DBN python代码实现回归预测

DBN（Deep Belief Network）是一种深度学习模型，用于无监督学习和特征学习。在Python中，可以使用Theano和Pylearn2库来实现DBN。下面是一个简单的DBN回归预测的Python代码示例：

import numpy as np
import theano
import theano.tensor as T
import pylearn2
from pylearn2.models import dbn
from pylearn2.models.mlp import Sigmoid, Linear
from pylearn2.training_algorithms import sgd

# 创建训练数据和标签
train_data = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
train_labels = np.array([3, 7, 11, 15])

# 转换为Theano共享变量
train_data = theano.shared(train_data)
train_labels = theano.shared(train_labels)

# 创建DBN模型
hidden_layer_sizes = [10, 10]  # 隐藏层大小
layers = []
for i in range(len(hidden_layer_sizes)):
    if i == 0:
        input_size = train_data.get_value().shape[1]
        layer_input = train_data
    else:
        input_size = hidden_layer_sizes[i-1]
        layer_input = layers[-1].h

    layer = dbn.HiddenLayer(
        input_size=input_size,
        output_size=hidden_layer_sizes[i],
        activation=Sigmoid()
    )
    layers.append(layer)

output_layer = dbn.HiddenLayer(
    input_size=hidden_layer_sizes[-1],
    output_size=1,
    activation=Linear()
)
layers.append(output_layer)

# 定义DBN模型
dbn_model = dbn.DBN(layers)

# 定义训练算法
trainer = sgd.SGD(
    learning_rate=0.1,
    batch_size=1,
    learning_rule=sgd.Momentum(0.9)
)

# 训练DBN模型
trainer.setup(dbn_model, train_data)
trainer.train(dataset=train_data, targets=train_labels)

# 使用训练好的模型进行预测
test_data = np.array([[9, 10]])
test_data = theano.shared(test_data)
prediction = dbn_model.fprop(test_data)

print("预测结果:", prediction.eval())