def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initialier=None,
bias_initializer=None,
name=None,
dtype=None,
**kwargs):
super(GRUCell, self).__init__(_reuse=reuse,
name=name,
dtype=dtype,
**kwargs)
_check_supported_dtypes(self.dtype)
if context.executing_eagerly() and context.num_gpus() > 0:
logging.warn("This is not optimized for performance.")
self.input_spec = input_spec.InputSpec(ndim=2)
self._num_units = num_units
if activation:
self._activation = activations.get(activation)
else:
self._activation = math_ops.tanh
self._kernel_initializer = initializers.get(kernel_initialier)
self._bias_initializer = initializers.get(bias_initializer)
def build(self, inputs_shape):
if inputs_shape[-1] is None:
raise ValueError(
"Expected inputs.shape[-1] to be known, saw shape: %s" %
str(inputs_shape))
_check_supported_dtypes(self.dtype)
input_depth = inputs_shape[-1] - 2
self._gate_kernel = self.add_variable(
"gates/%s" % _WEIGHTS_VARIABLE_NAME,
shape=[input_depth + self._num_units, 2 * self._num_units],
initializer=self._kernel_initializer)
self._gate_bias = self.add_variable(
"gates/%s" % _BIAS_VARIABLE_NAME,
shape=[2 * self._num_units],
initializer=(self._bias_initializer
if self._bias_initializer is not None else
init_ops.constant_initializer(1.0, dtype=self.dtype)))
self._candidate_kernel = self.add_variable(
"candidate/%s" % _WEIGHTS_VARIABLE_NAME,
shape=[input_depth + self._num_units, self._num_units],
initializer=self._kernel_initializer)
self._candidate_bias = self.add_variable(
"candidate/%s" % _BIAS_VARIABLE_NAME,
shape=[self._num_units],
initializer=(self._bias_initializer if self._bias_initializer
is not None else init_ops.zeros_initializer(
dtype=self.dtype)))
def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None,
name=None,
dtype=None,
**kwargs):
super(ContinuousLSTMCell, self).__init__(_reuse=reuse,
name=name,
dtype=dtype,
**kwargs)
_check_supported_dtypes(self.dtype)
if context.executing_eagerly() and context.num_gpus() > 0:
logging.warn(
"%s: Note that this cell is not optimized for performance. "
"Please use tf.contrib.cudnn_rnn.CudnnGRU for better "
"performance on GPU.", self)
# Inputs must be 2-dimensional.
self.input_spec = input_spec.InputSpec(ndim=2)
self._num_units = num_units
if activation:
self._activation = activations.get(activation)
else:
self._activation = math_ops.tanh
self._kernel_initializer = initializers.get(kernel_initializer)
self._bias_initializer = initializers.get(bias_initializer)