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"""Connects all float and double tensors to CheckNumericsOp."""
from tensorflow.python.framework import ops
from tensorflow.python.framework import types
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
def verify_tensor_all_finite(t, msg, name=None):
"""Assert that the tensor does not contain any NaN's or Inf's.
Args:
t: Tensor to check.
msg: Message to log on failure.
name: A name for this operation (optional).
Returns:
Same tensor as `t`.
"""
with ops.op_scope([t], name, "VerifyFinite") as name:
t = ops.convert_to_tensor(t, name="t")
with ops.device(t.device or t.graph.get_default_device()):
verify_input = array_ops.check_numerics(t, message=msg)
out = control_flow_ops.with_dependencies([verify_input], t)
return out
def add_check_numerics_ops():
"""Connect a check_numerics to every floating point tensor.
`check_numerics` operations themselves are added for each `float` or `double`
tensor in the graph. For all ops in the graph, the `check_numerics` op for
all of its (`float` or `double`) inputs is guaranteed to run before the
`check_numerics` op on any of its outputs.
Returns:
A `group` op depending on all `check_numerics` ops added.
"""
check_op = []
# This code relies on the ordering of ops in get_operations().
# The consumer of a tensor always comes before that tensor's producer in
# this list. This is true because get_operations() returns ops in the order
# added, and ops can only be added once its inputs are added.
for op in ops.get_default_graph().get_operations():
for output in op.outputs:
if output.dtype in [types.float32, types.float64]:
message = op.name + ":" + str(output.value_index)
with ops.control_dependencies(check_op):
check_op = [array_ops.check_numerics(output, message=message)]
return control_flow_ops.group(*check_op)
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