Python中如何实现深度置信网络算法进行查找

import numpy as np
import tensorflow as tf

data = np.array([['pidancode.com', 1],
          ['皮蛋编程', 2],
          ['pidancode.com', 3],
          ['皮蛋编程', 4]])

def to_one_hot(data):
    d = {}
    for i, val in enumerate(set(data)):
        tmp = np.zeros((1, len(set(data))))
        tmp[0][i] = 1
        d[val] = tmp
    return np.array([d[val] for val in data])

one_hot_data = np.hstack((to_one_hot(data[:, 0]), data[:, 1].reshape((-1,1))))

n_hidden_1 = 10 # hidden layer 1
n_input = one_hot_data.shape[1]
n_output = 1

weights = {
    'h1': tf.Variable(tf.random.normal([n_input, n_hidden_1])),
    'out': tf.Variable(tf.random.normal([n_hidden_1, n_output]))
}

biases = {
    'h1': tf.Variable(tf.random.normal([n_hidden_1])),
    'out': tf.Variable(tf.random.normal([n_output]))
}

def deep_belief_network(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['h1']), biases['h1']))
    output = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['out']), biases['out']))
    return output

learning_rate = 0.01
batch_size = 2
epochs = 1000
display_step = 100

def binary_crossentropy(y, pred):
    return -tf.reduce_mean(y * tf.log(pred) + (1-y) * tf.log(1-pred))

optimizer = tf.optimizers.Adam(learning_rate)

def train(x):
    with tf.GradientTape() as tape:
        pred = deep_belief_network(x)
        loss = binary_crossentropy(one_hot_data[:,1], pred)
    grads = tape.gradient(loss, list(weights.values())+list(biases.values()))
    optimizer.apply_gradients(zip(grads, list(weights.values())+list(biases.values())))

for epoch in range(epochs):
    avg_cost = 0.
    total_batch = int(one_hot_data.shape[0]/batch_size)
    for i in range(total_batch):
        batch_x = one_hot_data[i*batch_size:(i+1)*batch_size,:-1]
        train(batch_x)
        c = binary_crossentropy(one_hot_data[:,1], deep_belief_network(one_hot_data[:,:-1]))
        avg_cost += c / total_batch

    if epoch % display_step == 0:
        print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

def predict(x):
    return deep_belief_network(x)

x = np.hstack((to_one_hot(['pidancode.com']), 0))
print(predict(x))