#1:導入需要用到的模塊 import tensorflow as tf import tensorflow.contrib.layers as layers from sklearn import datasets import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler import pandas as pd import seaborn as sns''' 只有一個隱藏層的多層前饋網絡足以逼近任何函數 同時還可以保證很高的精度和令人滿意的效果 使用MLP（多層感知機進行函數逼近，預測波士頓房價） '''#2：加載數據集并創建Pandas數據幀來分析數據：boston = datasets.load_boston() df = pd.DataFrame(boston.data,columns=boston.feature_names) df['target'] = boston.target#3:了解一些數據的細節 df.describe()#4:三個參數RM,PTRATIO和LSTAT在幅度上輸出之間具有大于0.5的相關性 # 將數據集分解為訓練數據集和測試數據集。使用MinMaxScaler來規劃數據集 #由于神經網絡使用Sigmoid激活函數（Sigmoid的輸出只能在0-1之間） #所以必須對目標值進行歸一化x_train,x_test,y_train,y_test = train_test_split(df[['RM','LSTAT','PTRATIO']],df[['target']],test_size=0.3,random_state=0) #歸一化數據 X_train = MinMaxScaler().fit_transform(x_train) X_test = MinMaxScaler().fit_transform(x_test) Y_train = MinMaxScaler().fit_transform(y_train) Y_test = MinMaxScaler().fit_transform(y_test)#5:定義常量和超參數 m = len(X_train) n = 3 #特征的數量 n_hidden = 20 #隱藏層神經元的數量 #超參數 batch_size = 200 eta = 0.01 #學習率 max_epoch = 1000 #最大迭代數#6：創建一個單隱藏層的多層感知機模型 def multilayer_perceptron(x):fc1 = layers.fully_connected(x,n_hidden,activation_fn=tf.nn.relu,scope='fc1')out = layers.fully_connected(fc1,1,activation_fn=tf.sigmoid,scope='out')return out #7:聲明訓練數據的占位符并定義損失和優化器 x = tf.compat.v1.placeholder(tf.float32,name='X',shape=[m,n]) y = tf.compat.v1.placeholder(tf.float32,name='Y') y_hat = multilayer_perceptron(x) correct_prediction = tf.square(y-y_hat) mse = tf.reduce_mean(tf.cast(correct_prediction,'float')) train = tf.train.AdamOptimizer(learning_rate=eta).minimize(mse)#8:執行計算圖 init = tf.global_variables_initializer()with tf.compat.v1.Session() as sess:sess.run(init)writer = tf.summary.FileWriter('g3',sess.graph)# 訓練模型100次迭代for i in range(max_epoch):_, l,p = sess.run([train,mse,y_hat],feed_dict={x:X_train,y:Y_train})if i %100 ==0:print('Epoch {0}:Loss {1}'.format(i,l))print("Training Done")print('Optimization Finished!') #測試模型correct_prediction = tf.square(y-y_hat) #計算準確度accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float'))print('Mean ERROR:',accuracy.eval({x:X_train,y:Y_train}))plt.scatter(Y_train,p)writer.close()

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