Python tensorflow.python.summary.summary.FileWriter() Examples

def verify_scalar_summary_is_written(self, print_summary):
    value = 3
    tensor = array_ops.ones([]) * value
    name = 'my_score'
    prefix = 'eval'
    summaries.add_scalar_summary(tensor, name, prefix, print_summary)

    output_dir = tempfile.mkdtemp('scalar_summary_no_print_test')
    summary_op = summary.merge_all()

    summary_writer = summary.FileWriter(output_dir)
    with self.cached_session() as sess:
      new_summary = sess.run(summary_op)
      summary_writer.add_summary(new_summary, 1)
      summary_writer.flush()

    self.assert_scalar_summary(output_dir, {
        '%s/%s' % (prefix, name): value
    }) 

def create_tfevent_from_pb(model,optimized=False):
    print("> creating tfevent of model: {}".format(model))

    if optimized:
        model_path=ROOT_DIR+'/models/{}/optimized_inference_graph.pb'.format(model)
        log_dir=ROOT_DIR+'/models/{}/log_opt/'.format(model)
    else:
        model_path=ROOT_DIR+'/models/{}/frozen_inference_graph.pb'.format(model)
        log_dir=ROOT_DIR+'/models/{}/log/'.format(model)

    with session.Session(graph=ops.Graph()) as sess:
        with gfile.FastGFile(model_path, "rb") as f:
          graph_def = graph_pb2.GraphDef()
          graph_def.ParseFromString(f.read())
          importer.import_graph_def(graph_def)
        pb_visual_writer = summary.FileWriter(log_dir)
        pb_visual_writer.add_graph(sess.graph)
    print("> Model {} Imported. \nVisualize by running: \
    tensorboard --logdir={}".format(model_path, log_dir))

# Gather all Model Names in models/ 

def import_to_tensorboard(saved_model, output_dir):
  """View an imported saved_model.pb as a graph in Tensorboard.

  Args:
    saved_model: The location of the saved_model.pb to visualize.
    output_dir: The location for the Tensorboard log to begin visualization from.

  Usage:
    Call this function with your model location and desired log directory.
    Launch Tensorboard by pointing it to the log directory.
    View your imported `.pb` model as a graph.
  """
  with open(saved_model, "rb") as f:
    sm = saved_model_pb2.SavedModel()
    sm.ParseFromString(f.read())
    if 1 != len(sm.meta_graphs):
      print('More than one graph found. Not sure which to write')
      sys.exit(1)
    graph_def = sm.meta_graphs[0].graph_def

    pb_visual_writer = summary.FileWriter(output_dir)
    pb_visual_writer.add_graph(None, graph_def=graph_def)
    print("Model Imported. Visualize by running: "
          "tensorboard --logdir={}".format(output_dir)) 

def import_to_tensorboard(saved_model, output_dir):
  """View an imported saved_model.pb as a graph in Tensorboard.

  Args:
    saved_model: The location of the saved_model.pb to visualize.
    output_dir: The location for the Tensorboard log to begin visualization from.

  Usage:
    Call this function with your model location and desired log directory.
    Launch Tensorboard by pointing it to the log directory.
    View your imported `.pb` model as a graph.
  """
  with open(saved_model, "rb") as f:
    sm = saved_model_pb2.SavedModel()
    sm.ParseFromString(f.read())
    if 1 != len(sm.meta_graphs):
      print('More than one graph found. Not sure which to write')
      sys.exit(1)
    graph_def = sm.meta_graphs[0].graph_def

    pb_visual_writer = summary.FileWriter(output_dir)
    pb_visual_writer.add_graph(None, graph_def=graph_def)
    print("Model Imported. Visualize by running: "
          "tensorboard --logdir={}".format(output_dir)) 

def dump_graph_into_tensorboard(tf_graph):
    # type: (_tf.Graph) -> None
    _tb_log_dir = os.environ.get('TB_LOG_DIR')
    if _tb_log_dir:
        if is_tf2:
            from tensorflow.python.ops.summary_ops_v2 import graph as write_graph
            pb_visual_writer = _tf.summary.create_file_writer(_tb_log_dir)
            with pb_visual_writer.as_default():
                write_graph(tf_graph)
        else:
            from tensorflow.python.summary import summary
            pb_visual_writer = summary.FileWriter(_tb_log_dir)
            pb_visual_writer.add_graph(tf_graph) 

def set_model(self, model):
    self.model = model
    self.sess = K.get_session()
    if self.histogram_freq and self.merged is None:
      for layer in self.model.layers:
        for weight in layer.weights:
          mapped_weight_name = weight.name.replace(':', '_')
          tf_summary.histogram(mapped_weight_name, weight)
          if self.write_grads:
            grads = model.optimizer.get_gradients(model.total_loss, weight)

            def is_indexed_slices(grad):
              return type(grad).__name__ == 'IndexedSlices'

            grads = [grad.values if is_indexed_slices(grad) else grad
                     for grad in grads]
            tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads)
          if self.write_images:
            w_img = array_ops.squeeze(weight)
            shape = K.int_shape(w_img)
            if len(shape) == 2:  # dense layer kernel case
              if shape[0] > shape[1]:
                w_img = array_ops.transpose(w_img)
                shape = K.int_shape(w_img)
              w_img = array_ops.reshape(w_img, [1, shape[0], shape[1], 1])
            elif len(shape) == 3:  # convnet case
              if K.image_data_format() == 'channels_last':
                # switch to channels_first to display
                # every kernel as a separate image
                w_img = array_ops.transpose(w_img, perm=[2, 0, 1])
                shape = K.int_shape(w_img)
              w_img = array_ops.reshape(w_img,
                                        [shape[0], shape[1], shape[2], 1])
            elif len(shape) == 1:  # bias case
              w_img = array_ops.reshape(w_img, [1, shape[0], 1, 1])
            else:
              # not possible to handle 3D convnets etc.
              continue

            shape = K.int_shape(w_img)
            assert len(shape) == 4 and shape[-1] in [1, 3, 4]
            tf_summary.image(mapped_weight_name, w_img)

        if hasattr(layer, 'output'):
          tf_summary.histogram('{}_out'.format(layer.name), layer.output)
    self.merged = tf_summary.merge_all()

    if self.write_graph:
      self.writer = tf_summary.FileWriter(self.log_dir, self.sess.graph)
    else:
      self.writer = tf_summary.FileWriter(self.log_dir)