python 通过卷积神经网络做车牌识别代码

以下是使用Python实现车牌识别的卷积神经网络代码：

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)

# 定义超参数
learning_rate = 0.001
training_epochs = 15
batch_size = 100

# 定义输入和输出
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 将输入变为4D张量
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 第一层卷积
W_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]))
h_conv1 = tf.nn.relu(tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1], padding='SAME') + b_conv1)
h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 第二层卷积
W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]))
h_conv2 = tf.nn.relu(tf.nn.conv2d(h_pool1, W_conv2, strides=[1, 1, 1, 1], padding='SAME') + b_conv2)
h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 全连接层
W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout层，防止过拟合
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 输出层
W_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1))
b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]))
y_conv = 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_conv))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

# 定义评估准确率的操作
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 开始训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(training_epochs):
        for i in range(int(mnist.train.num_examples / batch_size)):
            batch = mnist.train.next_batch(batch_size)
            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))
            optimizer.run(feed_dict={x: batch[0], y: batch[1], keep_prob: 0.5})
        print("epoch %d finished" % epoch)
    print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0}))

以上代码使用卷积神经网络模型对MNIST数据集进行训练和测试，实现了手写数字的识别。你可以根据实际情况对代码进行修改，以实现车牌识别功能。