基于tensorflow2.x版本python代码实现深度学习回归预测（以lstm为例）

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基于tensorflow2.x版本python代码实现深度学习回归预测（以lstm为例）

代码实现（能直接跑通本文中的代码）

import  os
import  tensorflow as tf
import  numpy as np
from tensorflow import keras
from general import *

#import  tensorflow as tf
#from tensorflow import keras
from keras.models import Sequential
from keras.layers import *
#from general import *

tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')

import numpy as np
import pandas as pd
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers

from sklearn import datasets
from sklearn.cross_validation import train_test_split


#生成回归训练集
def generate_regression_train_data():
    dataset_path = keras.utils.get_file("auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
    dataset_path
    column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
                'Acceleration', 'Model Year', 'Origin']
    raw_dataset = pd.read_csv(dataset_path, names=column_names,
                          na_values = "?", comment='\t',
                          sep=" ", skipinitialspace=True)
    dataset = raw_dataset.copy()
    dataset = dataset.dropna()
    origin = dataset.pop('Origin')
    dataset['USA'] = (origin == 1)*1.0
    dataset['Europe'] = (origin == 2)*1.0
    dataset['Japan'] = (origin == 3)*1.0
    
    train_dataset = dataset.sample(frac=0.8,random_state=0)
    test_dataset = dataset.drop(train_dataset.index)
    train_stats = train_dataset.describe()
    train_stats.pop("MPG")
    train_stats = train_stats.transpose()
    y_train = train_dataset.pop('MPG')
    y_test = test_dataset.pop('MPG')
    def norm(x):
        return (x - train_stats['mean']) / train_stats['std']
    
    X_train = norm(train_dataset)
    X_test = norm(test_dataset)
    x_train = np.array(X_train)
    x_test = np.array(X_test)
    y_train = np.array(y_train)
    y_test = np.array(y_test)
    return x_train, y_train, x_test, y_test



#Func构建方式
class FuncMRegressor(keras.Model):
    def __init__(self, units, num_classes, num_layers):
        super(FuncMRegressor, self).__init__()

        # self.cells = [keras.layers.LSTMCell(units) for _ in range(num_layers)]
        #
        # self.rnn = keras.layers.RNN(self.cells, unroll=True)
        self.rnn = keras.layers.LSTM(units, return_sequences=True)
                        # return_sequences设置lstm中单个细胞中返回时间点的类型。非最后一层，return_sequences=True才能满足三维输入的数据格式要求
                        #默认 False。在输出序列中，返回单个 hidden state值还是返回全部time step 的 hidden state值。 False 返回单个， true 返回全部。
                    
        self.rnn2 = keras.layers.LSTM(units) #最后一层，return_sequences=False，对于单个样本而言，返回的是一维（对于所有样本是二维）
        
#         self.fc1 = layers.Dense(64, activation='relu')
#         self.fc2 = layers.Dense(64, activation='relu')

        # self.cells = (keras.layers.LSTMCell(units) for _ in range(num_layers))
        # 
        # self.rnn = keras.layers.RNN(self.cells, return_sequences=True, return_state=True)
        # self.rnn = keras.layers.LSTM(units, unroll=True)
        # self.rnn = keras.layers.StackedRNNCells(self.cells)

        # have 1000 words totally, every word will be embedding into 100 length vector
        # the max sentence lenght is 80 words
#         self.embedding = keras.layers.Embedding(1000, 100, input_length=9)  
                            #指定隐含层的shape-->[top_words,100]，input_length参数是最初输入的二维矩阵的维度。
                            # 这样，根据隐含层原理，隐含层输出的数据shape为-->[None,input_length,100]=[None,80,100]
#         self.soft_max = keras.layers.Softmax()
#         self.fc1 = keras.layers.Dense(32, activation='relu')   

        self.fc3 = keras.layers.Dense(1)  #最后一层全连接层。对于N分类问题，最后一层全连接输出个数为N个；对于回归问题，最后一层全连接层的输出为1
   
    #定义输入输出
    def call(self, inputs, training=None, mask=None):  ###*** 该函数重写了RNN的call()方法，该方法是模型输出表达式（即，模型）
        #print('y', inputs.shape)
        #1 利用隐含层将二维数组变为三维数组
        #    [None,80]-->[None,80,100]
        
        # [b, sentence len] => [b, sentence len, word embedding]
#         y = self.embedding(inputs)  #用隐含层的目的是让本来二维的数据变成三维输入到lstm中（大多数的深度学习算法都要求输入的数据是三维，对于传统的如房价预测，需要将二维数组通过一定方法转换为3维数组）
#         print("y1 shape:",y.shape)
        #2 得到了满足lstm（大多数深度学习算法）输入的三维数据格式后，搭建lstm网络
        
        #搭建神经网络：可以根据需要搭建输入、多层结构层、dropout、激活函数、池化等
        y = self.rnn(inputs)   #要求输入到lstm模型中的数据必须是三维
        #print("y2 shape:", y.shape)  
        y = self.rnn2(y) 
        # print('rnn', x.shape)
        #3 确定输出。
        #y = self.fc1(y)   #全连接层
        #print(y.shape)
        #y = self.fc2(y)
        y = self.fc3(y)
        return y

#Seque构建方式（推荐）
class SequeRegressor():
    def __init__(self, units):
        self.units = units
        self.model = None
     
    #构建神经网络模型：（根据各层输入输出的shape）搭建网络结构、确定损失函数、确定优化器
    def build_model(self, loss, optimizer, metrics):
        self.model = Sequential()
        self.model.add(LSTM(self.units, return_sequences=True))
        self.model.add(LSTM(self.units))
        self.model.add(Dense(1))
        
        self.model.compile(loss=loss, 
              optimizer=optimizer,   #优化器的选择很重要
              metrics=metrics)
    
    

if __name__ == "__main__":
    print("**********************【Func搭建方式】********************")
    # 获取二维数组（特征提取过的）
    x_train, y_train, x_test, y_test = generate_regression_train_data()
    # 把二维数组改为三维数组（通过np.newaxis随机增加一维数组）
    x_train = x_train[:, :, np.newaxis]
    x_test = x_test[:, :, np.newaxis]
    
    #训练模型
    units = 64   #细胞个数
    num_classes = 1  #回归问题，该参数为1
    batch_size = 32
    epochs = 35
    model = FuncMRegressor(units, num_classes, num_layers=2)
    optimizer = tf.keras.optimizers.RMSprop(0.001)
    
    model.compile(loss='mse',
                optimizer=optimizer,
                metrics=['mae', 'mse'])
    
    
    # model.compile(optimizer=keras.optimizers.Adam(0.001),
    #                   loss=keras.losses.BinaryCrossentropy(from_logits=False),
    #                   metrics=['accuracy', 'mse'])
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
                  validation_data=(x_test, y_test), verbose=1)
    
    # 模型应用预测
    #model.predict只返回y_pred
    out = model.predict(x_train)
    #print("out:", out)
    #evaluate用于评估您训练的模型。它的输出是准确度或损失，而不是对输入数据的预测。
    scores = model.evaluate(x_test, y_test, batch_size, verbose=1)
    print("Final test loss and accuracy :", scores)
    
    
    print("**********************【Seque搭建方式】********************")
    #1 获取训练数据集，并调整为三维输入格式
    x_train, y_train, x_test, y_test = generate_regression_train_data()
    x_train = x_train[:, :, np.newaxis]
    x_test = x_test[:, :, np.newaxis]
    
    #2 构建神经网络模型：（根据各层输入输出的shape）搭建网络结构、确定损失函数、确定优化器
    units = 64 #lstm细胞个数
    loss = "mse"  #损失函数类型
    optimizer = tf.keras.optimizers.RMSprop(0.001)  #优化器类型
    metrics = ['mae', 'mse']  #评估方法类型
    srlstm = SequeRegressor(units) 
    srlstm.build_model(loss, optimizer, metrics)
    
    #3 训练模型
    epochs = 35
    batch_size = 32
    srlstm.model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size)
    
    #4模型评估
    score = srlstm.model.evaluate(x_test, y_test, batch_size=16)
    print("model score:", score)
    
    #5 模型应用：预测
    prediction = srlstm.model.predict(x_test)

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