Question

我想和Keras一起做个预测。但是，它在培训中始终具有相同的acc价值。但是在训练中，损失却越来越严重

我正在尝试预测生产参数。

所以我想从其他方面基本上预测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正确的。你有什么建议吗？

从头到尾您都可以看到acc始终相同（0.1333）。

我还要强调一点，我的数据量很少。

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，因为您具有输出单元和最后一个致密层的线性激活函数，所以您正在执行回归。

但是，张量流的准确性将用于分类任务。请参阅文档：https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Accuracy。

Keras深度学习模型在培训中始终给予相同的认可

1 个答案: