model.py 文件源码

def loss(logits, depths, invalid_depths):
    #304*228    55*74
    #576*172    27*142
    out_put_size = 55*74
    logits_flat = tf.reshape(logits, [-1, out_put_size])
    depths_flat = tf.reshape(depths, [-1, out_put_size])
    invalid_depths_flat = tf.reshape(invalid_depths, [-1, out_put_size])

    predict = tf.multiply(logits_flat, invalid_depths_flat)
    target = tf.multiply(depths_flat, invalid_depths_flat)

    d = tf.subtract(predict, target)
    square_d = tf.square(d)
    sum_square_d = tf.reduce_sum(square_d, 1)
    sum_d = tf.reduce_sum(d, 1)
    sqare_sum_d = tf.square(sum_d)

    cost = tf.reduce_mean(sum_square_d / out_put_size - 0.5*sqare_sum_d / math.pow(out_put_size, 2))

    tf.add_to_collection('losses', cost)



    #return tf.add_n(tf.get_collection('losses'), name='total_loss')