我正在尝试在tensorflow 2.0中编写一个非常基本的损失函数。总之，我有 5 个类，我想使用一种热编码进行训练，而不对其中任何一个进行分组。我希望我的模型能够用 5 个类别中每个类别的值来预测每个输入。之后，我想尝试获取两个最高值，如果它们是 3 或 4，我想将其分类为“好”，如果不是，则将其分类为“坏”。最后，我希望我的损失为 1 精度，其中我所说的精度在以下情况下具有真阳性：
1.模型猜测3，真实类别为3
2.模型猜测为3，真实类别为4
3.模型猜测为4，真实类别为3
4.模型猜测为4，真实类别为4
再说一遍，我知道我可以更改数据标签，但我宁愿不这样做。
我使用了一些已经写好的指标来写我的损失，如下：
#@tf.function
def my_loss(output,real,threeandfour=1,weights=loss_weights,mod=m):
m = tf.keras.metrics.TruePositives(thresholds=0.5)
m.update_state(real,output,sample_weight=weights)
shape_0=tf.shape(output)[0]
#shape_1=tf.constant(2,dtype=tf.int32)
shape_1=2
halfs=tf.math.multiply(tf.constant(0.5,dtype=tf.float32),tf.ones((shape_0,shape_1),dtype=tf.float32))
thrsfrs_1=output[:,2:4]
thrsfrs=tf.cast(thrsfrs_1,dtype=tf.float32)
logs_1=tf.math.greater(thrsfrs,halfs)
logs=tf.cast(logs_1,dtype=tf.float32)
print('shape of log: ',np.shape(logs))
print('few logs: ',logs,)
num_of_3_4s_in_model=tf.reduce_sum(logs)
prec_1=tf.math.divide(m.result(),num_of_3_4s_in_model)
prec=tf.cast(prec_1,dtype=tf.float32)
return tf.math.subtract(tf.constant(1,dtype=tf.float32),prec)
梯度函数：
with tf.GradientTape() as tape:
tape.watch(model.trainable_variables)
y_=model(X_train)
print('y_: ',y_)
loss_value=my_loss(y_,tf_one_hot_train,mod=m,weights=loss_weights)
#loss_value=tf.cast(loss_value,dtype=tf.float32)
print('loss_value: ',loss_value)
grads=tape.gradient(loss_value,model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
它确实成功地获得了张量流的损失值，并且看起来不错。这是我得到的梯度和错误：
python
got grads
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]
ValueError Traceback (most recent call last)
<ipython-input-370-2f8f4b783a7b> in <module>()
23
24 #optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
---> 25 optimizer.apply_gradients(zip(grads, model.trainable_variables))
26
27 #print("Step: {}, Loss: {}".format(global_step.numpy(),
1 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py in _filter_grads(grads_and_vars)
973 if not filtered:
974 raise ValueError("No gradients provided for any variable: %s." %
--> 975 ([v.name for _, v in grads_and_vars],))
976 if vars_with_empty_grads:
977 logging.warning(
ValueError: No gradients provided for any variable: ['dense_40/kernel:0', 'dense_40/bias:0', 'dense_41/kernel:0', 'dense_41/bias:0', 'dense_42/kernel:0', 'dense_42/bias:0', 'dense_43/kernel:0', 'dense_43/bias:0', 'dense_44/kernel:0', 'dense_44/bias:0', 'dense_45/kernel:0', 'dense_45/bias:0', 'dense_46/kernel:0', 'dense_46/bias:0', 'dense_47/kernel:0', 'dense_47/bias:0']
我试图包含 @tf.function，我尝试将 2 转换为 int 等。我还尝试使用许多不同的其他函数（例如 tf.confusion_matrix）甚至不包含任何东西，包括仅 tf.arg_max诸如此类的事情。似乎什么都不起作用。
我正在为我能想到的损失添加最张量流的代码。同样的事情不断发生。我将它与张量流对象、numpy 对象一起使用，我检查了我的输入是从零到一，仍然没有渐变。这是我的张量流损失：
#@tf.function
def my_loss(real,output):
threeandfour=tf.constant(1,dtype=tf.float32)
#turning real into real classes (opposite of one hot encoding)
real_classes=tf.argmax(real,axis=1)
real_classes=tf.cast(real_classes,dtype=tf.float32)
#tf.print('real_classes: ',real_classes)
pred_classes=tf.argmax(output,axis=1)
pred_classes=tf.cast(pred_classes,dtype=tf.float32)
#tf.print('pred_classes: ',pred_classes)
#checking how many 3s and 4s there are in both
good_real=(tf.logical_or(tf.equal(real_classes,3),tf.equal(real_classes,4)))
good_real=tf.cast(good_real,dtype=tf.float32)
#tf.print('good_real: ',good_real)
good_pred=(tf.logical_or(tf.equal(pred_classes,3),tf.equal(pred_classes,4)))
good_pred=tf.cast(good_pred,dtype=tf.float32)
#tf.print('good_pred: ',good_pred)
#which ones do the real and model agree on
same=tf.math.equal(good_pred,good_real)
same=tf.cast(same,dtype=tf.float32)
#print('same: ',same)
#which ones do they both think are good (3 and 4)
same_goods=tf.math.multiply(same,good_pred)
same_goods=tf.cast(same_goods,dtype=tf.float32)
#print('same goods: ',same_goods)
#number of ones they both think are good
num_same_goods=tf.reduce_sum(same_goods)
num_same_goods=tf.cast(num_same_goods,dtype=tf.float32)
#print('num_same_goods: ',num_same_goods)
#number of ones model thinks are good
num_pred_goods=tf.reduce_sum(good_pred)
num_pred_goods=tf.cast(num_pred_goods,dtype=tf.float32)
#print('num_pred_goods: ',num_pred_goods)
#making sure not to divide by 0
non_zero_num=tf.math.add(num_pred_goods,tf.constant(0.0001,dtype=tf.float32))
#precision
prec=tf.math.divide(num_same_goods,non_zero_num)
prec=tf.cast(prec,dtype=tf.float32)
#tf.print('prec: ',prec)
#1-precision
one_minus_prec=tf.math.subtract(tf.constant(1,dtype=tf.float32),prec)
one_minus_prec=tf.cast(one_minus_prec,dtype=tf.float32)
return one_minus_prec
