dbn 深度置信网络代码

以下是使用Python和TensorFlow实现DBN深度置信网络的代码：

import tensorflow as tf
import numpy as np

class DBN(object):
    def __init__(self, n_layers, n_nodes, learning_rate=0.01, batch_size=100, n_epochs=10):
        self.n_layers = n_layers
        self.n_nodes = n_nodes
        self.learning_rate = learning_rate
        self.batch_size = batch_size
        self.n_epochs = n_epochs
        self.weights = []
        self.biases = []
        self.build_model()
        
    def build_model(self):
        self.X = tf.placeholder(tf.float32, shape=[None, self.n_nodes[0]])
        self.y = tf.placeholder(tf.float32, shape=[None, self.n_nodes[-1]])
        # 构建每一层的权重和偏置
        for i in range(self.n_layers-1):
            w = tf.Variable(tf.random_normal([self.n_nodes[i], self.n_nodes[i+1]], stddev=0.1), name='w'+str(i))
            b = tf.Variable(tf.zeros([self.n_nodes[i+1]]), name='b'+str(i))
            self.weights.append(w)
            self.biases.append(b)
        # 构建前向传播过程
        layers = [self.X]
        for i in range(self.n_layers-1):
            layer = tf.nn.sigmoid(tf.add(tf.matmul(layers[-1], self.weights[i]), self.biases[i]))
            layers.append(layer)
        self.y_pred = layers[-1]
        # 构建反向传播过程
        self.loss = tf.reduce_mean(tf.square(self.y - self.y_pred))
        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)
        self.init_op = tf.global_variables_initializer()
        
    def fit(self, X_train, y_train):
        n_samples = X_train.shape[0]
        with tf.Session() as sess:
            sess.run(self.init_op)
            for epoch in range(self.n_epochs):
                total_loss = 0
                for i in range(n_samples // self.batch_size):
                    batch_X = X_train[i*self.batch_size: (i+1)*self.batch_size]
                    batch_y = y_train[i*self.batch_size: (i+1)*self.batch_size]
                    _, loss = sess.run([self.optimizer, self.loss], feed_dict={self.X: batch_X, self.y: batch_y})
                    total_loss += loss
                avg_loss = total_loss / (n_samples // self.batch_size)
                print("Epoch:", epoch+1, "Loss:", avg_loss)
            self.weights = sess.run(self.weights)
            self.biases = sess.run(self.biases)
        
    def predict(self, X_test):
        with tf.Session() as sess:
            sess.run(self.init_op)
            for i in range(self.n_layers-1):
                sess.run([self.weights[i].assign(self.weights[i]), self.biases[i].assign(self.biases[i])])
            y_pred = sess.run(self.y_pred, feed_dict={self.X: X_test})
        return y_pred

使用方法：

import numpy as np
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# 加载数据并进行预处理
data = load_boston()
X = data.data
y = data.target.reshape(-1, 1)
X = StandardScaler().fit_transform(X)
y = StandardScaler().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 构建DBN模型
dbn = DBN(n_layers=3, n_nodes=[13, 10, 1], learning_rate=0.01, batch_size=100, n_epochs=50)
dbn.fit(X_train, y_train)

# 预测
y_pred = dbn.predict(X_test)

其中，`data`是加载的数据集，`X`是特征，`y`是目标变量。首先进行标准化处理，然后将数据划分为训练集和测试集。接着构建DBN模型，其中`n_layers`是层数，`n_nodes`是每一层的节点数，`learning_rate`是学习率，`batch_size`是批次大小，`n_epochs`是训练轮数。调用`fit`方法进行训练，调用`predict`方法进行预测。