python - Keras 深度学习模型在训练中始终给出相同的帐户

Question

我想用 Keras 进行预测。但它在训练中总是给出相同的 acc 值。但训练过程中损失正在减少

我正在尝试预测生产参数。下面给出了一些示例

Data

所以我想基本上从其他人那里预测 fill_press 参数。我的代码在这里:

x = pd.concat([volume, injector, filling_time, machine], axis=1)


x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, random_state=1)



predicter = Sequential()

predicter.add(Dense(units=9, use_bias = True,  kernel_initializer = 'RandomUniform', activation = 'linear', input_dim = 9)) #Input Layer

predicter.add(Dense(units=7, use_bias = True,  kernel_initializer = 'RandomUniform', activation = 'linear'))

predicter.add(Dense(units=4, use_bias = True,  kernel_initializer = 'RandomUniform', activation = 'linear'))

predicter.add(Dense(units=1, kernel_initializer = 'RandomUniform', activation = 'linear'))

predicter.compile(optimizer = "sgd", loss = 'mean_absolute_error', metrics = ['accuracy'])

predicter.fit(x_train, y_train, batch_size =10, epochs = 1000)



y_pred = predicter.predict(X_test)

我应该改变什么？我也不确定我的模型是否正确。有什么推荐吗？

如您所见，acc 从开始到结束始终相同(0.1333)。

我还应该强调一点，我的数据量非常少。

训练输出:

Epoch 985/1000
45/45 [==============================] - 0s 337us/step - loss: 0.0990 - acc: 0.1333
Epoch 986/1000
45/45 [==============================] - 0s 289us/step - loss: 0.1006 - acc: 0.1333
Epoch 987/1000
45/45 [==============================] - 0s 266us/step - loss: 0.1003 - acc: 0.1333
Epoch 988/1000
45/45 [==============================] - 0s 355us/step - loss: 0.0997 - acc: 0.1333
Epoch 989/1000
45/45 [==============================] - 0s 199us/step - loss: 0.1003 - acc: 0.1333
Epoch 990/1000
45/45 [==============================] - 0s 167us/step - loss: 0.1001 - acc: 0.1333
Epoch 991/1000
45/45 [==============================] - 0s 200us/step - loss: 0.0997 - acc: 0.1333
Epoch 992/1000
45/45 [==============================] - 0s 222us/step - loss: 0.0987 - acc: 0.1333
Epoch 993/1000
45/45 [==============================] - 0s 304us/step - loss: 0.0984 - acc: 0.1333
Epoch 994/1000
45/45 [==============================] - 0s 244us/step - loss: 0.1001 - acc: 0.1333
Epoch 995/1000
45/45 [==============================] - 0s 332us/step - loss: 0.1006 - acc: 0.1333
Epoch 996/1000
45/45 [==============================] - 0s 356us/step - loss: 0.0999 - acc: 0.1333
Epoch 997/1000
45/45 [==============================] - 0s 332us/step - loss: 0.1014 - acc: 0.1333
Epoch 998/1000
45/45 [==============================] - 0s 394us/step - loss: 0.0988 - acc: 0.1333
Epoch 999/1000
45/45 [==============================] - 0s 269us/step - loss: 0.1013 - acc: 0.1333
Epoch 1000/1000
45/45 [==============================] - 0s 242us/step - loss: 0.0992 - acc: 0.1333

Answer 1

我想既然你有输出单元和最后一个密集层的线性激活函数，你正在执行回归。

但是， tensorflow 的准确性旨在用于分类任务。请参阅文档:https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy .