@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])
