手写数字识别的改进：将识别准确率提高到98%以上：
import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data #读取mnist数据集 如果没有则会下载 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #每个批次的大小 batch_size = 100 #计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size #定义两个占位符 x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) keep_prob = tf.placeholder(tf.float32) lr = tf.Variable(0.001,dtype=tf.float32) #创建简单的神经网络 #通常这样初始化 #W = tf.Variable(tf.truncated_normal([784,10]))#权值 #b = tf.Variable(tf.zeros([10]))#偏置值 W1 = tf.Variable(tf.truncated_normal([784,500],stddev=0.1))#权值 b1 = tf.Variable(tf.zeros([500])+0.1) L1 = tf.nn.tanh(tf.matmul(x,W1)+b1) L1_drop = tf.nn.dropout(L1,keep_prob) W2 = tf.Variable(tf.truncated_normal([500,250],stddev=0.1))#权值 b2 = tf.Variable(tf.zeros([250])+0.1) L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2) L2_drop = tf.nn.dropout(L2,keep_prob) W = tf.Variable(tf.truncated_normal([250,10],stddev=0.1)) b = tf.Variable(tf.zeros([10])+0.1) #预测值 prediction = tf.nn.softmax(tf.matmul(L2_drop,W)+b) #二次代价函数 #loss = tf.reduce_mean(tf.square(y-prediction)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) #使用梯度下降法 train_step = tf.train.AdamOptimizer(lr).minimize(loss) #初始化变量 init = tf.global_variables_initializer() #预测数据与样本比较，如果相等就返回1 求出标签 #结果存放在布尔型列表中 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置 #求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #进行训练 with tf.Session() as sess: sess.run(init) for i in range(31):#周期 sess.run(tf.assign(lr,0.001*(0.95**i))) for batch in range(n_batch):#批次 batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0}) acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels, keep_prob: 1.0}) lr_show = sess.run(lr) print("周期 ："+ str(i) + "预测准确率：" + str(acc)+ "学习率：" + str(lr_show))
TensorBoard的网络结构
用tensorboard来画神经网络的结构。
需要先定义一个命名空间name_scope。
然后加一句用于创建新文件夹和画图：
然后logs文件夹下就会出现一个新的文件。
之后在windows下打开Anaconda Prompt，路径改到当前磁盘的目录下，比如"d:"
视频里说打开命令行，但是我的电脑里打开命令行不承认tensorboard这个命令，见下：
在Anaconda Prompt下输入："tensorboard --logdir=logs"，‘=‘’后面加logs文件夹所在的路径，因为本身就在当前文件夹下，所以直接输入logs就可以了。
import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data #读取mnist数据集 如果没有则会下载 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #每个批次的大小 batch_size = 100 #计算一共有多少批次 n_batch = mnist.train.num_examples // batch_size #命名空间 with tf.name_scope('input'): #定义两个占位符 x = tf.placeholder(tf.float32,[None,784],name='x-input') y = tf.placeholder(tf.float32,[None,10],name='y-input') with tf.name_scope('layer'): #创建简单的神经网络 #群值 with tf.name_scope('wight'): W = tf.Variable(tf.zeros([784,10])) #偏置值 with tf.name_scope('biases'): b = tf.Variable(tf.zeros([10])) #预测值 with tf.name_scope('wx_plus_b'): wx_plus_b = tf.matmul(x,W)+b with tf.name_scope('softmax'): prediction = tf.nn.softmax(wx_plus_b) #二次代价函数 with tf.name_scope('loss'): loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) #使用梯度下降法 with tf.name_scope('train'): train_step = tf.train.GradientDescentOptimizer(0.3).minimize(loss) #初始化变量 init = tf.global_variables_initializer() with tf.name_scope('accuracy'): #预测数据与样本比较，如果相等就返回1 求出标签 #结果存放在布尔型列表中 with tf.name_scope('correct_prediction'): correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置 #求准确率 with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #进行训练 with tf.Session() as sess: sess.run(init) writer = tf.summary.FileWriter('logs/',sess.graph) for i in range(1):#周期 for batch in range(n_batch):#批次 batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys}) acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print("周期 ："+ str(i) + "准确率：" + str(acc))
流程图：
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib.tensorboard.plugins import projector # 载入数据集 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 运行次数 max_steps = 1001 # 图片数量 image_num = 3000 # 文件路径 DIR = "D:/Embedded Code/Python_Demo/Tensorflow/" # 定义会话 sess = tf.Session() # 载入图片 embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding') # 参数概要 def variable_summaries(var): with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) # 平均值 with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev) # 标准差 tf.summary.scalar('max', tf.reduce_max(var)) # 最大值 tf.summary.scalar('min', tf.reduce_min(var)) # 最小值 tf.summary.histogram('histogram', var) # 直方图 # 命名空间 with tf.name_scope('input'): # 这里的none表示第一个维度可以是任意的长度 x = tf.placeholder(tf.float32, [None, 784], name='x-input') # 正确的标签 y = tf.placeholder(tf.float32, [None, 10], name='y-input') # 显示图片 with tf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1, 28, 28, 1]) tf.summary.image('input', image_shaped_input, 10) with tf.name_scope('layer'): # 创建一个简单神经网络 with tf.name_scope('weights'): W = tf.Variable(tf.zeros([784, 10]), name='W') variable_summaries(W) with tf.name_scope('biases'): b = tf.Variable(tf.zeros([10]), name='b') variable_summaries(b) with tf.name_scope('wx_plus_b'): wx_plus_b = tf.matmul(x, W) + b with tf.name_scope('softmax'): prediction = tf.nn.softmax(wx_plus_b) with tf.name_scope('loss'): # 交叉熵代价函数 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) tf.summary.scalar('loss', loss) with tf.name_scope('train'): # 使用梯度下降法 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss) # 初始化变量 sess.run(tf.global_variables_initializer()) with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置 with tf.name_scope('accuracy'): # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 把correct_prediction变为float32类型 tf.summary.scalar('accuracy', accuracy) # 产生metadata文件 if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'): tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv') with open(DIR + 'projector/projector/metadata.tsv', 'w') as f: labels = sess.run(tf.argmax(mnist.test.labels[:], 1)) for i in range(image_num): f.write(str(labels[i]) + '\n') projector_writer = tf.summary.FileWriter(DIR + 'projector/projector', sess.graph) saver = tf.train.Saver() config = projector.ProjectorConfig() embed = config.embeddings.add() embed.tensor_name = embedding.name embed.metadata_path = DIR + 'projector/projector/metadata.tsv' embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png' embed.sprite.single_image_dim.extend([28, 28]) projector.visualize_embeddings(projector_writer, config) # 合并所有的summary merged = tf.summary.merge_all() for i in range(max_steps): # 每个批次100个样本 batch_xs, batch_ys = mnist.train.next_batch(100) run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys}, options=run_options,run_metadata=run_metadata) projector_writer.add_run_metadata(run_metadata, 'step%03d' % i) projector_writer.add_summary(summary, i) if i % 100 == 0: acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Iter " + str(i) + ", Testing Accuracy= " + str(acc)) saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps) projector_writer.close() sess.close()
