def __init__(self,
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)
self._config = config
self._access = MemoryAccess(memory_size=self._config.memory_size, word_size=self._config.word_size,\
num_reads=self._config.num_reads, num_writes=self._config.num_writes)
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)
def zero_state(config, batch_size, dtype=np.float32):
return DNCState(
access_output=np.zeros([batch_size, config.num_reads, config.word_size], dtype=dtype),
access_state=MemoryAccess.zero_state(config, batch_size, dtype))
def state_placeholder(config, dtype=tf.float32):
return DNCState(
access_output=tf.placeholder(dtype, shape=(None, config.num_reads, config.word_size)),
access_state=MemoryAccess.state_placeholder(config, dtype),
controller_state=tf.nn.rnn_cell.LSTMStateTuple(tf.placeholder(dtype, shape=(None, config.controller_h_size)), \
tf.placeholder(dtype, shape=(None, config.controller_h_size))))
def state_placeholder(config, dtype=tf.float32):
return DNCState(
access_output=tf.placeholder(dtype, shape=(None, config.num_reads, config.word_size)),
access_state=MemoryAccess.state_placeholder(config, dtype))
def zero_state(config, batch_size, dtype=np.float32):
return DNCState(
access_output=np.zeros([batch_size, config.num_reads, config.word_size], dtype=dtype),
access_state=MemoryAccess.zero_state(config, batch_size, dtype),
controller_state=tf.nn.rnn_cell.LSTMStateTuple(np.zeros([batch_size, config.controller_h_size], dtype=dtype), \
np.zeros([batch_size, config.controller_h_size], dtype=dtype)))