python实现DBN数据预测，输入输出数据均为三维

以下是使用Python实现DBN进行三维数据预测的示例代码：

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split

# 生成随机三维数据，输入为x，输出为y
x = np.random.rand(100, 10, 5)
y = np.random.rand(100, 1)

# 数据集分割
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# 定义网络参数
n_visible = 50
n_hidden_1 = 25
n_hidden_2 = 10

# 定义网络输入
x_input = tf.placeholder(tf.float32, [None, n_visible])

# 定义权重和偏置
W_1 = tf.Variable(tf.truncated_normal([n_visible, n_hidden_1], stddev=0.1))
b_1 = tf.Variable(tf.zeros([n_hidden_1]))
W_2 = tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2], stddev=0.1))
b_2 = tf.Variable(tf.zeros([n_hidden_2]))
W_3 = tf.Variable(tf.zeros([n_hidden_2, 1]))
b_3 = tf.Variable(tf.zeros([1]))

# 定义网络模型
def network(input):
    hidden_1 = tf.nn.sigmoid(tf.matmul(input, W_1) + b_1)
    hidden_2 = tf.nn.sigmoid(tf.matmul(hidden_1, W_2) + b_2)
    output = tf.matmul(hidden_2, W_3) + b_3
    return output

# 定义损失函数和优化器
y_pred = network(x_input)
y_true = tf.placeholder(tf.float32, [None, 1])
loss = tf.reduce_mean(tf.square(y_true - y_pred))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

# 定义评估模型的准确率
mse = tf.reduce_mean(tf.square(y_true - y_pred))

# 训练模型
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for i in range(1000):
    _, train_loss = sess.run([train_step, loss], feed_dict={x_input: x_train.reshape(-1, n_visible), y_true: y_train.reshape(-1, 1)})
    if i % 100 == 0:
        test_loss = sess.run(loss, feed_dict={x_input: x_test.reshape(-1, n_visible), y_true: y_test.reshape(-1, 1)})
        print("Epoch:", i, "Train loss:", train_loss, "Test loss:", test_loss)

# 评估模型
test_mse = sess.run(mse, feed_dict={x_input: x_test.reshape(-1, n_visible), y_true: y_test.reshape(-1, 1)})
print("Test MSE:", test_mse)

这个示例代码实现了一个两层的DBN，用于三维数据的预测。代码使用TensorFlow实现，将输入数据x展平为二维，定义网络结构、损失函数、优化器和评估指标，并进行训练和测试。在训练过程中，使用reshape()函数将输入数据x从三维转换为二维。最后，使用测试集评估模型的性能，计算MSE（均方误差）。