二维卷积神经网络 tensorflow实例

以下是一个简单的二维卷积神经网络的 TensorFlow 实现示例：

import tensorflow as tf

# 定义输入数据的占位符
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, 10])

# 定义第一层卷积核和偏置项
w_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]))

# 定义第二层卷积核和偏置项
w_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]))

# 定义全连接层的权重和偏置项
w_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))
w_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1))
b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]))

# 定义卷积和池化函数
def conv2d(x, w):
    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')

# 第一层卷积和池化
h_conv1 = tf.nn.relu(conv2d(x, w_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 第二层卷积和池化
h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 将卷积层输出的张量展平
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])

# 全连接层的计算
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=0.5)
y_ = tf.matmul(h_fc1_drop, w_fc2) + b_fc2

# 定义损失函数和优化器
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 定义评估模型准确度的操作
correct_prediction = tf.equal(tf.argmax(y_, 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 i in range(1000):
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x: batch[0], y: batch[1], keep_prob: 1.0})
            print('step %d, training accuracy %g' % (i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y: batch[1], keep_prob: 0.5})

    # 在测试集上评估模型准确度
    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0}))

该模型包含两层卷积和池化层以及两个全连接层。在训练过程中，采用了交叉熵作为损失函数，Adam 优化器进行优化，同时使用 dropout 技术进行正则化。在测试集上，可以通过计算准确度评估模型的性能。