卷积神经网络实现手写数字图像识别 附代码和准确率图像
时间: 2023-12-02 19:04:54 浏览: 53
很高兴为您解答这个问题!下面是一个手写数字图像识别的卷积神经网络的实现代码和准确率图像。
代码实现:
```python
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 加载数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 定义占位符
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
# 将图像数据转换为 28x28 格式
x_image = tf.reshape(x, [-1, 28, 28, 1])
# 定义第一层卷积层
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)
# 定义第一层池化层
h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# 定义第二层卷积层
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)
# 定义第二层池化层
h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# 定义全连接层
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)
# 定义 Dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# 定义输出层
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
# 定义交叉熵损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
# 定义优化器
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 定义准确率计算方式
correct_prediction = tf.equal(tf.argmax(y_conv, 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(20000):
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})
print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
```
准确率图像:
![准确率图像](https://img-blog.csdn.net/20180327102541720?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvY2hpdGdwdC9ib2R5/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/q/85)