Fix Inheriting RNNCell build() ValueError: Initializer for variable is from inside a control-flow construct – TensorFlow Tutorial

By | March 31, 2022

We can inherit tensorflow RNNCell to create a custom RNN and create some variables in build() function. However, we may get this error: ValueError: Initializer for variable is from inside a control-flow construct, such as a loop or conditional. When creating a variable inside a loop or conditional, use a lambda as the initializer.

In this tutorial, we will introduce you how to fix it.

For example:

class CustomCell(tf.nn.rnn_cell.RNNCell):
	def __init__(self, num_units, reuse = None, dtype = tf.float32, name = "custom_cell"):
		super(CustomCell, self).__init__(_reuse=reuse, dtype = dtype, name=name)

		self._num_units = num_units # the dimension of rnn cell

	@property
	def state_size(self):
		return self._num_units

	@property
	def output_size(self):
		return self._num_units

	def build(self, inputs_shape):
		inputs_dim = inputs_shape[-1].value
		self._w = tf.Variable(tf.truncated_normal([inputs_dim, self._num_units], stddev=0.1), name="weight")
		self._b = tf.Variable(tf.truncated_normal([self._num_units], stddev=0.1), name="bias")
		self.built = True

	def call(self, inputs, state):

		# call body
		# how to use previous rnn cell output and state to generate new output and hidden
		new_h = tf.tanh(tf.matmul(inputs+state, self._w) + self._b)
		new_c = tf.nn.swish(tf.matmul(state, self._w) + self._b)
		return new_h, new_c

If you use this CustomCell class, you will get this error:

Fix Inheriting RNNCell build() ValueError - Initializer for variable is from inside a control-flow construct - TensorFlow Tutorial

How to fix this ValueError?

We can not create tensorflow variables in build() function using tf.Variable(). We should use self.add_variable().

For example:

class CustomCell(tf.nn.rnn_cell.RNNCell):
	def __init__(self, num_units, reuse = None, dtype = tf.float32, name = "custom_cell"):
		super(CustomCell, self).__init__(_reuse=reuse, dtype = dtype, name=name)

		self._num_units = num_units # the dimension of rnn cell

	@property
	def state_size(self):
		return self._num_units

	@property
	def output_size(self):
		return self._num_units

	def build(self, inputs_shape):
		inputs_dim = inputs_shape[-1].value
		self._w = self.add_variable(name="weight", shape = [inputs_dim, self._num_units], initializer = tf.glorot_normal_initializer(), dtype = tf.float32)
		self._b = self.add_variable(name="bias", shape=[self._num_units], initializer=tf.glorot_normal_initializer(), dtype=tf.float32)

		self.built = True

	def call(self, inputs, state):

		# call body
		# how to use previous rnn cell output and state to generate new output and hidden
		new_h = tf.tanh(tf.matmul(inputs+state, self._w) + self._b)
		new_c = tf.nn.swish(tf.matmul(state, self._w) + self._b)
		return new_h, new_c

Then we can use code below to evaluate it.

size = 100
inputs = tf.Variable(tf.truncated_normal([3, 20, 100], stddev=0.1), name="inputs")
input_lengths = tf.Variable(tf.truncated_normal([3, 20], stddev=0.1), name="inputs_length")
_fw_cell =CustomCell(size, name='encoder_fw_')
_bw_cell =CustomCell(size, name='encoder_bw')
with tf.variable_scope("Custom_BiLSTM"):
    outputs, (fw_state, bw_state) = tf.nn.bidirectional_dynamic_rnn(
		_fw_cell,
		_bw_cell,
		inputs,
		sequence_length=None,
		dtype=tf.float32,
		swap_memory=True)

    outputs = tf.concat(outputs, axis=2)  # Concat and return forward + backward outputs

init = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
with tf.Session() as sess:
    sess.run([init, init_local])
    np.set_printoptions(precision=4, suppress=True)
    f =sess.run([inputs, outputs])

Run this code, we will get:

f shape= (3, 20, 100) (3, 20, 200)
[array([[[ 0.1409,  0.107 ,  0.0258, ...,  0.0281, -0.0612,  0.0525],
        [ 0.0652, -0.0202,  0.0169, ..., -0.1956,  0.0543, -0.0334],
        [-0.0559,  0.1613,  0.0257, ...,  0.0858,  0.1105, -0.0963],
        ...,
        [-0.0716, -0.0563, -0.0451, ...,  0.1238, -0.0111, -0.0465],
        [-0.0484,  0.0344, -0.0566, ..., -0.1707, -0.0705,  0.01  ],
        [-0.0037, -0.0209, -0.0565, ...,  0.0233,  0.0548,  0.1174]]],
      dtype=float32), array([[[-0.0311, -0.246 , -0.0412, ..., -0.0077, -0.0249,  0.0986],
        [-0.1012, -0.1396, -0.0219, ..., -0.0893,  0.128 ,  0.1197],
        [ 0.0678, -0.2054, -0.0608, ..., -0.0068, -0.0627,  0.1589],
        ...,
        [ 0.0128, -0.1721,  0.0594, ...,  0.1566,  0.1048, -0.104 ],
        [-0.0187, -0.2965,  0.0667, ..., -0.0013,  0.0132,  0.046 ],
        [ 0.0082, -0.1131,  0.0417, ...,  0.068 ,  0.2191,  0.0546]]],
      dtype=float32)]

This value error is fixed.

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