利用TensorFlow实现识别手写数字

以下是一个简单的示例，使用TensorFlow来实现手写数字的识别：

1. 导入必要的库

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

2. 加载MNIST数据集

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

3. 构建模型

x = tf.placeholder(tf.float32, [None, 784])  # 输入层
y_ = tf.placeholder(tf.float32, [None, 10])  # 输出层

W = tf.Variable(tf.zeros([784, 10]))  # 权重
b = tf.Variable(tf.zeros([10]))  # 偏置

y = tf.nn.softmax(tf.matmul(x, W) + b)  # 模型输出

4. 定义损失函数和优化器

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))  # 交叉熵损失函数
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)  # 梯度下降优化器

5. 训练模型

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

6. 评估模型

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

完整代码如下：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

输出结果为识别准确率，大约为91%。