手写数字识别程序,模型和测试图片
有训练代码和测试代码和我已经训练好的模型,还有几张我的测试图片
详情见我的博客:https://blog.csdn.net/qq_38269418/article/details/78991649
代码片段和文件信息
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 3602 2018-01-06 16:16 5.png
文件 3761 2018-01-06 16:21 6.png
文件 2460 2018-10-22 14:47 mnistdeep.py
文件 3808 2018-01-06 20:22 test.png
文件 2950 2018-10-25 14:28 test.py
文件 195 2018-01-06 11:54 SAVEcheckpoint
文件 39295616 2018-01-06 11:54 SAVEmodel.ckpt.data-00000-of-00001
文件 914 2018-01-06 11:54 SAVEmodel.ckpt.index
文件 72562 2018-01-06 11:54 SAVEmodel.ckpt.meta
文件 3525 2018-01-06 16:59 4.png
目录 0 2018-07-11 14:28 SAVE
----------- --------- ---------- ----- ----
39389393 11
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets(‘F:/DEEPLEARN/Anaconda/Lib/site-packages/tensorflow/examples/tutorials/mnist/MNIST_data‘ one_hot=True)
x = tf.placeholder(tf.float32 [None 784])
y_ = tf.placeholder(tf.float32 [None 10])
def weight_variable(shape):
initial = tf.truncated_normal(shapestddev = 0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1shape = shape)
return tf.Variable(initial)
def conv2d(xW):
return tf.nn.conv2d(x W strides = [1111] padding = ‘SAME‘)
def max_pool_2x2(x):
return tf.nn.max_pool(x ksize=[1221] strides=[1221] padding=‘SAME‘)
W_conv1 = weight_variable([5 5 1 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x[-128281])
h_conv1 = tf.nn.relu(conv2d(x_imageW_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
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)
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_conv1) tf.argmax(y_1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction “float“))
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
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})
saver.save(sess ‘C:/Users/mercheve/Desktop/SAVE/model.ckpt‘)
print(‘test accuracy %g‘ % accuracy.eval(feed_dict={
x: mnist.test.images y_: mnist.test.labels keep_prob: 1.0}))
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 3602 2018-01-06 16:16 5.png
文件 3761 2018-01-06 16:21 6.png
文件 2460 2018-10-22 14:47 mnistdeep.py
文件 3808 2018-01-06 20:22 test.png
文件 2950 2018-10-25 14:28 test.py
文件 195 2018-01-06 11:54 SAVEcheckpoint
文件 39295616 2018-01-06 11:54 SAVEmodel.ckpt.data-00000-of-00001
文件 914 2018-01-06 11:54 SAVEmodel.ckpt.index
文件 72562 2018-01-06 11:54 SAVEmodel.ckpt.me
文件 3525 2018-01-06 16:59 4.png
目录 0 2018-07-11 14:28 SAVE
----------- --------- ---------- ----- ----
39389393 11
版权声明:本文内容由互联网用户自发贡献,该文观点仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌抄袭侵权/违法违规的内容, 请发送邮件举报,一经查实,本站将立刻删除。
评论列表(条)