(self, tag, textdata, step=None)
Saves a text summary.

    Args:
      tag: str: label for this data
      textdata: string, or 1D/2D list/numpy array of strings
      step: int: training step
    Note: markdown formatting is rendered by tensorboard.
Saves a text summary.
Saves a text summary .
def text(self, tag, textdata, step=None):
    """Saves a text summary.

    Args:
      tag: str: label for this data
      textdata: string, or 1D/2D list/numpy array of strings
      step: int: training step
    Note: markdown formatting is rendered by tensorboard.
    """
    if step is None:
      step = self._step
    else:
      self._step = step
    smd = tf.compat.v1.SummaryMetadata(
        plugin_data=tf.compat.v1.SummaryMetadata.PluginData(plugin_name='text'))
    if isinstance(textdata, (str, bytes)):
      tensor = tf.make_tensor_proto(
          values=[textdata.encode(encoding='utf_8')], shape=(1,))
    else:
      textdata = np.array(textdata)  # convert lists, jax arrays, etc.
      datashape = np.shape(textdata)
      if len(datashape) == 1:
        tensor = tf.make_tensor_proto(
            values=[td.encode(encoding='utf_8') for td in textdata],
            shape=(datashape[0],))
      elif len(datashape) == 2:
        tensor = tf.make_tensor_proto(
            values=[
                td.encode(encoding='utf_8') for td in np.reshape(textdata, -1)
            ],
            shape=(datashape[0], datashape[1]))
    summary = tf.compat.v1.Summary(
        value=[tf.compat.v1.Summary.Value(
            tag=tag, metadata=smd, tensor=tensor)])
    self.add_summary(summary, step)
def text ( self , tag , textdata , step = None ) : if step is None : step = self . _step else : self . _step = step smd = tf . compat . v1 . SummaryMetadata ( plugin_data = tf . compat . v1 . SummaryMetadata . PluginData ( plugin_name = 'text' ) ) if isinstance ( textdata , ( str , bytes ) ) : tensor = tf . make_tensor_proto ( values = [ textdata . encode ( encoding = 'utf_8' ) ] , shape = ( 1 , ) ) else : textdata = np . array ( textdata ) # convert lists, jax arrays, etc. datashape = np . shape ( textdata ) if len ( datashape ) == 1 : tensor = tf . make_tensor_proto ( values = [ td . encode ( encoding = 'utf_8' ) for td in textdata ] , shape = ( datashape [ 0 ] , ) ) elif len ( datashape ) == 2 : tensor = tf . make_tensor_proto ( values = [ td . encode ( encoding = 'utf_8' ) for td in np . reshape ( textdata , - 1 ) ] , shape = ( datashape [ 0 ] , datashape [ 1 ] ) ) summary = tf . compat . v1 . Summary ( value = [ tf . compat . v1 . Summary . Value ( tag = tag , metadata = smd , tensor = tensor ) ] ) self . add_summary ( summary , step )