def __init__(self,<EOL>name='<STR_LIT:relu>',<EOL>alpha=None,<EOL>beta=<NUM_LIT:1.0>,<EOL>max=None,<EOL>min=None,<EOL>named_tensors=None,<EOL>scope='<STR_LIT>',<EOL>summary_labels=()<EOL>):
self.name = name<EOL>self.alpha = None<EOL>self.max = None<EOL>self.min = None<EOL>self.beta_learn = False<EOL>super(Nonlinearity, self).__init__(named_tensors=named_tensors, scope=scope, summary_labels=summary_labels)<EOL>if max is not None:<EOL><INDENT>self.max = float(max)<EOL><DEDENT>if min is not None:<EOL><INDENT>self.min = float(min)<EOL><DEDENT>if alpha is not None:<EOL><INDENT>self.alpha = float(alpha)<EOL><DEDENT>if beta == '<STR_LIT>':<EOL><INDENT>self.beta_learn = True<EOL>self.beta = None<EOL><DEDENT>else:<EOL><INDENT>self.beta = tf.constant(float(beta), dtype=util.tf_dtype('<STR_LIT:float>'))<EOL><DEDENT>
Non-linearity activation layer.

Args:
    name: Non-linearity name, one of 'elu', 'relu', 'selu', 'sigmoid', 'swish',
        'leaky_relu' (or 'lrelu'), 'crelu', 'softmax', 'softplus', 'softsign', 'tanh' or 'none'.
    alpha: (float|int) Alpha value for leaky Relu
    beta: (float|int|'learn') Beta value or 'learn' to train value (default 1.0)
    max: (float|int) maximum (beta * input) value passed to non-linearity function
    min: (float|int) minimum (beta * input) value passed to non-linearity function
    summary_labels: Requested summary labels for tensorboard export, add 'beta' to watch beta learning