本文整理汇总了Python中tensorflow.python.keras.regularizers.get方法的典型用法代码示例。如果您正苦于以下问题：Python regularizers.get方法的具体用法？Python regularizers.get怎么用？Python regularizers.get使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.keras.regularizers的用法示例。
在下文中一共展示了regularizers.get方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,
input_dim,
output_dim,
dropout_rate=0.0,
activation='tanh',
kernel_initializer='glorot_uniform',
bias_initializer='zeros'):
super(HighwayLayer, self).__init__()
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.dropout_rate = dropout_rate
self.shape = (input_dim, output_dim)
self.input_dim = input_dim
self.output_dim = output_dim
self.kernel = None
self.bias = None 示例2: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,units,
activation=None,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
use_bias=False,
bias_initializer="zeros",
trainable=True,
name=None):
super(Dense3D,self).__init__(trainable=trainable,name=name)
self.units = units
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.use_bias=use_bias
self.bias_initializer = bias_initializer 示例3: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self, alpha_fwd=0.999, alpha_bkw=0.99,
axis=1, epsilon=1e-5,
stream_mu_initializer='zeros', stream_var_initializer='ones',
u_ctrl_initializer='zeros', v_ctrl_initializer='zeros',
trainable=True, name=None, **kwargs):
super(Norm, self).__init__(trainable=trainable, name=name, **kwargs)
# setup mixed precesion
self.dtype_policy = self._mixed_precision_policy \
if self._mixed_precision_policy.name == "infer_float32_vars" \
else self._dtype
if isinstance(self.dtype_policy, Policy):
self.mixed_precision = True
self.fp_type = tf.float32 # full precision
self.mp_type = tf.float16 # reduced precision
else:
self.mixed_precision = False
self.fp_type = self._dtype if self._dtype else tf.float32 # full precision
self.mp_type = self.fp_type # reduced precision
assert axis == 1, 'kernel requires channels_first data_format'
self.axis = axis
self.norm_ax = None
self.epsilon = epsilon
self.alpha_fwd = alpha_fwd
self.alpha_bkw = alpha_bkw
self.stream_mu_initializer = initializers.get(stream_mu_initializer)
self.stream_var_initializer = initializers.get(stream_var_initializer)
self.u_ctrl_initializer = initializers.get(u_ctrl_initializer)
self.v_ctrl_initializer = initializers.get(v_ctrl_initializer) 示例4: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,
input_dim,
output_dim,
adj,
num_features_nonzero,
dropout_rate=0.0,
is_sparse_inputs=False,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer='l2',
bias_regularizer='l2',
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(GraphConvolution, self).__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.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.kernels = list()
self.bias = None
self.input_dim = input_dim
self.output_dim = output_dim
self.is_sparse_inputs = is_sparse_inputs
self.num_features_nonzero = num_features_nonzero
self.adjs = [tf.SparseTensor(indices=am[0], values=am[1], dense_shape=am[2]) for am in adj]
self.dropout_rate = dropout_rate 示例5: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,
input_dim,
output_dim,
adj,
num_features_nonzero,
dropout_rate=0.0,
num_base=-1,
is_sparse_inputs=False,
featureless=False,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer="l2",
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(RGraphConvolutionLayer, self).__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.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.bias = None
self.input_dim = input_dim
self.output_dim = output_dim
self.is_sparse_inputs = is_sparse_inputs
self.featureless = featureless
self.num_features_nonzero = num_features_nonzero
self.support = len(adj)
self.adj_list = [tf.SparseTensor(indices=adj[i][0], values=adj[i][1], dense_shape=adj[i][2])
for i in range(len(adj))]
self.dropout_rate = dropout_rate
self.num_bases = num_base
self.W = list() 示例6: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,
kernel_initializer = 'glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(StressIntensityRange, self).__init__(**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint) 示例7: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,
kernel_initializer = 'glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
table_shape=(1,4,4,1),
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(TableInterpolation, 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.table_shape = table_shape 示例8: __init__
# 需要导入模块: from tensorflow.python.keras import regularizers [as 别名]
# 或者: from tensorflow.python.keras.regularizers import get [as 别名]
def __init__(self,
units,
relations,
kernel_basis_size=None,
activation=None,
use_bias=False,
batch_normalisation=False,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
feature_dropout=None,
support_dropout=None,
name='relational_graph_conv',
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(RelationalGraphConv, self).__init__(
activity_regularizer=regularizers.get(activity_regularizer),
name=name, **kwargs)
self.units = int(units)
self.relations = int(relations)
self.kernel_basis_size = (int(kernel_basis_size)
if kernel_basis_size is not None else None)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.batch_normalisation = batch_normalisation
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.feature_dropout = feature_dropout
self.support_dropout = support_dropout
self.supports_masking = True
self.input_spec = InputSpec(min_ndim=2)
self.dense_layer = rgat_layers.BasisDecompositionDense(
units=self.units * self.relations,
basis_size=self.kernel_basis_size,
coefficients_size=self.relations,
use_bias=False,
kernel_initializer=self.kernel_initializer,
kernel_regularizer=self.kernel_regularizer,
kernel_constraint=self.kernel_constraint,
name=name + '_basis_decomposition_dense',
**kwargs)
if self.batch_normalisation:
self.batch_normalisation_layer = tf.layers.BatchNormalization()