def __init__(self, epsilon=1e-6, beta_initializer='zeros', gamma_initializer='ones', tau_initializers='zeros', beta_regularizer=None, gamma_regularizer=None, tau_regularizer=None, beta_constraint=None, gamma_constraint=None, tau_constraint=None, **kwargs): super(FRN, self).__init__(**kwargs) self.supports_masking = True self.epsilon = epsilon self.beta_initializer = initializers.get(beta_initializer) self.tau_initializer = initializers.get(tau_initializers) self.gamma_initializer = initializers.get(gamma_initializer) self.beta_regularizer = regularizers.get(beta_regularizer) self.gamma_regularizer = regularizers.get(gamma_regularizer) self.tau_regularizer = regularizers.get(tau_regularizer) self.beta_constraint = constraints.get(beta_constraint) self.gamma_constraint = constraints.get(gamma_constraint) self.tau_constraint = constraints.get(tau_constraint) self.tau = None self.gamma = None self.beta = None self.axis = -1
def __init__(self, init='glorot_uniform', U_regularizer=None, b_start_regularizer=None, b_end_regularizer=None, U_constraint=None, b_start_constraint=None, b_end_constraint=None, weights=None, **kwargs): self.supports_masking = True self.uses_learning_phase = True self.input_spec = [InputSpec(ndim=3)] self.init = initializers.get(init) self.U_regularizer = regularizers.get(U_regularizer) self.b_start_regularizer = regularizers.get(b_start_regularizer) self.b_end_regularizer = regularizers.get(b_end_regularizer) self.U_constraint = constraints.get(U_constraint) self.b_start_constraint = constraints.get(b_start_constraint) self.b_end_constraint = constraints.get(b_end_constraint) self.initial_weights = weights super(ChainCRF, self).__init__(**kwargs)
def __init__(self, units=None, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, **kwargs): if units is None: assert 'output_dim' in kwargs, 'Missing argument: units' else: kwargs['output_dim'] = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) 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.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) super(ExtendedRNNCell, self).__init__(**kwargs)
def __init__(self, units, 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, tied_to=None, **kwargs): self.tied_to = tied_to if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) super().__init__(**kwargs) self.units = units 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 = layers.InputSpec(min_ndim=2) self.supports_masking = True
def __init__(self, units, sigma_init=0.02, 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(NoisyDense, self).__init__(**kwargs) self.units = units self.sigma_init = sigma_init 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)
def __init__(self, epsilon=1e-4, axis=-1, center=True, scale=True, beta_initializer='zeros', gamma_diag_initializer=sqrt_init, gamma_off_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): self.supports_masking = True self.epsilon = epsilon self.axis = axis self.center = center self.scale = scale self.beta_initializer = initializers.get(beta_initializer) self.gamma_diag_initializer = initializers.get(gamma_diag_initializer) self.gamma_off_initializer = initializers.get(gamma_off_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) super(ComplexLayerNorm, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform', activation=None, weights=None, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None, **kwargs): if 'transform_bias' in kwargs: kwargs.pop('transform_bias') warnings.warn('`transform_bias` argument is deprecated and ' 'has been removed.') self.init = initializers.get(init) self.activation = activations.get(activation) self.W_regularizer = regularizers.get(W_regularizer) self.b_regularizer = regularizers.get(b_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.W_constraint = constraints.get(W_constraint) self.b_constraint = constraints.get(b_constraint) self.bias = bias self.initial_weights = weights self.input_spec = InputSpec(ndim=2) self.input_dim = input_dim if self.input_dim: kwargs['input_shape'] = (self.input_dim, ) super(Highway, self).__init__(**kwargs)
def __init__(self, axis=None, epsilon=1e-3, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, **kwargs): super(InstanceNormalization, self).__init__(**kwargs) self.supports_masking = True self.axis = axis if self.axis == 0: raise ValueError('Axis cannot be zero') 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, units, activation=lambda x: x, use_bias=True, kernel_initializer='glorot_uniform', kernel_regularizer=None, kernel_constraint=None, bias_initializer='zeros', bias_regularizer=None, bias_constraint=None, activity_regularizer=None, **kwargs): self.units = units 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) super(GCNConv, self).__init__()
def __init__(self, channels, adj, 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) self.channels = channels self.adj = adj 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.supports_masking = False fltr = localpooling_filter(self.adj) self.fltr = sp_matrix_to_sp_tensor(fltr)
def __init__(self, alpha_initializer='zeros', b_initializer='zeros', S=1, alpha_regularizer=None, b_regularizer=None, alpha_constraint=None, b_constraint=None, shared_axes=None, **kwargs): super(APL, self).__init__(**kwargs) self.supports_masking = True self.alpha_initializer = initializers.get(alpha_initializer) self.alpha_regularizer = regularizers.get(alpha_regularizer) self.alpha_constraint = constraints.get(alpha_constraint) self.b_initializer = initializers.get(b_initializer) self.b_regularizer = regularizers.get(b_regularizer) self.b_constraint = constraints.get(b_constraint) if shared_axes is None: self.shared_axes = None elif not isinstance(shared_axes, (list, tuple)): self.shared_axes = [shared_axes] else: self.shared_axes = list(shared_axes) self.S = S self.alpha_arr = [] self.b_arr = []
def __init__(self, output_dim, num_filters, graph_conv_filters, 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(GraphCNN, self).__init__(**kwargs) self.output_dim = output_dim self.num_filters = num_filters if num_filters != int(graph_conv_filters.get_shape().as_list()[-2] / graph_conv_filters.get_shape().as_list()[-1]): raise ValueError( 'num_filters does not match with graph_conv_filters dimensions.' ) self.graph_conv_filters = graph_conv_filters self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_initializer.__name__ = 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)
def __init__(self, units, support=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): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) super(GraphConvolution, self).__init__(**kwargs) self.units = units 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.supports_masking = True self.support = support assert support >= 1.0
def __init__( self, units, G_constraint=None, G_initializer='glorot_uniform', G_regularizer=None, M_hat_constraint=None, M_hat_initializer='glorot_uniform', M_hat_regularizer=None, W_hat_constraint=None, W_hat_initializer='glorot_uniform', W_hat_regularizer=None, cell=None, e=1e-28, **kwargs, ): assert cell in ['a', 'm', None] super(NALU, self).__init__(**kwargs) self.cell = cell self.G = None self.G_constraint = constraints.get(G_constraint) self.G_initializer = initializers.get(G_initializer) self.G_regularizer = regularizers.get(G_regularizer) self.M_hat = None self.M_hat_constraint = constraints.get(M_hat_constraint) self.M_hat_initializer = initializers.get(M_hat_initializer) self.M_hat_regularizer = regularizers.get(M_hat_regularizer) self.W_hat = None self.W_hat_constraint = constraints.get(W_hat_constraint) self.W_hat_initializer = initializers.get(W_hat_initializer) self.W_hat_regularizer = regularizers.get(W_hat_regularizer) self.e = e self.supports_masking = True self.units = units
def __init__( self, algebra: GeometricAlgebra, blade_indices_kernel: List[int], blade_indices_bias: Union[None, List[int]] = None, 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__(algebra=algebra, activity_regularizer=activity_regularizer, **kwargs) self.blade_indices_kernel = tf.convert_to_tensor( blade_indices_kernel, dtype_hint=tf.int64) if use_bias: self.blade_indices_bias = tf.convert_to_tensor( blade_indices_bias, dtype_hint=tf.int64) 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, units, kernel_initializer='glorot_uniform', activation=None, weights=None, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, use_bias=True, **kwargs): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'), ) self.kernel_initializer = initializers.get(kernel_initializer) self.activation = activations.get(activation) self.units = units 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.use_bias = use_bias self.initial_weights = weights super(CosineDense, self).__init__(**kwargs)
def __init__(self, groups=8, 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): """ Initializes one group normalization layer. References: - [Group Normalization](https://arxiv.org/abs/1803.08494) """ 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, units, 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): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) super(Dense, self).__init__( activity_regularizer=regularizers.get(activity_regularizer), units=int(units), 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), kernel_constraint=constraints.get(kernel_constraint), bias_constraint=constraints.get(bias_constraint), **kwargs) self.u = K.random_normal_variable( [1, units], 0, 1, dtype=self.dtype, name="sn_estimate") # [1, out_channels] self.spectral_normalization = spectral_normalization
def __init__(self, units, use_bias=False, 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.use_bias = use_bias 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)
def __init__( self, num_factors, axis, # the axis along which the inputs are normalized epsilon=1e-3, beta_initializer="zeros", gamma_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, **kwargs): super().__init__(**kwargs) # normalize either along the time steps, or the channels # axis == -1 is layer norm # axis == 1 is instance norm assert axis == 1 or axis == -1 self.num_factors = num_factors self.axis = axis 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, filters, kernel_size, use_bias=False, dropout=0.5, 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.filters = filters self.kernel_size = kernel_size self.use_bias = use_bias self.dropout_rate = dropout 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)
def __init__(self, units, use_bias=True, 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): kwargs.pop('concat', None) # in order to be compatible with `SAGEAggregator` super().__init__(**kwargs) self.units = units self.use_bias = use_bias 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)
def __init__(self, k, mlp_hidden=None, mlp_activation='relu', return_mask=False, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super().__init__(**kwargs) self.k = k self.mlp_hidden = mlp_hidden if mlp_hidden else [] self.mlp_activation = mlp_activation self.return_mask = return_mask 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, units, activation=None, use_bias=True, final_layer=False, input_dim=None, kernel_initializer="glorot_uniform", kernel_regularizer=None, kernel_constraint=None, bias_initializer="zeros", bias_regularizer=None, bias_constraint=None, **kwargs): if "input_shape" not in kwargs and input_dim is not None: kwargs["input_shape"] = (input_dim, ) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.final_layer = final_layer self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_initializer = initializers.get(bias_initializer) self.bias_regularizer = regularizers.get(bias_regularizer) self.bias_constraint = constraints.get(bias_constraint) super().__init__(**kwargs)
def __init__(self, activation='relu', transform_activation='sigmoid', kernel_initializer='glorot_uniform', transform_initializer='glorot_uniform', bias_initializer='zeros', transform_bias_initializer=-2, kernel_regularizer=None, transform_regularizer=None, bias_regularizer=None, transform_bias_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): self.activation = activations.get(activation) self.transform_activation = activations.get(transform_activation) self.kernel_initializer = initializers.get(kernel_initializer) self.transform_initializer = initializers.get(transform_initializer) self.bias_initializer = initializers.get(bias_initializer) if isinstance(transform_bias_initializer, int): self.transform_bias_initializer = Constant(value=transform_bias_initializer) else: self.transform_bias_initializer = initializers.get(transform_bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.transform_regularizer = regularizers.get(transform_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.transform_bias_regularizer = regularizers.get(transform_bias_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) super(Highway, self).__init__(**kwargs)
def __init__( self, output_dim: int = 0, bias: bool = False, act: Union[Callable, AnyStr] = "relu", kernel_initializer="glorot_uniform", kernel_regularizer=None, kernel_constraint=None, bias_initializer="zeros", bias_regularizer=None, bias_constraint=None, **kwargs, ): self.output_dim = output_dim self.has_bias = bias self.act = activations.get(act) super().__init__(**kwargs) self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_initializer = initializers.get(bias_initializer) self.bias_regularizer = regularizers.get(bias_regularizer) self.bias_constraint = constraints.get(bias_constraint) # These will be filled in at build time self.bias = None self.w_self = None self.w_group = None self.weight_dims = None self.included_weight_groups = None
def __init__( self, output_dim: int = 0, bias: bool = False, act: Union[Callable, AnyStr] = "relu", kernel_initializer="glorot_uniform", kernel_regularizer=None, kernel_constraint=None, bias_initializer="zeros", bias_regularizer=None, bias_constraint=None, **kwargs, ): self.output_dim = output_dim if output_dim % 2 != 0: raise ValueError("The output_dim must be a multiple of two.") self.half_output_dim = output_dim // 2 self.has_bias = bias self.act = activations.get(act) self.nr = None self.w_neigh = [] self.w_self = None self.bias = None self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_initializer = initializers.get(bias_initializer) self.bias_regularizer = regularizers.get(bias_regularizer) self.bias_constraint = constraints.get(bias_constraint) super().__init__(**kwargs)
def __init__( self, units, activation=None, use_bias=True, final_layer=None, input_dim=None, kernel_initializer="glorot_uniform", kernel_regularizer=None, kernel_constraint=None, bias_initializer="zeros", bias_regularizer=None, bias_constraint=None, **kwargs, ): if "input_shape" not in kwargs and input_dim is not None: kwargs["input_shape"] = (input_dim,) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias if final_layer is not None: raise ValueError( "'final_layer' is not longer supported, use 'tf.gather' or 'GatherIndices' separately" ) self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_initializer = initializers.get(bias_initializer) self.bias_regularizer = regularizers.get(bias_regularizer) self.bias_constraint = constraints.get(bias_constraint) super().__init__(**kwargs)
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, filters, kernel_size, strides=(1, 1), padding='SAME', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=tf.keras.regularizers.l2(1e-4), bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, k=8): super(LightConv2D, self).__init__() self.filters = filters self.kernel_size = kernel_size self.strides = strides self.padding = padding self.data_format = data_format self.dilation_rate = dilation_rate self.activation = 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.k = k