MNIST手写数字识别之Tensorflow实现---全连接实现

import tensorflow as tf # 用tensorflow 导入数据 from tensorflow.examples.tutorials.mnist import input_data import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' # 设置按需使用GPU config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.InteractiveSession(config=config) # MNIST_data代表当前程序文件所在的目录中，用于存放MNIST数据的文件夹，如果没有则新建，然后下载． # 如果当前文件所在目录中，不存在 MNIST_data 这个目录的话，程序会自动下载 MNIST 数据到这个位置，# 如果已经存在了的话，就直接读取数据文件。 mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # print('训练集信息：', mnist.train.images.shape, mnist.train.labels.shape) # print('测试集信息：', mnist.test.images.shape, mnist.test.labels.shape) # print('验证集信息：', mnist.validation.images.shape, mnist.validation.labels.shape) # 获取第5张图片，标签为[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] # image = mnist.train.images[5] # 将图像数据还原成28*28的分辨率 # image = image.reshape(28, 28) # 打印对应的标签 # print(mnist.train.labels[5]) # # plt.figure() # plt.imshow(image) # plt.show() # 权值初始化 def weight_variable(shape): # 用正态分布来初始化权值 initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) # 偏置初始化 def bias_variable(shape): # 本例中用relu激活函数，所以用一个很小的正偏置较好 initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) # input_layer x_input = tf.placeholder(tf.float32, [None, 784], name="x_input") y_input = tf.placeholder(tf.float32, [None, 10], name="y_input") # FC1 W_fc1 = weight_variable([784, 1024]) b_fc1 = bias_variable([1024]) h_fc1 = tf.nn.relu(tf.matmul(x_input, W_fc1) + b_fc1, name="h_fc1") # FC2 W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_pre = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2, name="y_pre") # 1.损失函数loss：loss_cross_entropy # loss_cross_entropy = -tf.reduce_sum(y_input * tf.log(y_pre)) # 损失模型隐藏到loss-model模块 with tf.name_scope("loss-model"): # 1.损失函数loss：loss_cross_entropy loss_cross_entropy = -tf.reduce_sum(y_input * tf.log(y_pre)) # 给损失模型的输出添加scalar，用来观察loss的收敛曲线 tf.summary.scalar("loss", loss_cross_entropy) # 2.优化函数：AdamOptimizer, 优化速度要比 GradientOptimizer 快很多 # train_step = tf.train.AdamOptimizer(0.001).minimize(loss_cross_entropy) train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_cross_entropy) # 等价于以下语句： # optimizer = tf.train.AdamOptimizer(0.001) # train_step = optimizer.minimize(loss_cross_entropy) # 3.预测结果评估 # 预测值中最大值（１）即分类结果，是否等于原始标签中的（１）的位置。 # argmax()取最大值所在的下标 # y_pre和y_input一行对应一个标签，行数对应batch的size大小 # correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.arg_max(y_input, 1)) # accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.name_scope("accuracy-model"): # y_pre和y_input一行对应一个标签，行数对应batch的size大小 correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.arg_max(y_input, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 给损失模型的输出添加scalar，用来观察accracy的收敛曲线 tf.summary.scalar("test_acc", accuracy) # 开始运行 sess.run(tf.global_variables_initializer()) # 调用 merge_all() 收集所有的操作数据 merged = tf.summary.merge_all() # 模型运行产生的所有数据保存到 ./tensorflow 文件夹供 TensorBoard 使用 writer = tf.summary.FileWriter('./tensorboard', sess.graph, filename_suffix="_mnist") # 迭代 100 个 epoch， 训练准确率已经达到了0.98 for i in range(55000): x_batch, y_batch = mnist.train.next_batch(batch_size=100) # train_step.run(feed_dict={x_input: x_batch, y_input: y_batch}) # 等价于下面语句： # sess.run(train_step,feed_dict={x_input: x_batch, y_input: y_batch}) # summary = sess.run(merged, feed_dict={x_input: x_batch, y_input: y_batch}) # train_step.run(feed_dict={x_input: x_batch, y_input: y_batch}) # 等价于上述两条语句 summary, _, train_loss = sess.run([merged, train_step, loss_cross_entropy], feed_dict={x_input: x_batch, y_input: y_batch}) # print("step %d, loss %s" % (i, train_loss)) if (i + 1) % 100 == 0: # print("step %d,\ttrain_loss %s" % (i+1, train_loss)) train_accuracy = accuracy.eval(feed_dict={x_input: mnist.train.images, y_input: mnist.train.labels}) print("step %d,\ttrain_loss %s,\ttrain acc %g" % (i + 1, train_loss, train_accuracy)) if (i + 1) % 500 == 0: test_accuracy = accuracy.eval(feed_dict={x_input: mnist.test.images, y_input: mnist.test.labels}) print("= " * 10, "step %d,\ttest acc %g" % (i + 1, test_accuracy)) writer.add_summary(summary, i) val_accuracy = accuracy.eval(feed_dict={x_input: mnist.validation.images, y_input: mnist.validation.labels}) print("* " * 10, "step %d, validation acc %g" % (i + 1, val_accuracy))

手动下载mnist数据集地址：http://yann.lecun.com/exdb/mnist/ 

本程序是使用两个全连接层实现手写数字识别分类 ，模型框架如图所示：