Python tensorflow.keras.backend.tanh() Examples

The following are 6 code examples of tensorflow.keras.backend.tanh(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.keras.backend , or try the search function .
Example #1
Source File: topk_pool.py    From spektral with MIT License 6 votes vote down vote up 
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)
Example #2
Source File: utils.py    From neuron with GNU General Public License v3.0 5 votes vote down vote up 
def logtanh(x, a=1):
    """
    log * tanh

    See Also: arcsinh
    """
    return K.tanh(x) *  K.log(2 + a * abs(x))
Example #3
Source File: backend.py    From bert4keras with Apache License 2.0 5 votes vote down vote up 
def gelu_tanh(x):
    """基于Tanh近似计算的gelu函数
    """
    cdf = 0.5 * (
        1.0 + K.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * K.pow(x, 3))))
    )
    return x * cdf
Example #4
Source File: backend.py    From bert4keras with Apache License 2.0 5 votes vote down vote up 
def set_gelu(version):
    """设置gelu版本
    """
    version = version.lower()
    assert version in ['erf', 'tanh'], 'gelu version must be erf or tanh'
    if version == 'erf':
        keras.utils.get_custom_objects()['gelu'] = gelu_erf
    else:
        keras.utils.get_custom_objects()['gelu'] = gelu_tanh
Example #5
Source File: layers.py    From keras-YOLOv3-model-set with MIT License 5 votes vote down vote up 
def mish(x):
    return x * K.tanh(K.softplus(x))
Example #6
Source File: quantizers.py    From qkeras with Apache License 2.0 4 votes vote down vote up 
def __call__(self, x):
    if isinstance(self.alpha, six.string_types):
      assert self.alpha in ["auto", "auto_po2"]
    if self.alpha is None:
      scale = self.default_alpha
    elif isinstance(self.alpha, six.string_types):
      scale = 1.0
    elif isinstance(self.alpha, np.ndarray):
      scale = self.alpha
    else:
      scale = float(self.alpha)

    if self.use_stochastic_rounding:
      len_axis = len(x.shape.as_list())
      if len_axis == 1:
        axis = None
      elif K.image_data_format() == "channels_last":
        axis = list(range(len_axis - 1))
      else:
        axis = list(range(1, len_axis))

      # if stochastic_round is through, we need to scale
      # number so that the precision is small enough.
      # This is especially important if range of x is very
      # small, which occurs during initialization of weights.
      m = K.max(tf.abs(x), axis=axis, keepdims=True)
      m = tf.where(m > 1.0, tf.ones_like(m), m)
      f = 2 * m

      x = tf_utils.smart_cond(
          K.learning_phase(),
          lambda: f * _round_through(
              x / f, use_stochastic_rounding=True, precision=0.125),
          lambda: x)

    k_sign = tf.sign(x)
    if self.use_stochastic_rounding:
      # in inference, we use a biased "1" for stochastic rounding right now
      k_sign += (1.0 - tf.abs(k_sign)) * tf_utils.smart_cond(
          K.learning_phase(),
          lambda: 2.0 * tf.round(tf.random.uniform(tf.shape(x))) - 1.0,
          lambda: tf.ones_like(tf.shape(x), dtype=K.floatx()))
      # if something still remains, just make it positive for now.
    k_sign += (1.0 - tf.abs(k_sign))
    if self.use_01:
      k_sign = (k_sign + 1.0) / 2.0

    # approximate binary by tanh(x) as it has limited range between -1 and +1.
    if self.alpha is None:
      x = K.tanh(x)

    scale = _get_scale(self.alpha, x, k_sign)
    self.scale = scale
    return x + tf.stop_gradient(-x + scale * k_sign)