Cannot replace LSTMBlockCell with LSTMBlockFusedCell in Python TensorFlow

Question

Replacing with calls in static_rnn '. I am using TensorFlow 1.2.0-rc1 compiled from source. LSTMBlockCell LSTMBlockFusedCell LSTMBlockFusedCell

Full error message:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-2986e054cb6b> in <module>()
     19     enc_cell = tf.contrib.rnn.LSTMBlockFusedCell(rnn_size)
     20     enc_layers = tf.contrib.rnn.MultiRNNCell([enc_cell] * num_layers, state_is_tuple=True)
---> 21     _, enc_state = tf.contrib.rnn.static_rnn(enc_layers, enc_input_unstacked, dtype=dtype)
     22 
     23 with tf.variable_scope('decoder'):

~/Virtualenvs/scikit/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py in static_rnn(cell, inputs, initial_state, dtype, sequence_length, scope)
   1139 
   1140   if not _like_rnncell(cell):
-> 1141     raise TypeError("cell must be an instance of RNNCell")
   1142   if not nest.is_sequence(inputs):
   1143     raise TypeError("inputs must be a sequence")

TypeError: cell must be an instance of RNNCell

Code to play:

import tensorflow as tf

batch_size = 8
enc_input_length = 1000

dtype = tf.float32
rnn_size = 8
num_layers = 2

enc_input = tf.placeholder(dtype, shape=[batch_size, enc_input_length, 1])
enc_input_unstacked = tf.unstack(enc_input, axis=1)

with tf.variable_scope('encoder'):
    enc_cell = tf.contrib.rnn.LSTMBlockFusedCell(rnn_size)
    enc_layers = tf.contrib.rnn.MultiRNNCell([enc_cell] * num_layers)
    _, enc_state = tf.contrib.rnn.static_rnn(enc_layers, enc_input_unstacked, dtype=dtype)

_like_rnncell looks like that:

def _like_rnncell(cell):
  """Checks that a given object is an RNNCell by using duck typing."""
  conditions = [hasattr(cell, "output_size"), hasattr(cell, "state_size"),
                hasattr(cell, "zero_state"), callable(cell)]
  return all(conditions)

It turns out that LSTMBlockFusedCellit has no properties output_sizeand state_sizewhich it implements LSTMBlockCell.

This is a mistake, or is there a way to use LSTMBlockFusedCellthat I am missing.

Answer 1

LSTMBlockFusedCell FusedRNNCell RNNCell, tf.nn.static_rnn tf.nn.dynamic_rnn, RNNCell ( ).

, .

inputs = tf.placeholder(tf.float32, [time_len, batch_size, input_size])
fused_rnn_cell = tf.contrib.rnn.LSTMBlockFusedCell(num_units)

outputs, state = fused_rnn_cell(inputs, dtype=tf.float32)

# outputs shape is (time_len, batch_size, num_units)
# state: LSTMStateTuple where c shape is (batch_size, num_units)
#  and h shape is also (batch_size, num_units).

LSTMBlockFusedCell gen_lstm_ops.block_lstm , LSTM.

, FusedRNNCell , , .