def __init__(self,
groups=32,
axis=-1,
epsilon=1e-5,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(GroupNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.groups = groups
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
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,
epsilon=1e-3,
mode=0,
axis=-1,
momentum=0.99,
r_max_value=3.,
d_max_value=5.,
t_delta=1e-3,
weights=None,
beta_init='zero',
gamma_init='one',
gamma_regularizer=None,
beta_regularizer=None,
**kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.mode = mode
self.axis = axis
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
if self.mode == 0:
self.uses_learning_phase = True
super(BatchRenormalization, self).__init__(**kwargs)
def __init__(
self,
units,
tied_to=None, # Enter a layer as input to enforce weight-tying
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):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'), )
super(DenseTransposeTied, self).__init__(**kwargs)
self.units = units
# We add these two properties to save the tied weights
self.tied_to = tied_to
self.tied_weights = self.tied_to.weights
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(min_ndim=2)
self.supports_masking = True
def __init__(self,
alpha_initializer='zeros',
activity_regularizer=None,
alpha_constraint=None,
shared_axes=None,
**kwargs):
super(SparseReLU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha_initializer = initializers.get(alpha_initializer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.alpha_constraint = constraints.get(alpha_constraint)
def __init__(self, axis=0, activity_regularizer=None, **kwargs):
self.axis = axis
self.uses_learning_phase = True
self.activity_regularizer = regularizers.get(activity_regularizer)
super(SumToOne, self).__init__(**kwargs)
