Keras 简明教程

Keras - Regression Prediction using MPL

在本章中，让我们编写一个简单的基于 MPL 的 ANN 来进行回归预测。到目前为止，我们只完成了基于分类的预测。现在，我们将尝试通过分析先前（连续）值及其影响因素来预测下一个可能的值。

In this chapter, let us write a simple MPL based ANN to do regression prediction. Till now, we have only done the classification based prediction. Now, we will try to predict the next possible value by analyzing the previous (continuous) values and its influencing factors.

回归 MPL 可以表示为以下内容：

The Regression MPL can be represented as below −

mpl

该模型的核心功能如下：

The core features of the model are as follows −

  1. Input layer consists of (13,) values.

  2. First layer, Dense consists of 64 units and ‘relu’ activation function with ‘normal’ kernel initializer.

  3. Second layer, Dense consists of 64 units and ‘relu’ activation function.

  4. Output layer, Dense consists of 1 unit.

  5. Use mse as loss function.

  6. Use RMSprop as Optimizer.

  7. Use accuracy as metrics.

  8. Use 128 as batch size.

  9. Use 500 as epochs.

Step 1 − Import the modules

Step 1 − Import the modules

让我们导入必需的模块。

Let us import the necessary modules.

import keras

from keras.datasets import boston_housing
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping
from sklearn import preprocessing
from sklearn.preprocessing import scale

Step 2 − Load data

Step 2 − Load data

让我们导入 Boston 住房数据集。

Let us import the Boston housing dataset.

(x_train, y_train), (x_test, y_test) = boston_housing.load_data()

在此，

Here,

boston_housing 是由Keras提供的用于数据集。它表示波士顿地区房价信息的一个集合，其中每条信息有13个特性。

boston_housing is a dataset provided by Keras. It represents a collection of housing information in Boston area, each having 13 features.

Step 3 − Process the data

Step 3 − Process the data

让我们根据我们的模型改变数据集，以便我们可以将它送入我们的模型。可以使用以下代码来改变数据−

Let us change the dataset according to our model, so that, we can feed into our model. The data can be changed using below code −

x_train_scaled = preprocessing.scale(x_train)
scaler = preprocessing.StandardScaler().fit(x_train)
x_test_scaled = scaler.transform(x_test)

在这里，我们已经使用 sklearn.preprocessing.scale 函数来规范化训练数据。 preprocessing.StandardScaler().fit 函数返回一个带有训练数据规范化均值和标准偏差的标量，我们可以使用 scalar.transform 函数将其应用于测试数据。这样会使用和训练数据中一样的设定来规范化测试数据。

Here, we have normalized the training data using sklearn.preprocessing.scale function. preprocessing.StandardScaler().fit function returns a scalar with the normalized mean and standard deviation of the training data, which we can apply to the test data using scalar.transform function. This will normalize the test data as well with the same setting as that of training data.

Step 4 − Create the model

Step 4 − Create the model

让我们创建实际模型。

Let us create the actual model.

model = Sequential()
model.add(Dense(64, kernel_initializer = 'normal', activation = 'relu',
input_shape = (13,)))
model.add(Dense(64, activation = 'relu')) model.add(Dense(1))

Step 5 − Compile the model

Step 5 − Compile the model

让我们使用选定的损失函数、优化器和指标来编译模型。

Let us compile the model using selected loss function, optimizer and metrics.

model.compile(
   loss = 'mse',
   optimizer = RMSprop(),
   metrics = ['mean_absolute_error']
)

Step 6 − Train the model

Step 6 − Train the model

让我们使用 fit() 方法训练模型。

Let us train the model using fit() method.

history = model.fit(
   x_train_scaled, y_train,
   batch_size=128,
   epochs = 500,
   verbose = 1,
   validation_split = 0.2,
   callbacks = [EarlyStopping(monitor = 'val_loss', patience = 20)]
)

在这里，我们使用了回调函数 EarlyStopping 。此回调函数的目的是监控每个时期中的损失值，并将其与前一个时期中的损失值进行对比，以寻找训练中的改进。如果 patience 次都没有改进，则会停止整个进程。

Here, we have used callback function, EarlyStopping. The purpose of this callback is to monitor the loss value during each epoch and compare it with previous epoch loss value to find the improvement in the training. If there is no improvement for the patience times, then the whole process will be stopped.

执行应用程序将给出如下的信息作为输出−

Executing the application will give the below information as output −

Train on 323 samples, validate on 81 samples Epoch 1/500 2019-09-24 01:07:03.889046: I
tensorflow/core/platform/cpu_feature_guard.cc:142]
Your CPU supports instructions that this
TensorFlow binary was not co mpiled to use: AVX2 323/323
[==============================] - 0s 515us/step - loss: 562.3129
- mean_absolute_error: 21.8575 - val_loss: 621.6523 - val_mean_absolute_erro
r: 23.1730 Epoch 2/500
323/323 [==============================] - 0s 11us/step - loss: 545.1666
- mean_absolute_error: 21.4887 - val_loss: 605.1341 - val_mean_absolute_error
: 22.8293 Epoch 3/500
323/323 [==============================] - 0s 12us/step - loss: 528.9944
- mean_absolute_error: 21.1328 - val_loss: 588.6594 - val_mean_absolute_error
: 22.4799 Epoch 4/500
323/323 [==============================] - 0s 12us/step - loss: 512.2739
- mean_absolute_error: 20.7658 - val_loss: 570.3772 - val_mean_absolute_error
: 22.0853 Epoch 5/500
323/323 [==============================] - 0s 9us/step - loss: 493.9775
- mean_absolute_error: 20.3506 - val_loss: 550.9548 - val_mean_absolute_error: 21.6547
..........
..........
..........
Epoch 143/500
323/323 [==============================] - 0s 15us/step - loss: 8.1004
- mean_absolute_error: 2.0002 - val_loss: 14.6286 - val_mean_absolute_error:
2. 5904 Epoch 144/500
323/323 [==============================] - 0s 19us/step - loss: 8.0300
- mean_absolute_error: 1.9683 - val_loss: 14.5949 - val_mean_absolute_error:
2. 5843 Epoch 145/500
323/323 [==============================] - 0s 12us/step - loss: 7.8704
- mean_absolute_error: 1.9313 - val_loss: 14.3770 - val_mean_absolute_error: 2. 4996

Step 7 − Evaluate the model

Step 7 − Evaluate the model

让我们使用测试数据评估模型。

Let us evaluate the model using test data.

score = model.evaluate(x_test_scaled, y_test, verbose = 0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

执行以上代码将会输出以下信息−

Executing the above code will output the below information −

Test loss: 21.928471583946077 Test accuracy: 2.9599233234629914

Step 8 − Predict

Step 8 − Predict

最后，如下预测使用测试数据−

Finally, predict using test data as below −

prediction = model.predict(x_test_scaled)
print(prediction.flatten())
print(y_test)

上述应用程序的输出如下 −

The output of the above application is as follows −

[ 7.5612316 17.583357 21.09344 31.859276 25.055613 18.673872 26.600405 22.403967 19.060272 22.264952
17.4191 17.00466 15.58924 41.624374 20.220217 18.985565 26.419338 19.837091 19.946192 36.43445
12.278508 16.330965 20.701359 14.345301 21.741161 25.050423 31.046402 27.738455 9.959419 20.93039
20.069063 14.518344 33.20235 24.735163 18.7274 9.148898 15.781284 18.556862 18.692865 26.045074
27.954073 28.106823 15.272034 40.879818 29.33896 23.714525 26.427515 16.483374 22.518442 22.425386
33.94826 18.831465 13.2501955 15.537227 34.639984 27.468002 13.474407 48.134598 34.39617
22.8503124.042334 17.747198 14.7837715 18.187277 23.655672 22.364983 13.858193 22.710032 14.371148
7.1272087 35.960033 28.247292 25.3014 14.477208 25.306196 17.891165 20.193708 23.585173 34.690193
12.200583 20.102983 38.45882 14.741723 14.408362 17.67158 18.418497 21.151712 21.157492 22.693687
29.809034 19.366991 20.072294 25.880817 40.814568 34.64087 19.43741 36.2591 50.73806 26.968863 43.91787
32.54908 20.248306 ] [ 7.2 18.8 19. 27. 22.2 24.5 31.2 22.9 20.5 23.2 18.6 14.5 17.8 50. 20.8 24.3 24.2
19.8 19.1 22.7 12. 10.2 20. 18.5 20.9 23. 27.5 30.1 9.5 22. 21.2 14.1 33.1 23.4 20.1 7.4 15.4 23.8 20.1
24.5 33. 28.4 14.1 46.7 32.5 29.6 28.4 19.8 20.2 25. 35.4 20.3 9.7 14.5 34.9 26.6 7.2 50. 32.4 21.6 29.8
13.1 27.5 21.2 23.1 21.9 13. 23.2 8.1 5.6 21.7 29.6 19.6 7. 26.4 18.9 20.9 28.1 35.4 10.2 24.3 43.1 17.6
15.4 16.2 27.1 21.4 21.5 22.4 25. 16.6 18.6 22. 42.8 35.1 21.5 36. 21.9 24.1 50. 26.7 25. ]

两个数组的输出大约有10-30%的差异，这表示我们的模型预测在合理范围内。

The output of both array have around 10-30% difference and it indicate our model predicts with reasonable range.