DDTL_alex.py 文件源码

def source_distance(x,y):
    y = tf.cast(tf.argmax(y,axis=1),tf.float32)
    y1,_,_ = tf.unique_with_counts(y)
    TensorArr = tf.TensorArray(tf.float32,size=1, dynamic_size=True,clear_after_read=False)
    x_array = TensorArr.unstack(y1)
    size = x_array.size()
    initial_outputs = tf.TensorArray(dtype=tf.float32,size=size)
    i = tf.constant(0)
    def should_continue(i, *args):
        return i < size
    def loop(i,output):
        y_class = x_array.read(i)
        idx_i = tf.where(tf.equal(y,y_class))
        xi = tf.gather_nd(x,idx_i)
        initial_outputs1 = tf.TensorArray(dtype=tf.float32,size=size)
        j = tf.constant(0)
        def should_continue1(j,*args):
            return j<size
        def loop1(j,output1):
            y2=x_array.read(j)
            idx_j = tf.where(tf.equal(y,y2))
            xj = tf.gather_nd(x,idx_j)
            dis = tf.reduce_mean (tf.square(tf.reduce_mean(xi,0)
                        -tf.reduce_mean(xj,0)))
            output1 = output1.write(j,dis)
            return j+1,output1
        j,r1=tf.while_loop(should_continue1,loop1,[j,initial_outputs1])
        output = output.write(i,r1.stack())
        return i+1,output
    i,r = tf.while_loop(should_continue,loop,[i,initial_outputs])
    out = r.stack()
    return out