[TensorFlow]minist学习

qq_18892629

于 2019-07-07 21:50:11 发布

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本文链接：https://blog.youkuaiyun.com/qq_18892629/article/details/95032909

本教程通过使用TensorFlow框架实现了一个简单的线性模型，用于识别MNIST数据集中的手写数字。代码中详细展示了从数据加载到模型训练及评估的全过程，包括初始化变量、定义模型、设置损失函数、执行训练步骤以及计算准确率。

入门代码：

import input_data
import tensorflow as tf
 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSession()
 

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
 
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x,W) + b)
 

cross_entropy = -tf.reduce_sum(y_*tf.log(y))
 

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
 

for i in range(1000):
  batch = mnist.train.next_batch(50)
  train_step.run(feed_dict={x: batch[0], y_: batch[1]})
 

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