深度学习MNIST手写数字识别

最新推荐文章于 2025-02-20 15:24:52 发布

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最新推荐文章于 2025-02-20 15:24:52 发布

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分类专栏： python tensorflow mnist 深度学习 deep learn

本文链接：https://blog.youkuaiyun.com/seTaire/article/details/85007941

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本文介绍了一种使用TensorFlow实现的手写数字识别模型。该模型采用两层卷积神经网络，通过训练MNIST数据集，实现了高精度的数字识别。文章详细展示了模型的构建过程，包括卷积层、池化层、全连接层的设计，以及损失函数、优化器的选择。

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python3.6

tensorflow1.12.0

# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
x = tf.placeholder("float",[None,784])

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

y_ = tf.placeholder("float",[None,10])

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial)
  
  
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2,1], padding='SAME')

x_image = tf.reshape( x,shape=[-1,28,28,1] )

# 第 1 层卷积
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])


h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


# 第 2 层卷积
W_conv2 = weight_variable([5, 5, 32, 64]) 
b_conv2 = bias_variable([64])
  
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 密集连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024]) 
b_fc1 = bias_variable([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)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10])
y_conv= tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    for i in range(2000):
        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}))