Keras学习二乘乘法的问题

我只是想玩一下Keras，但是我在教它一个基本函数（乘以2）时遇到了一些麻烦。我的设置如下。由于我是新手，我在评论中添加了我认为在每一步都会发生的事情

x_train = np.linspace(1,1000,1000)
y_train=x_train*2
model = Sequential()
model.add(Dense(32, input_dim=1, activation='sigmoid')) #add a 32-node layer
model.add(Dense(32, activation='sigmoid')) #add a second 32-node layer
model.add(Dense(1, activation='sigmoid')) #add a final output layer
model.compile(loss='mse',
              optimizer='rmsprop') #compile it with loss being mean squared error

model.fit(x_train,y_train, epochs = 10, batch_size=100) #train 
score = model.evaluate(x_train,y_train,batch_size=100)
print(score)

我得到以下输出：

1000/1000 [==============================] - 0s 355us/step - loss: 1334274.0375
Epoch 2/10
1000/1000 [==============================] - 0s 21us/step - loss: 1333999.8250
Epoch 3/10
1000/1000 [==============================] - 0s 29us/step - loss: 1333813.4062
Epoch 4/10
1000/1000 [==============================] - 0s 28us/step - loss: 1333679.2625
Epoch 5/10
1000/1000 [==============================] - 0s 27us/step - loss: 1333591.6750
Epoch 6/10
1000/1000 [==============================] - 0s 51us/step - loss: 1333522.0000
Epoch 7/10
1000/1000 [==============================] - 0s 23us/step - loss: 1333473.7000
Epoch 8/10
1000/1000 [==============================] - 0s 24us/step - loss: 1333440.6000
Epoch 9/10
1000/1000 [==============================] - 0s 29us/step - loss: 1333412.0250
Epoch 10/10
1000/1000 [==============================] - 0s 21us/step - loss: 1333390.5000
1000/1000 [==============================] - 0s 66us/step
['loss']
1333383.1143554687

Epoch 860/1000
1000/1000 [==============================] - 0s 29us/step - loss: 5.1868e-08

这个基本函数的损失似乎非常大，我很困惑为什么它不能学习它。我是糊涂了还是做错了什么？

• 使用sigmoid激活将输出限制在[0,1]范围内。但是您的目标输出在[0，2000]范围内，因此您的网络无法学习。请尝试使用

  relu

  激活
• 试着在调试时使用

  adam

  而不是

  rmsprop

  ，它几乎总是工作得更好
• 训练时间更长

综合起来，我得到以下输出：

1000/1000 [==============================] - 0s 355us/step - loss: 1334274.0375
Epoch 2/10
1000/1000 [==============================] - 0s 21us/step - loss: 1333999.8250
Epoch 3/10
1000/1000 [==============================] - 0s 29us/step - loss: 1333813.4062
Epoch 4/10
1000/1000 [==============================] - 0s 28us/step - loss: 1333679.2625
Epoch 5/10
1000/1000 [==============================] - 0s 27us/step - loss: 1333591.6750
Epoch 6/10
1000/1000 [==============================] - 0s 51us/step - loss: 1333522.0000
Epoch 7/10
1000/1000 [==============================] - 0s 23us/step - loss: 1333473.7000
Epoch 8/10
1000/1000 [==============================] - 0s 24us/step - loss: 1333440.6000
Epoch 9/10
1000/1000 [==============================] - 0s 29us/step - loss: 1333412.0250
Epoch 10/10
1000/1000 [==============================] - 0s 21us/step - loss: 1333390.5000
1000/1000 [==============================] - 0s 66us/step
['loss']
1333383.1143554687

Epoch 860/1000
1000/1000 [==============================] - 0s 29us/step - loss: 5.1868e-08