Function batch_set_value

tensorflow/python/keras/backend.py:3224–3259

Sets the values of many tensor variables at once. Arguments: tuples: a list of tuples `(tensor, value)`. `value` should be a Numpy array.

(tuples)

@keras_export('keras.backend.batch_set_value')
def batch_set_value(tuples):
  """Sets the values of many tensor variables at once.

  Arguments:
      tuples: a list of tuples `(tensor, value)`.
          `value` should be a Numpy array.
  """
  if ops.executing_eagerly_outside_functions():
    with ops.init_scope():
      for x, value in tuples:
        x.assign(np.asarray(value, dtype=dtype(x)))
  else:
    with get_graph().as_default():
      if tuples:
        assign_ops = []
        feed_dict = {}
        for x, value in tuples:
          value = np.asarray(value, dtype=dtype(x))
          tf_dtype = dtypes_module.as_dtype(x.dtype.name.split('_')[0])
          if hasattr(x, '_assign_placeholder'):
            assign_placeholder = x._assign_placeholder
            assign_op = x._assign_op
          else:
            # In order to support assigning weights to resizable variables in
            # Keras, we make a placeholder with the correct number of dimensions
            # but with None in each dimension. This way, we can assign weights
            # of any size (as long as they have the correct dimensionality).
            placeholder_shape = tensor_shape.TensorShape([None] * value.ndim)
            assign_placeholder = array_ops.placeholder(
                tf_dtype, shape=placeholder_shape)
            assign_op = x.assign(assign_placeholder)
            x._assign_placeholder = assign_placeholder
            x._assign_op = assign_op
          assign_ops.append(assign_op)
          feed_dict[assign_placeholder] = value
        get_session().run(assign_ops, feed_dict=feed_dict)


@keras_export('keras.backend.print_tensor')