def __init__(self,
access_config,
controller_config,
output_size,
clip_value=None,
name='dnc'):
"""Initializes the DNC core.
Args:
access_config: dictionary of access module configurations.
controller_config: dictionary of controller (LSTM) module configurations.
output_size: output dimension size of core.
clip_value: clips controller and core output values to between
`[-clip_value, clip_value]` if specified.
name: module name (default 'dnc').
Raises:
TypeError: if direct_input_size is not None for any access module other
than KeyValueMemory.
"""
super(DNC, self).__init__(name=name)
with self._enter_variable_scope():
self._controller = snt.LSTM(**controller_config)
self._access = access.MemoryAccess(**access_config)
self._access_output_size = np.prod(self._access.output_size.as_list())
self._output_size = output_size
self._clip_value = clip_value or 0
self._output_size = tf.TensorShape([output_size])
self._state_size = DNCState(
access_output=self._access_output_size,
access_state=self._access.state_size,
controller_state=self._controller.state_size)
def __init__(self,
access_config,
controller_config,
output_size,
clip_value=None,
dropout=0.0,
mode=None,
batch_size=None,
name='dnc'):
"""Initializes the DNC core.
Args:
access_config: dictionary of access module configurations.
controller_config: dictionary of controller (LSTM) module configurations.
output_size: output dimension size of core.
clip_value: clips controller and core output values to between
`[-clip_value, clip_value]` if specified.
name: module name (default 'dnc').
Raises:
TypeError: if direct_input_size is not None for any access module other
than KeyValueMemory.
"""
super(DNC, self).__init__(name=name)
self.dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0
self.access_config = access_config
with self._enter_variable_scope():
def single_cell(num_units):
cell = tf.contrib.rnn.BasicLSTMCell(num_units, forget_bias=1.0)
if self.dropout > 0.0:
cell = tf.contrib.rnn.DropoutWrapper(
cell=cell, input_keep_prob=(1.0 - self.dropout))
return cell
self._controller = tf.contrib.rnn.MultiRNNCell([
single_cell(controller_config['num_units'])
for _ in range(controller_config['num_layers'])
])
self._access = access.MemoryAccess(**access_config)
self._access_output_size = np.prod(self._access.output_size.as_list())
self._output_size = output_size
self._clip_value = clip_value or 0
self._output_size = tf.TensorShape([output_size])
# self._state_size = DNCState(
# access_output=self._access_output_size,
# access_state=self._access.state_size,
# controller_state=self._controller.state_size)
self.batch_size = batch_size
self._state_size = DNCState(
access_output=tf.TensorShape((self.access_config['word_size'])),
access_state=access.AccessState(
memory=tf.TensorShape((self.access_config['memory_size'] *
self.access_config['word_size'])),
read_weights=tf.TensorShape(
(self.access_config['memory_size'])),
write_weights=tf.TensorShape(
(self.access_config['memory_size'])),
linkage=TemporalLinkageState(
link=tf.TensorShape((self.access_config['memory_size'] *
self.access_config['memory_size'])),
precedence_weights=tf.TensorShape(
(self.access_config['memory_size']))),
usage=self._access.state_size.usage),
controller_state=self._controller.state_size)
def setUp(self):
self.module = access.MemoryAccess(MEMORY_SIZE, WORD_SIZE, NUM_READS,
NUM_WRITES)
self.initial_state = self.module.initial_state(BATCH_SIZE)