Function write_op_log

tensorflow/python/profiler/tfprof_logger.py:192–218

Log provided 'op_log', and add additional model information below. The API also assigns ops in tf.compat.v1.trainable_variables() an op type called '_trainable_variables'. The API also logs 'flops' statistics for ops with op.RegisterStatistics() defined. flops calculation depends on

(graph, log_dir, op_log=None, run_meta=None, add_trace=True)

@tf_export(v1=['profiler.write_op_log'])
def write_op_log(graph, log_dir, op_log=None, run_meta=None, add_trace=True):
  """Log provided 'op_log', and add additional model information below.

    The API also assigns ops in tf.compat.v1.trainable_variables() an op type
    called '_trainable_variables'.
    The API also logs 'flops' statistics for ops with op.RegisterStatistics()
    defined. flops calculation depends on Tensor shapes defined in 'graph',
    which might not be complete. 'run_meta', if provided, completes the shape
    information with best effort.

  Args:
    graph: tf.Graph. If None and eager execution is not enabled, use
        default graph.
    log_dir: directory to write the log file.
    op_log: (Optional) OpLogProto proto to be written. If not provided, an new
        one is created.
    run_meta: (Optional) RunMetadata proto that helps flops computation using
        run time shape information.
    add_trace: Whether to add python code trace information.
        Used to support "code" view.
  """
  if not graph and not context.executing_eagerly():
    graph = ops.get_default_graph()
  op_log = merge_default_with_oplog(graph, op_log, run_meta, add_trace)

  with gfile.Open(os.path.join(log_dir, 'tfprof_log'), 'w') as log:
    log.write(op_log.SerializeToString())