal([2], stddev=1.0))
weights_with_zeros = array_ops.stack([array_ops.zeros([2]), weights],
axis=1)
logits = math_ops.matmul(images_placeholder, weights_with_zeros)
cross_entropy = nn_ops.sparse_softmax_cross_entropy_with_logits(
labels=labels_placeholder, logits=logits)
loss = math_ops.reduce_mean(cross_entropy)
# Taking ths second gradient should fail, since it is not
# yet supported.
with self.assertRaisesRegexp(LookupError,
"explicitly disabled"):
_ = gradients_impl.hessians(loss, [weights])
def testSecondGradient(self):
with self.test_session():
l = constant_op.constant([0.0, 0.0, 1.0, 0.0,
1.0, 0.0, 0.0, 0.0,
0.0, 0.5, 0.0, 0.5], shape=[12],
dtype=dtypes.float64, name="l")
f = constant_op.constant([0.1, 0.2, 0.3, 0.4,
0.1, 0.4, 0.9, 1.6,
0.1, 0.8, 2.7, 6.4], shape=[12],
dtype=dtypes.float64, name="f")
x = nn_ops.softmax_cross_entropy_with_logits(labels=l, logits=f,
name="xent")
loss = math_ops.reduce_mean(x)
# Taking ths second gradient should fail, since it is not
# yet supported.
with self.assertRaisesRegexp(LookupError,
"explicitly disabled"):
_ = gradients_impl.hessians(loss, [f])
def testSecondGradient(self):
with self.test_session():
l = constant_op.constant(
[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5],
shape=[12],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[12],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits(labels=l,
logits=f,
name="xent")
loss = math_ops.reduce_mean(x)
# Taking ths second gradient should fail, since it is not
# yet supported.
with self.assertRaisesRegexp(LookupError, "explicitly disabled"):
_ = gradients_impl.hessians(loss, [f])