layers.py 文件源码

def __call__(self, x, deterministic, train_clip=False, thresh=3):
        # Alpha is the dropout rate
        log_alpha = clip(self.log_sigma2 - tf.log(self.W**2 + eps))

        # Values of log_alpha that are above the threshold
        clip_mask = tf.greater_equal(log_alpha, thresh)

        def true_path(): # For inference
            # If log_alpha >= thresh, return 0
            # If log_alpha < thresh, return tf.matmul(x,self.W)
            return tf.matmul(x, tf.where(clip_mask, tf.zeros_like(self.W), self.W))

        def false_path(): # For training
            # Sample from a normal distribution centred on tf.matmul(x,W)
            # and with variance roughly proportional to the size of tf.matmul(x,W)*tf.exp(log_alpha)
            W = self.W
            if train_clip:
                raise NotImplementedError
            mu = tf.matmul(x,W)
            si = tf.matmul(x*x, tf.exp(log_alpha) * self.W * self.W)
            si = tf.sqrt(si + eps)
            return mu + tf.random_normal(tf.shape(mu), mean=0.0, stddev=1.0) * si

        h = tf.cond(deterministic, true_path, false_path)
        return self.nonlinearity(h + self.b)