我正在尝试在 Keras 中定义自定义损失函数
def yolo_loss(y_true, y_pred):
这里 y_true 和 y_pred 的形状是 [batch_size,19,19,5]。
对于批次中的每个图像，我想将损失计算为：
loss =
square(y_true[:,:,0] - y_pred[:,:,0])
+ square(y_true[:,:,1] - y_pred[:,:,1])
+ square(y_true[:,:,2] - y_pred[:,:,2])
+ (sqrt(y_true[:,:,3]) - sqrt(y_pred[:,:,3]))
+ (sqrt(y_true[:,:,4]) - sqrt(y_pred[:,:,4]))
我想到了几种方法来做到这一点，
1）使用for循环：
def yolo_loss(y_true, y_pred):
y_ret = tf.zeros([1,y_true.shape[0]])
for i in range(0,int(y_true.shape[0])):
op1 = y_true[i,:,:,:]
op2 = y_pred[i,:,:,:]
class_error = tf.reduce_sum(tf.multiply((op1[:,:,0]-op2[:,:,0]),(op1[:,:,0]-op2[:,:,0])))
row_error = tf.reduce_sum(tf.multiply((op1[:,:,1]-op2[:,:,1]),(op1[:,:,1]-op2[:,:,1])))
col_error = tf.reduce_sum(tf.multiply((op1[:,:,2]-op2[:,:,2]),(op1[:,:,2]-op2[:,:,2])))
h_error = tf.reduce_sum(tf.abs(tf.sqrt(op1[:,:,3])-tf.sqrt(op2[:,:,3])))
w_error = tf.reduce_sum(tf.abs(tf.sqrt(op1[:,:,4])-tf.sqrt(op2[:,:,4])))
total_error = class_error + row_error + col_error + h_error + w_error …