深度置信网络 特征提取

以下是使用Python和TensorFlow实现

的代码：

```python

import tensorflow as tf

import numpy as np

class

(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：  ```python 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

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。

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