TensorFlow CNN Implementation Guide

Here is a basic code example for implementing a convolutional neural network using TensorFlow.

import tensorflow as tf

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

# 定义卷积神经网络的结构
conv1 = tf.layers.conv2d(inputs=x, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

flat = tf.layers.flatten(pool2)
dense = tf.layers.dense(inputs=flat, units=1024, activation=tf.nn.relu)
logits = tf.layers.dense(inputs=dense, units=10)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

# 训练模型
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# 在这里可以加载数据集并进行训练
# 这里省略了数据加载和训练过程

# 测试模型
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 在这里可以加载测试数据并计算准确率
# 这里省略了测试数据加载和准确率计算过程

This is a simple convolutional neural network model that can be modified and expanded based on your own dataset and task requirements. You can adjust the network structure, loss functions, and optimizers according to your specific needs. Hope this is helpful to you!