Question

我想训练一个LSTM模型来预测海洋漂浮物的位置（纬度，经度）。我尝试使用Haversine损失函数，但我不知道如何实现它。

确切地说，我使用Keras，模型输出的形状为（batch_size，2）

@tf.function
def haversine(label_true,label_pred):
    lon1 = label_true[1]
    lat1 = label_true[0]
    lon2 = label_pred[1]
    lat2 = label_pred[0]

    lon1,lat1,lon2,lat2 = map(radians, [lon1,lat1,lon2,lat2])

    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = sin(dlat/2)**2 + cos(lat1)*cos(lat2)*sin(dlon/2)**2
    c = 2 * asin(sqrt(a))
    r = 6371   
    return c*r*1000


@tf.function
def loss_haversine(y_true,y_pred):
    loss =0
    total_dis = np.array([haversine(label_true,label_pred) for label_true,label_pred in zip(y_true,y_pred)])
    print(total_dis.shape)
    return tf.convert_to_tensor(np.sum(total_dis), dtype=tf.float32)

def create_model(lstm_size=32,timesteps=3,features=6,lstm_dropout=0.1):
    model = Sequential()
    model.add(LSTM(lstm_size,input_shape=(timesteps,features),recurrent_dropout=lstm_dropout,return_sequences=True))
    model.add(Dropout(0.2))
    model.add(LSTM(lstm_size,input_shape=(timesteps,features),return_sequences=False))
    model.add(Dropout(0.2))
    model.add(Dense(2)) 
    #model.add(Activation('linear'))
    model.compile(loss=loss_haversine,optimizer='adam', metrics=['accuracy']) 
    print(model.summary())
    return model

训练模型时，出现以下错误： “ OperatorNotAllowedInGraphError：不允许在tf.Tensor上进行迭代：AutoGraph没有转换此函数。”

非常感谢！

Answer 1

鉴于张量流是如何工作的，我的解决方案是通过矩阵运算来计算正弦距离。代码如下：

import tensorflow as tf

RADIUS_KM = 6378.1

def degrees_to_radians(deg):
    pi_on_180 = 0.017453292519943295
    return deg * pi_on_180

def loss_haversine(observation, prediction):    
    obv_rad = tf.map_fn(degrees_to_radians, observation)
    prev_rad = tf.map_fn(degrees_to_radians, prediction)

    dlon_dlat = obv_rad - prev_rad 
    v = dlon_dlat / 2
    v = tf.sin(v)
    v = v**2

    a = v[:,1] + tf.cos(obv_rad[:,1]) * tf.cos(prev_rad[:,1]) * v[:,0] 

    c = tf.sqrt(a)
    c = 2* tf.math.asin(c)
    c = c*RADIUS_KM
    final = tf.reduce_sum(c)

    #if you're interested in having MAE with the haversine distance in KM
    #uncomment the following line
    #final = final/tf.dtypes.cast(tf.shape(observation)[0], dtype= tf.float32)

    return final

在Tensorflow中训练模型时如何使用hasversine函数作为损失函数？

1 个答案: