Question

我想预测理想的线性数据（同函数）

data = np.asarray(range(100),dtype=np.float32)

我习惯了这个线性函数

model = Sequential([
    Dense(1, input_shape=(1,))
])

model.compile(optimizer='sgd', loss='mse')

model.fit(data, data, epochs=10, batch_size=100)

但是我的损失功能正在增加。这个简单的代码有什么问题？

Epoch 1/10
100/100 [==============================] - 1s 7ms/step - loss: 3559.4075
Epoch 2/10
100/100 [==============================] - 0s 20us/step - loss: 14893056.0000
Epoch 3/10
100/100 [==============================] - 0s 170us/step - loss: 62314639360.0000
Epoch 4/10
100/100 [==============================] - 0s 30us/step - loss: 260733187129344.0000
Epoch 5/10
100/100 [==============================] - 0s 70us/step - loss: 1090944439330799616.0000
Epoch 6/10
100/100 [==============================] - 0s 20us/step - loss: 4564665060617919397888.0000
Epoch 7/10
100/100 [==============================] - 0s 30us/step - loss: 19099198494067630815576064.0000
Epoch 8/10
100/100 [==============================] - 0s 30us/step - loss: 79913699011849558249925771264.0000
Epoch 9/10
100/100 [==============================] - 0s 50us/step - loss: 334370041805433555342669660553216.0000
Epoch 10/10
100/100 [==============================] - 0s 20us/step - loss: 1399051141583436919510296595359858688.0000

Answer 1

您需要标准化输入功能。并且您可以学习How and why do normalization and feature scaling work?。让我在这里以(x-mean(x))/std(x)为例。

import numpy as np
from keras.layers import Dense
from keras.models import Sequential

data = np.asarray(range(100),dtype=np.float32)
model = Sequential([
    Dense(1, input_shape=(1,))
])

model.compile(optimizer='sgd', loss='mse')
model.fit((data-np.mean(data))/np.std(data), data, epochs=200, batch_size=100) 

Epoch 1/200
100/100 [==============================] - 3s 26ms/step - loss: 3284.6235
Epoch 2/200
100/100 [==============================] - 0s 25us/step - loss: 3154.5522
Epoch 3/200
100/100 [==============================] - 0s 22us/step - loss: 3029.6318
...
100/100 [==============================] - 0s 27us/step - loss: 1.1016
Epoch 200/200
100/100 [==============================] - 0s 28us/step - loss: 1.0579

基本示例中增加损失函数

1 个答案: