There is no "name" variable in the constructor of BasicLSTMCell

Question

In order to differentiate LSTMs, I wish to give a name to the BasicLSTMCell variable in my code. But it reported the following error:

    num_units=self.config.num_lstm_units, state_is_tuple=True, name="some_basic_lstm")
TypeError: __init__() got an unexpected keyword argument 'name'

And I found in the library of my tensorflow installation. Int the file rnn_cell_impl.py:

class BasicLSTMCell(RNNCell):
  """Basic LSTM recurrent network cell.

  The implementation is based on: http://arxiv.org/abs/1409.2329.

  We add forget_bias (default: 1) to the biases of the forget gate in order to
  reduce the scale of forgetting in the beginning of the training.

  It does not allow cell clipping, a projection layer, and does not
  use peep-hole connections: it is the basic baseline.

  For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
  that follows.
  """

  def __init__(self, num_units, forget_bias=1.0,
               state_is_tuple=True, activation=None, reuse=None):
    """Initialize the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
        Must set to `0.0` manually when restoring from CudnnLSTM-trained
        checkpoints.
      state_is_tuple: If True, accepted and returned states are 2-tuples of
        the `c_state` and `m_state`.  If False, they are concatenated
        along the column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.  Default: `tanh`.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.

Is it a bug in my version of tensorflow? How can I give it a "name"?

Answer 1

I think @aswinids provided the best answer here in comments, but let me explain why it is should not be considered a bug. An LSTM cell is comprised of at least 4 variables (there are a few others used for control flow and such). There are 4 sub-network operations that occur in an LSTM. The diagram below from Colah's blog illustrates the internals of an LSTM cell (http://colah.github.io/posts/2015-08-Understanding-LSTMs/):

Each of the yellow boxes has a set of weights assigned to it and is effectively a single layer neural network operation (piped together in an interesting way, defined by the LSTM architecture).

A good approach to naming these would then be tf.variable_scope('some_name') such that all 4 of the variables defined in the LSTM have a common base naming structure such as:

lstm_cell/f_t
lstm_cell/i_t
lstm_cell/C_t
lstm_cell/o_t

I suspect that previously they just did this and hard coded lstm_cell or whatever name they used as the prefix for all the variables under the LSMT cell. In the later versions as @ashwinids points out, there is a name variable and I suspect that just replaced lstm_cell I used in the example here.