Function keras_model

tensorflow/python/ops/summary_ops_v2.py:1076–1120

Writes a Keras model as JSON to as a Summary. Writing the Keras model configuration allows the TensorBoard graph plugin to render a conceptual graph, as opposed to graph of ops. In case the model fails to serialze as JSON, it ignores and returns False. Args: name: A name for this summa

(name, data, step=None)

def keras_model(name, data, step=None):
  """Writes a Keras model as JSON to as a Summary.

  Writing the Keras model configuration allows the TensorBoard graph plugin to
  render a conceptual graph, as opposed to graph of ops. In case the model fails
  to serialze as JSON, it ignores and returns False.

  Args:
    name: A name for this summary. The summary tag used for TensorBoard will be
      this name prefixed by any active name scopes.
    data: A Keras Model to write.
    step: Explicit `int64`-castable monotonic step value for this summary. If
      omitted, this defaults to `tf.summary.experimental.get_step()`, which must
      not be None.

  Returns:
    True on success, or False if no summary was written because no default
    summary writer was available.

  Raises:
    ValueError: if a default writer exists, but no step was provided and
      `tf.summary.experimental.get_step()` is None.
  """
  summary_metadata = summary_pb2.SummaryMetadata()
  # Hard coding a plugin name. Please refer to go/tb-plugin-name-hardcode for
  # the rationale.
  summary_metadata.plugin_data.plugin_name = "graph_keras_model"
  # version number = 1
  summary_metadata.plugin_data.content = b"1"

  try:
    json_string = data.to_json()
  except Exception as exc:  # pylint: disable=broad-except
    # An exception should not break a model code.
    logging.warn("Model failed to serialize as JSON. Ignoring... %s" % exc)
    return False

  with summary_scope(name, "graph_keras_model", [data, step]) as (tag, _):
    with ops.device("cpu:0"):
      tensor = constant_op.constant(json_string, dtype=dtypes.string)
    return write(
        tag=tag,
        tensor=tensor,
        step=step,
        metadata=summary_metadata)


_TraceContext = collections.namedtuple("TraceContext", ("graph", "profiler"))