h1 = tf.nn.sigmoid(tf.add(tf.matmul(input_x, weights['h1']), biases['b1'])) h1 = tf.nn.dropout(h1, keep_prob) h2 = tf.nn.sigmoid(tf.add(tf.matmul(h1, weights['h2']), biases['b2'])) h2 = tf.nn.dropout(h2, keep_prob) pred = tf.nn.sigmoid(tf.add(tf.matmul(h2, weights['out']), biases['out']))；这里采用了什么优化器

这段代码没有显示使用任何优化器，只有定义了神经网络的前向传播过程。如果要使用优化器进行训练，需要在定义完神经网络后，使用 `tf.train` 模块中的优化器来最小化损失函数。例如，可以使用 AdamOptimizer 进行优化，代码如下：

cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

相关问题

使用手写集数据集编写DBN的代码

以下是使用手写集数据集编写DBN的代码：

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义DBN的参数
n_visible = 784  # 输入层神经元个数
n_hidden_1 = 500  # 隐藏层1神经元个数
n_hidden_2 = 200  # 隐藏层2神经元个数
n_hidden_3 = 50  # 隐藏层3神经元个数
n_classes = 10  # 输出层神经元个数

# 定义输入和输出的占位符
x = tf.placeholder(tf.float32, [None, n_visible])
y = tf.placeholder(tf.float32, [None, n_classes])

# 定义权重和偏置
weights = {
    'encoder_h1': tf.Variable(tf.random_normal([n_visible, n_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])),
    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
    'decoder_h3': tf.Variable(tf.random_normal([n_hidden_1, n_visible])),
    'out': tf.Variable(tf.random_normal([n_hidden_3, n_classes]))
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),
    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_2])),
    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b3': tf.Variable(tf.random_normal([n_visible])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 定义编码器
def encoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2']))
    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']), biases['encoder_b3']))
    return layer_3

# 定义解码器
def decoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2']))
    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']), biases['decoder_b3']))
    return layer_3

# 定义分类器
def classifier(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(encoder(x), weights['out']), biases['out']))
    return layer_1

# 定义损失函数和优化器
encoded = encoder(x)
decoded = decoder(encoded)
y_pred = classifier(x)
cost = tf.reduce_mean(tf.square(x - decoded)) + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)

# 定义评估模型的准确率的函数
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(50):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / 100)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(100)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
            avg_cost += c / total_batch
        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
    print("Optimization Finished!")
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

希望这个代码对你有帮助！

深度信念网络python代码

以下是使用Python和深度学习框架TensorFlow实现深度信念网络的示例代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 导入MNIST数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# 定义模型结构
n_input = 784  # 输入层节点数
n_hidden_1 = 256  # 第一层隐藏层节点数
n_hidden_2 = 128  # 第二层隐藏层节点数
n_classes = 10  # 输出层节点数

# 定义输入占位符
x = tf.placeholder("float", [None, n_input])

# 定义权重和偏置变量
weights = {
    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b2': tf.Variable(tf.random_normal([n_input])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 定义编码器和解码器网络结构
def encoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2']))
    return layer_2

def decoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2']))
    return layer_2

# 定义模型输出
encoder_op = encoder(x)
decoder_op = decoder(encoder_op)
y_pred = tf.nn.softmax(tf.matmul(encoder_op, weights['out']) + biases['out'])

# 定义损失函数和优化器
y_true = x
cross_entropy = -tf.reduce_sum(y_true * tf.log(y_pred))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cross_entropy)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        batch_x, _ = mnist.train.next_batch(100)
        _, loss = sess.run([optimizer, cross_entropy], feed_dict={x: batch_x})
        if i % 100 == 0:
            print("Step: %d, Loss: %f" % (i, loss))
    # 测试模型
    correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y_true: mnist.test.images}))

以上代码实现了一个简单的自编码器，其中编码器和解码器均由两个隐藏层组成，最后输出层为Softmax层。训练过程中使用交叉熵作为损失函数，并使用Adam优化器进行优化。最后计算模型在测试集上的准确率。