self_coded_defs.py 文件源码

def regretion_loss(outputs,target_y,batch_size=BATCHSIZE,n_steps=TIMESTEP):
    target_y=tf.reshape(target_y, [-1,LONGITUDE, WIDTH])
    outputs=tf.reshape(outputs,[-1,LONGITUDE, WIDTH])
    losses = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
            [tf.reshape(outputs, [-1], name='reshape_pred')],
            [tf.reshape(target_y, [-1], name='reshape_target')],
            [tf.ones([batch_size * n_steps], dtype=tf.float32)],
            average_across_timesteps=True,
            softmax_loss_function=tf.square(tf.subtract(target_y, outputs)),
            name='losses')
    with tf.name_scope('average_cost'):
        cost = tf.div(
            tf.reduce_sum(losses, name='losses_sum'),batch_size,name='average_cost')
    return cost