def __init__(self,
rank,
filters,
kernel_size,
groups=1,
strides=1,
padding='VALID',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super().__init__(activity_regularizer=activity_regularizer, **kwargs)
if filters % groups != 0:
raise ValueError(
"Groups must divide filters evenly, but got {}/{}".format(
filters, groups))
self.filters = filters
self.groups = groups
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank,
'kernel_size')
self.data_format = data_format
self.padding = padding
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.dilation_rate = conv_utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
def __init__(self, filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1, 1),
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(SeparableConv3D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.pointwise_initializer = initializers.get(pointwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.pointwise_constraint = constraints.get(pointwise_constraint)
def __init__(self,
units,
concat=False,
use_bias=True,
agg_method='mean',
activation=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super().__init__(**kwargs)
self.units = units
self.concat = concat
self.use_bias = use_bias
self.agg_method = agg_method
self.aggregator = {
'mean': tf.reduce_mean,
'sum': tf.reduce_sum,
'max': tf.reduce_max,
'min': tf.reduce_min
}[agg_method]
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
if concat:
self.output_dim = units * 2
else:
self.output_dim = units
def __init__(self,
units,
activation='tanh',
recurrent_activation='sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
use_ln=False,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
implementation=1,
**kwargs):
super().__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.use_ln = use_ln
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.state_size = LSTMState(h=self.units, c=self.units)
self.output_size = self.units
def __init__(self,
units,
mode='parallel',
return_aweights=False,
scaling_factor=None,
noise_std=0,
weights_initializer='he_normal',
bias_initializer='zeros',
weights_regularizer=None,
bias_regularizer=None,
weights_constraint=None,
bias_constraint=None,
**kwargs):
if 'name' not in kwargs:
kwargs['name'] = ""
super(MonotonicBahdanauAttention, self).__init__(**kwargs)
self.units = units
self.mode = mode
self.return_aweights = return_aweights
self.scaling_factor = scaling_factor
self.noise_std = noise_std
self.weights_initializer = initializers.get(weights_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.weights_regularizer = regularizers.get(weights_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.weights_constraint = constraints.get(weights_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self._wa = layers.Dense(self.units, use_bias=False,\
kernel_initializer=weights_initializer, bias_initializer=bias_initializer,\
kernel_regularizer=weights_regularizer, bias_regularizer=bias_regularizer,\
kernel_constraint=weights_constraint, bias_constraint=bias_constraint,\
name=self.name+"Wa")
self._ua = layers.Dense(self.units,\
kernel_initializer=weights_initializer, bias_initializer=bias_initializer,\
kernel_regularizer=weights_regularizer, bias_regularizer=bias_regularizer,\
kernel_constraint=weights_constraint, bias_constraint=bias_constraint,\
name=self.name+"Ua")
self._va = layers.Dense(1, use_bias=False, kernel_initializer=weights_initializer,\
kernel_regularizer=weights_regularizer, bias_regularizer=bias_regularizer,\
bias_initializer=bias_initializer, kernel_constraint=weights_constraint,\
bias_constraint=bias_constraint, name=self.name+"Va")
self.supports_masking = True
def __init__(self, units,
activation='relu',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer=None,
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
use_batch_norm=False,
**kwargs):
super(IndRNNCell, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
if recurrent_initializer is None:
self.recurrent_initializer = initializers.RandomUniform(-1.0, 1.0)
else:
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.use_batch_norm = use_batch_norm
self.state_size = self.units
self.output_size = self.units
def __init__(self,
units,
use_bias=True,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
bias_initializer=None,
bias_regularizer=None,
trainable=True,
name=None,
**kwargs):
super(Dense_SN, self).__init__(name=name,
trainable=trainable,
**kwargs)
self.units = units
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
def __init__(self,
n_styles,
epsilon=1e-3,
beta_initializer="zeros",
gamma_initializer="ones",
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(ConditionalInstanceNormalization, self).__init__(**kwargs)
self.n_styles = n_styles
self.epsilon = epsilon
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
def __init__(self, n_classes=10, s=64, m=0.50, regularizer=None , use_fp16=False,**kwargs):
super(ArcFace, self).__init__(**kwargs)
self.n_classes = n_classes
self.s = s
self.m = m
self.regularizer = regularizers.get(regularizer)
self.use_fp16 = use_fp16
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
self.mm = self.sin_m * m
self.threshold = math.cos(math.pi - m)
def __init__(self,
units: int,
kernel_initializer,
kernel_regularizer=None,
kernel_constraint=None,
**kwargs):
super(MultivariateGaussianNoise, self).__init__(**kwargs)
self.units = units
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
def __init__(self, trainable=True,
initializer='ones',
regularizer=None,
constraint=None,
**kwargs):
super().__init__(**kwargs)
self.trainable = trainable
self.initializer = initializers.get(initializer)
self.regularizer = regularizers.get(regularizer)
self.constraint = constraints.get(constraint)
def __init__(self,
tau_regularizer=None,
**kwargs):
"""Initialize the TLU layer.
Args:
tau_regularizer: tf.keras.regularizer for tau.
**kwargs: keyword arguments passed to the Keras layer base class.
"""
self.tau_regularizer = regularizers.get(tau_regularizer)
super(TLU, self).__init__(**kwargs)
def __init__(self, n_classes=10, s=10.0, m=0.50, regularizer=None, **kwargs):
self.n_classes = n_classes
self.s = tf.dtypes.cast(s, dtype=K.floatx())
self.m = tf.dtypes.cast(m, dtype=K.floatx())
self.cos_m = tf.dtypes.cast(math.cos(m), dtype=K.floatx())
self.sin_m = tf.dtypes.cast(math.sin(m), dtype=K.floatx())
self.threshold = tf.dtypes.cast(math.cos(math.pi - m), dtype=K.floatx())
self.mm = tf.dtypes.cast(math.sin(m)*m, dtype=K.floatx())
self.regularizer = regularizers.get(regularizer)
super(ArcFace, self).__init__(**kwargs)
def __init__(self,
n_classes=10,
s=30.0,
m=0.50,
regularizer=None,
**kwargs):
super(ArcFace, self).__init__(**kwargs)
self.n_classes = n_classes
self.s = s
self.m = m
self.regularizer = regularizers.get(regularizer)
def __init__(self,
initializer='glorot_uniform',
activation='sigmoid',
activity_regularizer=None,
**kwargs):
super(RawWeights, self).__init__(**kwargs)
self.activation = activations.get(activation)
self.initializer = initializers.get(initializer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.supports_masking = False
def __init__(self,
attn_kernel_initializer='glorot_uniform',
attn_kernel_regularizer=None,
attn_kernel_constraint=None,
**kwargs):
super().__init__(**kwargs)
self.attn_kernel_initializer = initializers.get(
attn_kernel_initializer)
self.attn_kernel_regularizer = regularizers.get(
attn_kernel_regularizer)
self.attn_kernel_constraint = constraints.get(attn_kernel_constraint)
def __init__(self,
embedding_dim=10,
regularizer='l2',
trainable=True,
numerical_embedding=True,
**kwargs):
super(Embedding, self).__init__(**kwargs)
self._regularizer = regularizers.get(regularizer)
self._embedding_dim = embedding_dim
self._trainable = trainable
self._numerical_embedding = numerical_embedding
def __init__(self,
n_classes=10,
enhance=64.0,
penalty=0.50,
regularizer=None,
**kwargs):
super(ArcFace, self).__init__(**kwargs)
self.n_classes = n_classes
self.s = enhance
self.m = penalty
self.regularizer = get(regularizer)
def __init__(self,
nb_gaussian,
init='normal',
weights=None,
W_regularizer=None,
activity_regularizer=None,
W_constraint=None,
**kwargs):
self.nb_gaussian = nb_gaussian
#self.init = initializers.get(init, dim_ordering='th')
self.init = initializers.get(init) #New Version
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
super(LearningPrior, self).__init__(**kwargs)
def __init__(self,
W_regularizer=None, u_regularizer=None, b_regularizer=None,
W_constraint=None, u_constraint=None, b_constraint=None,
bias=True,
return_attention=False, **kwargs):
self.supports_masking = True
self.return_attention = return_attention
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.u_regularizer = regularizers.get(u_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.u_constraint = constraints.get(u_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def __init__(self,
axis=-1,
momentum=0.99,
center=True,
scale=True,
epsilon=1e-3,
r_max_value=3.,
d_max_value=5.,
t_delta=1.,
weights=None,
beta_initializer='zero',
gamma_initializer='one',
moving_mean_initializer='zeros',
moving_variance_initializer='ones',
gamma_regularizer=None,
beta_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
self.supports_masking = True
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.momentum = momentum
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
self.r_max_value = r_max_value
self.d_max_value = d_max_value
self.t_delta = t_delta
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.moving_mean_initializer = initializers.get(
moving_mean_initializer)
self.moving_variance_initializer = initializers.get(
moving_variance_initializer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
super(BatchRenormalization, self).__init__(**kwargs)
def __init__(self,
head_num,
name="attention",
activation='relu',
use_bias=True,
kernel_initializer='glorot_normal',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
history_only=False,
**kwargs):
"""Initialize the layer.
:param head_num: Number of heads.
:param activation: Activations for linear mappings.
:param use_bias: Whether to use bias term.
:param kernel_initializer: Initializer for linear mappings.
:param bias_initializer: Initializer for linear mappings.
:param kernel_regularizer: Regularizer for linear mappings.
:param bias_regularizer: Regularizer for linear mappings.
:param kernel_constraint: Constraints for linear mappings.
:param bias_constraint: Constraints for linear mappings.
:param history_only: Whether to only use history in attention layer.
"""
self.supports_masking = True
self.head_num = head_num
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.history_only = history_only
self.Wq, self.Wk, self.Wv, self.Wo = None, None, None, None
self.bq, self.bk, self.bv, self.bo = None, None, None, None
super(MultiHeadAttention, self).__init__(name=name, **kwargs)
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
spectral_normalization=True,
bias_constraint=None,
**kwargs):
if data_format is None:
data_format = K.image_data_format()
super(Conv2D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.u = K.random_normal_variable(
[1, filters], 0, 1, dtype=self.dtype, name="sn_estimate") # [1, out_channels]
self.spectral_normalization = spectral_normalization
def __init__(self,
rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
spectral_normalization=True,
**kwargs):
super(_ConvSN, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank,
'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
self.spectral_normalization = spectral_normalization
self.u = None
def __init__(self,
axis=-1,
momentum=0.9,
epsilon=1e-4,
center=True,
scale=True,
beta_initializer='zeros',
gamma_diag_initializer='sqrt_init',
gamma_off_initializer='zeros',
moving_mean_initializer='zeros',
moving_variance_initializer='sqrt_init',
moving_covariance_initializer='zeros',
beta_regularizer=None,
gamma_diag_regularizer=None,
gamma_off_regularizer=None,
beta_constraint=None,
gamma_diag_constraint=None,
gamma_off_constraint=None,
**kwargs):
super(ComplexBatchNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.axis = axis
self.momentum = momentum
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = sanitizedInitGet(beta_initializer)
self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
self.moving_mean_initializer = sanitizedInitGet(
moving_mean_initializer)
self.moving_variance_initializer = sanitizedInitGet(
moving_variance_initializer)
self.moving_covariance_initializer = sanitizedInitGet(
moving_covariance_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
self.gamma_off_constraint = constraints.get(gamma_off_constraint)
def __init__(self,
filters,
kernel_size,
activation=None,
use_bias=True,
kernel_initializer='uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
normalize=False,
offset=None,
in_channels=None,
**kwargs):
from tensorflow.keras import activations, initializers, regularizers
self.filters = filters
self.kernel_size = kernel_size
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.normalize = normalize
if not (np.asarray(kernel_size) == kernel_size[0]).all():
raise Exception("Only cubic kernel sizes are supported.")
if offset is None:
if kernel_size[0] % 2:
self.offset = tf.zeros(shape=(3, ))
else:
self.offset = tf.fill([3], -0.5)
else:
self.offset = offset
self.fixed_radius_search = FixedRadiusSearch(metric='Linf',
ignore_query_point=False,
return_distances=False)
super().__init__(**kwargs)
def _get_regularisers_from_keywords(self, kwargs):
self.kernel_initializer = initializers.get(
kwargs.pop("kernel_initializer", "glorot_uniform"))
self.kernel_regularizer = regularizers.get(
kwargs.pop("kernel_regularizer", None))
self.kernel_constraint = constraints.get(
kwargs.pop("kernel_constraint", None))
self.bias_initializer = initializers.get(
kwargs.pop("bias_initializer", "zeros"))
self.bias_regularizer = regularizers.get(
kwargs.pop("bias_regularizer", None))
self.bias_constraint = constraints.get(
kwargs.pop("bias_constraint", None))
self.attn_kernel_initializer = initializers.get(
kwargs.pop("attn_kernel_initializer", "glorot_uniform"))
self.attn_kernel_regularizer = regularizers.get(
kwargs.pop("attn_kernel_regularizer", None))
self.attn_kernel_constraint = constraints.get(
kwargs.pop("attn_kernel_constraint", None))
def __init__(self,
kernel_initializer='ones',
kernel_regularizer=None,
kernel_constraint=regularizers.l1_l2(l1=1e-3, l2=1e-3),
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'), )
super(DFS, self).__init__(**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.supports_masking = True
def __init__(self,
reg_epsilon=1.0e-6,
tau_regularizer=None,
beta_regularizer=None,
gamma_regularizer=None,
**kwargs):
"""Initialize the FRN layer.
Args:
reg_epsilon: float, the regularization parameter preventing a division by
zero.
tau_regularizer: tf.keras.regularizer for tau.
beta_regularizer: tf.keras.regularizer for beta.
gamma_regularizer: tf.keras.regularizer for gamma.
**kwargs: keyword arguments passed to the Keras layer base class.
"""
self.reg_epsilon = reg_epsilon
self.tau_regularizer = regularizers.get(tau_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
super(FRN, self).__init__(**kwargs)
def __init__(self,
step_dim,
W_regularizer=None,
b_regularizer=None,
W_constraint=None,
b_constraint=None,
bias=True,
**kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.step_dim = step_dim
self.features_dim = 0
super(Attention, self).__init__(**kwargs)
