def _SymGrad(op, out_grads):
"""Backprop through a function call node op given its outputs' gradients."""
f_in = [x for x in op.inputs] + out_grads
f_types = [x.dtype for x in op.inputs]
f = attr_value_pb2.NameAttrList()
f.name = op.type
for k in op.node_def.attr:
f.attr[k].CopyFrom(op.node_def.attr[k])
in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f)
return in_grads
def _SymGrad(op, out_grads):
"""Backprop through a function call node op given its outputs' gradients."""
f_in = [x for x in op.inputs] + out_grads
f_types = [x.dtype for x in op.inputs]
f = attr_value_pb2.NameAttrList()
f.name = op.type
for k in op.node_def.attr:
f.attr[k].CopyFrom(op.node_def.attr[k])
# pylint: disable=protected-access
in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f)
# pylint: enable=protected-access
return in_grads
def _SymGrad(op, out_grads):
"""Backprop through a function call node op given its outputs' gradients."""
f_in = [x for x in op.inputs] + out_grads
f_types = [default_gradient.get_zeros_dtype(x) for x in op.inputs]
f = attr_value_pb2.NameAttrList()
if _IsPartitionedCall(op):
f.name = op.get_attr("f").name
else:
f.name = op.type
for k in op.node_def.attr:
f.attr[k].CopyFrom(op.node_def.attr[k])
in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f)
return in_grads
def testSymGradShape(self):
g = tf.Graph()
with g.as_default():
x = tf.placeholder(tf.float32, [25, 4])
y = tf.placeholder(tf.float32, [200, 100])
dz = tf.placeholder(tf.float32, [1])
# We assume Foo is a function of (x, y) -> (z) Then, Foo's
# gradient function is (x, y, dz) -> (dx, dy). dx's shape
# should be the same as x's; and dy's shape should be the same
# as y's.
dx, dy = symbolic_gradient(input=[x, y, dz], Tout=[tf.float32] * 2, f="Foo")
self.assertEquals(x.get_shape(), dx.get_shape())
self.assertEquals(y.get_shape(), dy.get_shape())
def _SymGrad(op, out_grads):
"""Backprop through a function call node op given its outputs' gradients."""
f_in = [x for x in op.inputs] + out_grads
f_types = [x.dtype for x in op.inputs]
f = attr_value_pb2.NameAttrList()
f.name = op.type
for k in op.node_def.attr:
f.attr[k].CopyFrom(op.node_def.attr[k])
# TODO(apassos) use a better dtype here
in_grads = functional_ops.symbolic_gradient(
input=f_in,
Tout=[x if x != dtypes.resource else dtypes.float32 for x in f_types],
f=f)
return in_grads
def _SymGrad(op, out_grads):
"""Backprop through a function call node op given its outputs' gradients."""
f_in = [x for x in op.inputs] + out_grads
f_types = [x.dtype for x in op.inputs]
f = attr_value_pb2.NameAttrList()
f.name = op.type
for k in op.node_def.attr:
f.attr[k].CopyFrom(op.node_def.attr[k])
# TODO(apassos) use a better dtype here
in_grads = functional_ops.symbolic_gradient(
input=f_in,
Tout=[x if x != dtypes.resource else dtypes.float32 for x in f_types],
f=f)
return in_grads
def testSymGradShape(self):
g = tf.Graph()
with g.as_default():
x = tf.placeholder(tf.float32, [25, 4])
y = tf.placeholder(tf.float32, [200, 100])
dz = tf.placeholder(tf.float32, [1])
# We assume Foo is a function of (x, y) -> (z) Then, Foo's
# gradient function is (x, y, dz) -> (dx, dy). dx's shape
# should be the same as x's; and dy's shape should be the same
# as y's.
dx, dy = symbolic_gradient(input=[x, y, dz],
Tout=[tf.float32] * 2,
f="Foo")
self.assertEquals(x.get_shape(), dx.get_shape())
self.assertEquals(y.get_shape(), dy.get_shape())
def XSquarePlusOneGrad(x, dy):
dx = symbolic_gradient(input=[x, dy],
Tout=[tf.float32],
f="XSquarePlusOne",
name="dx")
return dx
def XSquarePlusOneGrad(x, dy):
dx = symbolic_gradient(input=[x, dy], Tout=[tf.float32], f="XSquarePlusOne", name="dx")
return dx
