def graph(param, step=None, name=None):
"""Writes a TensorFlow graph to the summary interface.
The graph summary is, strictly speaking, not a summary. Conditions
like @{tf.contrib.summary.never_record_summaries} do not apply. Only
a single graph can be associated with a particular run. If multiple
graphs are written, then only the last one will be considered by
TensorBoard.
When not using eager execution mode, the user should consider passing
the `graph` parameter to @{tf.contrib.summary.initialize} instead of
calling this function. Otherwise special care needs to be taken when
using the graph to record the graph.
Args:
param: A @{tf.Tensor} containing a serialized graph proto. When
eager execution is enabled, this function will automatically
coerce @{tf.Graph}, @{tf.GraphDef}, and string types.
step: The global step variable. This doesn't have useful semantics
for graph summaries, but is used anyway, due to the structure of
event log files. This defaults to the global step.
name: A name for the operation (optional).
Returns:
The created @{tf.Operation} or a @{tf.no_op} if summary writing has
not been enabled for this context.
Raises:
TypeError: If `param` isn't already a @{tf.Tensor} in graph mode.
"""
if not context.in_eager_mode() and not isinstance(param, ops.Tensor):
raise TypeError(
"graph() needs a tf.Tensor (e.g. tf.placeholder) in graph "
"mode, but was: %s" % type(param))
writer = context.context().summary_writer_resource
if writer is None:
return control_flow_ops.no_op()
with ops.device("cpu:0"):
if step is None:
step = training_util.get_global_step()
else:
step = ops.convert_to_tensor(step, dtypes.int64)
if isinstance(param, (ops.Graph, graph_pb2.GraphDef)):
tensor = ops.convert_to_tensor(_serialize_graph(param),
dtypes.string)
else:
tensor = array_ops.identity(param)
return gen_summary_ops.write_graph_summary(writer,
step,
tensor,
name=name)
def graph(param, step=None, name=None):
"""Writes a TensorFlow graph to the summary interface.
The graph summary is, strictly speaking, not a summary. Conditions
like @{tf.contrib.summary.never_record_summaries} do not apply. Only
a single graph can be associated with a particular run. If multiple
graphs are written, then only the last one will be considered by
TensorBoard.
When not using eager execution mode, the user should consider passing
the `graph` parameter to @{tf.contrib.summary.initialize} instead of
calling this function. Otherwise special care needs to be taken when
using the graph to record the graph.
Args:
param: A @{tf.Tensor} containing a serialized graph proto. When
eager execution is enabled, this function will automatically
coerce @{tf.Graph}, @{tf.GraphDef}, and string types.
step: The global step variable. This doesn't have useful semantics
for graph summaries, but is used anyway, due to the structure of
event log files. This defaults to the global step.
name: A name for the operation (optional).
Returns:
The created @{tf.Operation} or a @{tf.no_op} if summary writing has
not been enabled for this context.
Raises:
TypeError: If `param` isn't already a @{tf.Tensor} in graph mode.
"""
if not context.in_eager_mode() and not isinstance(param, ops.Tensor):
raise TypeError("graph() needs a tf.Tensor (e.g. tf.placeholder) in graph "
"mode, but was: %s" % type(param))
writer = context.context().summary_writer_resource
if writer is None:
return control_flow_ops.no_op()
with ops.device("cpu:0"):
if step is None:
step = training_util.get_global_step()
else:
step = ops.convert_to_tensor(step, dtypes.int64)
if isinstance(param, (ops.Graph, graph_pb2.GraphDef)):
tensor = ops.convert_to_tensor(_serialize_graph(param), dtypes.string)
else:
tensor = array_ops.identity(param)
return gen_summary_ops.write_graph_summary(writer, step, tensor, name=name)