广义线性模型结合神经网络python实现代码

下面是将广义线性模型（GLM）和神经网络结合起来的 Python 实现代码：

import tensorflow as tf
import numpy as np

# 定义广义线性模型的参数
n_features = 10
n_classes = 3
learning_rate = 0.01

# 定义神经网络的参数
n_hidden = 50
n_epochs = 1000
batch_size = 100

# 生成数据
X_train = np.random.rand(1000, n_features)
y_train = np.random.randint(0, n_classes, size=1000)
X_test = np.random.rand(100, n_features)
y_test = np.random.randint(0, n_classes, size=100)

# 定义输入占位符
X = tf.placeholder(tf.float32, shape=[None, n_features])
y = tf.placeholder(tf.int32, shape=[None])

# 定义广义线性模型的权重和偏置
W = tf.Variable(tf.random_normal([n_features, n_classes]), name='weights')
b = tf.Variable(tf.zeros([n_classes]), name='bias')

# 定义神经网络的权重和偏置
W_hidden = tf.Variable(tf.random_normal([n_features, n_hidden]), name='hidden_weights')
b_hidden = tf.Variable(tf.zeros([n_hidden]), name='hidden_bias')
W_output = tf.Variable(tf.random_normal([n_hidden, n_classes]), name='output_weights')
b_output = tf.Variable(tf.zeros([n_classes]), name='output_bias')

# 定义广义线性模型和神经网络的输出
logits_glm = tf.matmul(X, W) + b
logits_nn = tf.matmul(tf.nn.relu(tf.matmul(X, W_hidden) + b_hidden), W_output) + b_output
logits = logits_glm + logits_nn

# 定义损失函数
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
loss = tf.reduce_mean(cross_entropy)

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)

# 定义评估指标
correct_pred = tf.equal(tf.argmax(logits, 1), tf.cast(y, tf.int64))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(n_epochs):
        for i in range(0, len(X_train), batch_size):
            X_batch = X_train[i:i+batch_size]
            y_batch = y_train[i:i+batch_size]
            sess.run(train_op, feed_dict={X: X_batch, y: y_batch})
        if epoch % 100 == 0:
            acc_train = accuracy.eval(feed_dict={X: X_train, y: y_train})
            acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})
            print("Epoch:", epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)

这段代码定义了一个包含广义线性模型和神经网络的多分类分类器。其中，`X_train` 和 `y_train` 是用于训练的数据，`X_test` 和 `y_test` 是用于测试的数据。模型首先定义了输入占位符 `X` 和 `y`，然后分别定义了广义线性模型和神经网络的权重和偏置。接着，将两个模型的输出相加，并计算损失函数和优化器。最后，使用 `Session` 对象运行训练操作，并使用 `accuracy` 评估模型的性能。