基于Tensorflow的MNIST手写数字识别分类

本文实例为大家分享了基于Tensorflow的MNIST手写数字识别分类的具体实现代码，供大家参考，具体内容如下

代码如下：

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector
import time

IMAGE_PIXELS = 28
hidden_unit = 100
output_nums = 10
learning_rate = 0.001
train_steps = 50000
batch_size = 500
test_data_size = 10000
#日志目录(这里根据自己的目录修改)
logdir = 'D:/Develop_Software/Anaconda3/WorkDirectory/summary/mnist'
#导入mnist数据
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)

 #全局训练步数
global_step = tf.Variable(0, name = 'global_step', trainable = False)
with tf.name_scope('input'):
 #输入数据
 with tf.name_scope('x'):
 x = tf.placeholder(
  dtype = tf.float32, shape = (None, IMAGE_PIXELS * IMAGE_PIXELS))
 #收集x图像的会总数据
 with tf.name_scope('x_summary'):
 shaped_image_batch = tf.reshape(
  tensor = x,
  shape = (-1, IMAGE_PIXELS, IMAGE_PIXELS, 1),
  name = 'shaped_image_batch')
 tf.summary.image(name = 'image_summary',
      tensor = shaped_image_batch,
      max_outputs = 10)
 with tf.name_scope('y_'):
 y_ = tf.placeholder(dtype = tf.float32, shape = (None, 10))

with tf.name_scope('hidden_layer'):
 with tf.name_scope('hidden_arg'):
 #隐层模型参数
 with tf.name_scope('hid_w'):
  
  hid_w = tf.Variable(
   tf.truncated_normal(shape = (IMAGE_PIXELS * IMAGE_PIXELS, hidden_unit)),
   name = 'hidden_w')
  #添加获取隐层权重统计值汇总数据的汇总操作
  tf.summary.histogram(name = 'weights', values = hid_w)
  with tf.name_scope('hid_b'):
  hid_b = tf.Variable(tf.zeros(shape = (1, hidden_unit), dtype = tf.float32),
       name = 'hidden_b')
 #隐层输出
 with tf.name_scope('relu'):
 hid_out = tf.nn.relu(tf.matmul(x, hid_w) + hid_b)
with tf.name_scope('softmax_layer'):
 with tf.name_scope('softmax_arg'):
 #softmax层参数
 with tf.name_scope('sm_w'):
  
  sm_w = tf.Variable(
   tf.truncated_normal(shape = (hidden_unit, output_nums)),
   name = 'softmax_w')
  #添加获取softmax层权重统计值汇总数据的汇总操作
  tf.summary.histogram(name = 'weights', values = sm_w)
  with tf.name_scope('sm_b'):
  sm_b = tf.Variable(tf.zeros(shape = (1, output_nums), dtype = tf.float32), 
       name = 'softmax_b')
 #softmax层的输出
 with tf.name_scope('softmax'):
 y = tf.nn.softmax(tf.matmul(hid_out, sm_w) + sm_b)
 #梯度裁剪，因为概率取值为[0, 1]为避免出现无意义的log(0)，故将y值裁剪到[1e-10, 1]
 y_clip = tf.clip_by_value(y, 1.0e-10, 1 - 1.0e-5)
with tf.name_scope('cross_entropy'):
 #使用交叉熵代价函数
 cross_entropy = -tf.reduce_sum(y_ * tf.log(y_clip) + (1 - y_) * tf.log(1 - y_clip))
 #添加获取交叉熵的汇总操作
 tf.summary.scalar(name = 'cross_entropy', tensor = cross_entropy)
 
with tf.name_scope('train'):
 #若不使用同步训练机制，使用Adam优化器
 optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)
 #单步训练操作，
 train_op = optimizer.minimize(cross_entropy, global_step = global_step)
#加载测试数据
test_image = mnist.test.images
test_label = mnist.test.labels
test_feed = {x:test_image, y_:test_label}

with tf.name_scope('accuracy'):
 prediction = tf.equal(tf.argmax(input = y, axis = 1),
      tf.argmax(input = y_, axis = 1))
 accuracy = tf.reduce_mean(
  input_tensor = tf.cast(x = prediction, dtype = tf.float32))
#创建嵌入变量
embedding_var = tf.Variable(test_image, trainable = False, name = 'embedding')
saver = tf.train.Saver({'embedding':embedding_var})
#创建元数据文件，将MNIST图像测试集对应的标签写入文件
def CreateMedaDataFile():
 with open(logdir + '/metadata.tsv', 'w') as f:
 label = np.nonzero(test_label)[1]
 for i in range(test_data_size):
  f.write('%d\n' % label[i])
#创建投影配置参数
def CreateProjectorConfig():
 config = projector.ProjectorConfig()
 embeddings = config.embeddings.add()
 embeddings.tensor_name = 'embedding:0'
 embeddings.metadata_path = logdir + '/metadata.tsv'
 
 projector.visualize_embeddings(writer, config)
 #聚集汇总操作
merged = tf.summary.merge_all()
#创建会话的配置参数
sess_config = tf.ConfigProto(
 allow_soft_placement = True,
 log_device_placement = False)
#创建会话
with tf.Session(config = sess_config) as sess:
 #创建FileWriter实例
 writer = tf.summary.FileWriter(logdir = logdir, graph = sess.graph)
 #初始化全局变量
 sess.run(tf.global_variables_initializer())
 time_begin = time.time()
 print('Training begin time: %f' % time_begin)
 while True:
 #加载训练批数据
 batch_x, batch_y = mnist.train.next_batch(batch_size)
 train_feed = {x:batch_x, y_:batch_y}
 loss, _, summary= sess.run([cross_entropy, train_op, merged], feed_dict = train_feed)
 step = global_step.eval()
 #如果step为100的整数倍
 if step % 100 == 0:
  now = time.time()
  print('%f: global_step = %d, loss = %f' % (
   now, step, loss))
  #向事件文件中添加汇总数据
  writer.add_summary(summary = summary, global_step = step)
 #若大于等于训练总步数，退出训练
 if step >= train_steps:
  break
 time_end = time.time()
 print('Training end time: %f' % time_end)
 print('Training time: %f' % (time_end - time_begin))
 #测试模型精度
 test_accuracy = sess.run(accuracy, feed_dict = test_feed)
 print('accuracy: %f' % test_accuracy)
 
 saver.save(sess = sess, save_path = logdir + '/embedding_var.ckpt')
 CreateMedaDataFile()
 CreateProjectorConfig()
 #关闭FileWriter
 writer.close()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持三水点靠木。