【小白学习keras教程】一、基于波士顿住房数据集训练简单的MLP回归模型

@Author：Runsen

多层感知机（MLP）有着非常悠久的历史，多层感知机(MLP)是深度神经网络(DNN)的基础算法

MLP基础知识

目的：创建用于简单回归/分类任务的常规神经网络（即多层感知器）和Keras

MLP结构

每个MLP模型由一个输入层、几个隐藏层和一个输出层组成

每层神经元的数目不受限制

回归任务的MLP

当目标（y）连续时

对于损失函数和评估指标，通常使用均方误差（MSE）

from tensorflow.keras.datasets import boston_housing (X_train, y_train), (X_test, y_test) = boston_housing.load_data()

数据集描述

波士顿住房数据集共有506个数据实例（404个培训和102个测试）

13个属性（特征）预测“某一地点房屋的中值”

文件编号：https://keras.io/datasets/

1.创建模型

Keras模型对象可以用Sequential类创建

一开始，模型本身是空的。它是通过添加附加层和编译来完成的

文档：https://keras.io/models/sequential/

from tensorflow.keras.models import Sequential model = Sequential()

1-1.添加层

Keras层可以添加到模型中

添加层就像一个接一个地堆叠乐高积木

文档：https://keras.io/layers/core/

from tensorflow.keras.layers import Activation, Dense # Keras model with two hidden layer with 10 neurons each model.add(Dense(10, input_shape = (13,))) # Input layer => input_shape should be explicitly designated model.add(Activation('sigmoid')) model.add(Dense(10)) # Hidden layer => only output dimension should be designated model.add(Activation('sigmoid')) model.add(Dense(10)) # Hidden layer => only output dimension should be designated model.add(Activation('sigmoid')) model.add(Dense(1)) # Output layer => output dimension = 1 since it is regression problem # This is equivalent to the above code block model.add(Dense(10, input_shape = (13,), activation = 'sigmoid')) model.add(Dense(10, activation = 'sigmoid')) model.add(Dense(10, activation = 'sigmoid')) model.add(Dense(1))

1-2.模型编译

Keras模型应在培训前“编译”

应指定损失类型（函数）和优化器

文档（优化器）：https://keras.io/optimizers/

文档（损失）：https://keras.io/losses/

from tensorflow.keras import optimizers sgd = optimizers.SGD(lr = 0.01) # stochastic gradient descent optimizer model.compile(optimizer = sgd, loss = 'mean_squared_error', metrics = ['mse']) # for regression problems, mean squared error (MSE) is often employed

模型摘要

model.summary()

odel: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 10) 140 _________________________________________________________________ activation (Activation) (None, 10) 0 _________________________________________________________________ dense_1 (Dense) (None, 10) 110 _________________________________________________________________ activation_1 (Activation) (None, 10) 0 _________________________________________________________________ dense_2 (Dense) (None, 10) 110 _________________________________________________________________ activation_2 (Activation) (None, 10) 0 _________________________________________________________________ dense_3 (Dense) (None, 1) 11 _________________________________________________________________ dense_4 (Dense) (None, 10) 20 _________________________________________________________________ dense_5 (Dense) (None, 10) 110 _________________________________________________________________ dense_6 (Dense) (None, 10) 110 _________________________________________________________________ dense_7 (Dense) (None, 1) 11 ================================================================= Total params: 622 Trainable params: 622 Non-trainable params: 0 _________________________________________________________________

2.培训

使用提供的训练数据训练模型

model.fit(X_train, y_train, batch_size = 50, epochs = 100, verbose = 1)

3.评估

Keras模型可以用evaluate（）函数计算

评估结果包含在列表中

文档：https://keras.io/metrics/

results = model.evaluate(X_test, y_test)

print(model.metrics_names) # list of metric names the model is employing print(results) # actual figure of metrics computed

print('loss: ', results[0]) print('mse: ', results[1])

Keras 机器学习

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