TensorFlow笔记之MNIST例程详解

最新推荐文章于 2025-07-03 09:25:47 发布

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最新推荐文章于 2025-07-03 09:25:47 发布 · 1.9w 阅读

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#mnist #tensorflow

这篇博客详细解析了使用TensorFlow进行MNIST手写数字识别的步骤，包括数据导入、模型定义、优化方法设置、模型训练及评估。通过理解这个例子，读者可以更好地掌握深度学习的基础操作。

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MNIST被称为这方面的HelloWorld，此程序修改自github

程序如下

# -*- coding:utf-8 -*-
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

dir='/home/kaka/Documents/input_data'
# 1.Import data
mnist = input_data.read_data_sets(dir, one_hot=True)

#Print the shape of mist
print (mnist.train.images.shape,mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.train.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)

# 2.Create the model
# y=wx+b
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])

# The raw formulation of cross-entropy,
#
#   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
#                                 reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

# Init model
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for i in range(100000):
    batch_xs, batch_ys = mnist.train.next_batch(10