用tensorflow搭建简单神经网络实现mnist分类

首先，我们需要导入必要的库和数据集：

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

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

接下来，我们需要定义输入和输出的占位符：

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

然后，我们需要定义权重和偏置：

weights = tf.Variable(tf.zeros([784, 10]))
biases = tf.Variable(tf.zeros([10]))

接着，我们需要定义模型：

logits = tf.matmul(x, weights) + biases

然后，我们需要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=logits))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

最后，我们需要训练模型：

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

for _ 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(logits, 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])

weights = tf.Variable(tf.zeros([784, 10]))
biases = tf.Variable(tf.zeros([10]))

logits = tf.matmul(x, weights) + biases

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

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

for _ 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(logits, 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}))