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 tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_datafrom tensorflow.contrib.tensorboard.plugins import projectorimport timeIMAGE_PIXELS = 28hidden_unit = 100output_nums = 10learning_rate = 0.001train_steps = 50000batch_size = 500test_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.imagestest_label = mnist.test.labelstest_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。