Python constraints.get方法代码示例

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 ratio,
                 return_mask=False,
                 sigmoid_gating=False,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):
        super().__init__(**kwargs)
        self.ratio = ratio
        self.return_mask = return_mask
        self.sigmoid_gating = sigmoid_gating
        self.gating_op = K.sigmoid if self.sigmoid_gating else K.tanh
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 channels,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super().__init__(**kwargs)
        self.channels = channels
        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) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 k,
                 channels=None,
                 return_mask=False,
                 activation=None,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):

        super().__init__(**kwargs)
        self.k = k
        self.channels = channels
        self.return_mask = return_mask
        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) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 channels,
                 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.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 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def convert_sequence_vocab(self, sequence, sequence_lengths):
        PFAM_TO_UNIREP_ENCODED = {encoding: UNIREP_VOCAB.get(aa, 23) for aa, encoding in PFAM_VOCAB.items()}

        def to_uniprot_unirep(seq, seqlens):
            new_seq = np.zeros_like(seq)

            for pfam_encoding, unirep_encoding in PFAM_TO_UNIREP_ENCODED.items():
                new_seq[seq == pfam_encoding] = unirep_encoding

            # add start/stop
            new_seq = np.pad(new_seq, [[0, 0], [1, 1]], mode='constant')
            new_seq[:, 0] = UNIREP_VOCAB['<START>']
            new_seq[np.arange(new_seq.shape[0]), seqlens + 1] = UNIREP_VOCAB['<STOP>']

            return new_seq

        new_sequence = tf.py_func(to_uniprot_unirep, [sequence, sequence_lengths], sequence.dtype)
        new_sequence.set_shape([sequence.shape[0], sequence.shape[1] + 2])

        return new_sequence 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 activation: OptStrOrCallable = None,
                 use_bias: bool = True,
                 kernel_initializer: OptStrOrCallable = 'glorot_uniform',
                 bias_initializer: OptStrOrCallable = 'zeros',
                 kernel_regularizer: OptStrOrCallable = None,
                 bias_regularizer: OptStrOrCallable = None,
                 activity_regularizer: OptStrOrCallable = None,
                 kernel_constraint: OptStrOrCallable = None,
                 bias_constraint: OptStrOrCallable = None,
                 **kwargs):
        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
            kwargs['input_shape'] = (kwargs.pop('input_dim'),)
        self.activation = activations.get(activation)  # noqa
        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().__init__(**kwargs) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def call(self, inputs):
        def brelu(x):
            # get shape of X, we are interested in the last axis, which is constant
            shape = K.int_shape(x)
            # last axis
            dim = shape[-1]
            # half of the last axis (+1 if necessary)
            dim2 = dim // 2
            if dim % 2 != 0:
                dim2 += 1
            # multiplier will be a tensor of alternated +1 and -1
            multiplier = K.ones((dim2,))
            multiplier = K.stack([multiplier, -multiplier], axis=-1)
            if dim % 2 != 0:
                multiplier = multiplier[:-1]
            # adjust multiplier shape to the shape of x
            multiplier = K.reshape(multiplier, tuple(1 for _ in shape[:-1]) + (-1,))
            return multiplier * tf.nn.relu(multiplier * x)

        return Lambda(brelu)(inputs) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 dilation_rate=1,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 demod=True,
                 **kwargs):
        super(Conv2DMod, self).__init__(**kwargs)
        self.filters = filters
        self.rank = 2
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate')
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.demod = demod
        self.input_spec = [InputSpec(ndim = 4),
                            InputSpec(ndim = 2)] 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def deserialize_kwarg(key, attr):
    if key.endswith('_initializer'):
        return initializers.get(attr)
    if key.endswith('_regularizer'):
        return regularizers.get(attr)
    if key.endswith('_constraint'):
        return constraints.get(attr)
    if key == 'activation':
        return activations.get(attr) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 trainable_kernel=False,
                 activation=None,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):

        super().__init__(**kwargs)
        self.trainable_kernel = trainable_kernel
        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) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
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) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 groups=4,
                 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) 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def __init__(self,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 depth_multiplier=1,
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 depthwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(DepthwiseConv2D, self).__init__(
            filters=None,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            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.depthwise_regularizer = regularizers.get(depthwise_regularizer)
        self.depthwise_constraint = constraints.get(depthwise_constraint)
        self.bias_initializer = initializers.get(bias_initializer)
        self.depthwise_kernel = None
        self.bias = None 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def get_auto_range_constraint_initializer(quantizer, constraint, initializer):
  """Get value range automatically for quantizer.

  Arguments:
   quantizer: A quantizer class in quantizers.py.
   constraint: A tf.keras constraint.
   initializer: A tf.keras initializer.

  Returns:
    a tuple (constraint, initializer), where
      constraint is clipped by Clip class in this file, based on the
      value range of quantizer.
      initializer is initializer contraint by value range of quantizer.
  """
  if quantizer is not None:
    # let's use now symmetric clipping function
    max_value = max(1, quantizer.max()) if hasattr(quantizer, "max") else 1.0
    min_value = quantizer.min() if hasattr(quantizer, "min") else -1.0

    if constraint:
      constraint = constraints.get(constraint)

    constraint = Clip(-max_value, max_value, constraint, quantizer)
    initializer = initializers.get(initializer)
    if initializer and initializer.__class__.__name__ not in ["Ones", "Zeros"]:
      # we want to get the max value of the quantizer that depends
      # on the distribution and scale
      if not (hasattr(quantizer, "alpha") and
              isinstance(quantizer.alpha, six.string_types)):
        initializer = QInitializer(
            initializer, use_scale=True, quantizer=quantizer)
  return constraint, initializer 

# 需要导入模块: from tensorflow.keras import constraints [as 别名]
# 或者: from tensorflow.keras.constraints import get [as 别名]
def get_config(self):
    return {
        "initializer": self.initializer,
        "use_scale": self.use_scale,
        "quantizer": self.quantizer,
    }


#
# Because it may be hard to get serialization from activation functions,
# we may be replacing their instantiation by QActivation in the future.
#