keras 回归模型训练 accuracy不变

在进行回归模型训练时，accuracy的值一直为一个固定的值是什么原因呀

小魔女参考了bing和GPT部分内容调写:
accuracy不变可能是模型过拟合或者欠拟合的原因，过拟合就是模型在训练集上表现很好，但是在测试集上表现不好，而欠拟合则是模型在训练集和测试集上都表现不好，accuracy值一直不变。

解决这个问题的方法有很多，比如增加训练数据，增加模型的复杂度，改变模型的结构，添加正则化项，改变学习率等等。

# 例子
model.add(Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.01)))

另外，在训练模型时，可以使用K折交叉验证，以确保模型的泛化能力，避免过拟合。K折交叉验证是将训练集分成K份，每次取其中一份作为验证集，其余K-1份作为训练集，这样重复K次，最后取K次训练结果的平均值作为最终的训练结果。

# 例子
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):
    model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)
    scores = model.evaluate(X[test], Y[test], verbose=0)
    cvscores.append(scores[1] * 100)

总之，accuracy不变可能是模型过拟合或者欠拟合的原因，可以通过增加训练数据，增加模型复杂度，改变模型结构，添加正则化项，改变学习率等方法来解决，同时也可以使用K折交叉验证来确保模型的泛化能力。
回答不易，记得采纳呀。

• 这篇文章：Keras-训练网络时的问题：loss一直为nan，accuracy一直为一个固定的数 也许有你想要的答案，你可以看看
• 除此之外, 这篇博客: Keras-训练网络时的问题：loss一直为nan，accuracy一直为一个固定的数中的 问题描述 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  

• 在使用VGG19做分类任务时，遇到一个问题：loss一直为nan，accuracy一直为一个固定的数，如下输出所示，即使加入了自动调整学习率 (ReduceLROnPlateau) 也没法解决问题。

  # Change learning_rate auto
  reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')
  
  earlystopping = EarlyStopping(monitor='val_accuracy', verbose=1, patience=30)
  
  # Vgg19
  # Train the model with the new callback
  history = model_vgg19.fit(train_gen, 
                      validation_data=valid_gen, 
                      epochs=200,
                      steps_per_epoch=len(train_gen),
                      validation_steps=len(valid_gen),
                      callbacks=[reduce_lr, earlystopping]) # callbacks=[cp_callback] Pass callback to training
  

  输出：

  Epoch 1/200
  176/176 [==============================] - 31s 177ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 2/200
  176/176 [==============================] - 31s 176ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 3/200
  176/176 [==============================] - 31s 175ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 4/200
  176/176 [==============================] - 31s 176ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 5/200
  176/176 [==============================] - 31s 175ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 6/200
  176/176 [==============================] - 31s 175ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 7/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 8/200
  176/176 [==============================] - 31s 175ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 9/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 10/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 0.0010
  Epoch 11/200
  176/176 [==============================] - 30s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 12/200
  176/176 [==============================] - 31s 175ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 13/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 14/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 15/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 16/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 17/200
  176/176 [==============================] - 30s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 18/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 19/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 20/200
  176/176 [==============================] - 30s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-04
  Epoch 21/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 22/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 23/200
  176/176 [==============================] - 31s 175ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 24/200
  176/176 [==============================] - 31s 177ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 25/200
  176/176 [==============================] - 31s 178ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 26/200
  176/176 [==============================] - 31s 177ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 27/200
  176/176 [==============================] - 31s 177ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 28/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 29/200
  176/176 [==============================] - 31s 173ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 30/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-05
  Epoch 31/200
  176/176 [==============================] - 31s 174ms/step - loss: nan - accuracy: 0.0100 - val_loss: nan - val_accuracy: 0.0100 - lr: 1.0000e-06
  Epoch 00031: early stopping