def build(self, input_shape):
default_caching_device = rnn_utils.caching_device(self)
self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
caching_device=default_caching_device)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint,
caching_device=default_caching_device)
if self.use_bias:
self.bias = self.add_weight(shape=(self.units, ),
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
caching_device=default_caching_device)
else:
self.bias = None
self.built = True
def build(self, input_shape):
input_dim = input_shape[-1]
default_caching_device = rnn_utils.caching_device(self)
self.kernel = self.add_weight(shape=(input_dim, self.units * 3),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
caching_device=default_caching_device)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 3),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint,
caching_device=default_caching_device)
if self.use_bias:
if not self.reset_after:
bias_shape = (3 * self.units, )
else:
# separate biases for input and recurrent kernels
# Note: the shape is intentionally different from CuDNNGRU biases
# `(2 * 3 * self.units,)`, so that we can distinguish the classes
# when loading and converting saved weights.
bias_shape = (2, 3 * self.units)
self.bias = self.add_weight(shape=bias_shape,
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
caching_device=default_caching_device)
else:
self.bias = None
self.built = True
def build(self, input_shape):
default_caching_device = rnn_utils.caching_device(self)
input_dim = input_shape[-1]
self.kernel = self.add_weight(shape=(input_dim, self.units * 4),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
caching_device=default_caching_device)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 4),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint,
caching_device=default_caching_device)
if self.use_bias:
if self.unit_forget_bias:
def bias_initializer(_, *args, **kwargs):
return backend.concatenate([
self.bias_initializer((self.units, ), *args, **kwargs),
initializers.get('ones')((self.units, ), *args,
**kwargs),
self.bias_initializer((self.units * 2, ), *args,
**kwargs),
])
else:
bias_initializer = self.bias_initializer
self.bias = self.add_weight(shape=(self.units * 4, ),
name='bias',
initializer=bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
caching_device=default_caching_device)
else:
self.bias = None
self.built = True