训练mnist数据集
结构组成：
input_image --> convolution1 --> pool1 --> convolution2 --> pool2 --> full_connecion1 --> full_connection2
# 卷积 import tensorflow as tf import input_data # 加载mnist数据集 mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # 构建多层卷积网络 # 权重及偏置初始化, ReLU神经元 用一个较小的正数来初始化偏置项来打破对称性以及避免0梯度 def weight_variable(shape): """ :param shape:二维tensor,第一个维度代表层中权重变量所连接（connect from）的单元数目， 第二个维度代表层中权重变量所连接（connect to）到的单元数量 :return: W """ initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) # 卷积及池化 def conv2d(x, W): """ 卷积 :param x: :param W: :return: """ return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME") def max_pool_2x2(x): """ 最大池化 :param x: :return: """ return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") def compute_accuracy(v_xs, v_ys): """ 计算的准确率 """ global prediction # prediction value y_pre = sess.run(prediction, feed_dict={xs: v_xs}) # 与期望的值比较 bool correct_pre = tf.equal(tf.argmax(y_pre, 1), tf.argmax(ys, 1)) # 将bools转化为数字 accuracy = tf.reduce_mean(tf.cast(correct_pre, tf.float32)) result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys}) return result # 数据图片 xs = tf.placeholder("float", shape=[None, 784]) # size 28 * 28 =784 # 预期概率 ys = tf.placeholder("float", shape=[None, 10]) # 10: 矩阵维度(分类) keep_prob = tf.placeholder(tf.float32) x_image = tf.reshape(xs, [-1, 28, 28, 1]) # -1: 任意数量的图片; 28*28:图片的长宽; 1:灰色图片为1 # layer1 W_conv1 = weight_variable([5, 5, 1, 32]) # 5*5:patch过滤长宽, 1:起始输入一张图片, 32:out_size b_conv1 = bias_variable([32]) # 32:上层输入的out_size h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output_size=28*28*32 h_pool1 = max_pool_2x2(h_conv1) # output_size=14*14*32 pool_strdes=2 # layer2 W_conv2 = weight_variable([5, 5, 32, 64]) # 64是训练中不断增加的高度，自定义 b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output_size=14*14*64 h_pool2 = max_pool_2x2(h_conv2) # output_size=7*7*64 池化的步长为[2,2] # func1 layer W_fc1 = weight_variable([7*7*64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) # 将pool2铺平为7*7*64 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # 矩阵相乘 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # 防止过拟合 # func2 layer W_fc2 = weight_variable([1024, 10]) # 传入的1024， 判断0-9的数字one-hot，10来代表每个数字 b_fc2 = bias_variable([10]) prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) # softmax分类 # the loss between prediction and really cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1])) tf.summary.scalar('loss', cross_entropy) # 字符串类型的标量张量，包含一个Summaryprotobuf 1.1记录标量 # training train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # AdamOptimizer使用复杂模型 sess = tf.Session() sess.run(tf.initialize_all_variables()) # start training for i in range(1000): batch_x, batch_y = mnist.train.next_batch(100) sess.run(train_step, feed_dict={xs: batch_x, ys: batch_y, keep_prob: 0.1}) if i % 50 == 0: print(compute_accuracy(mnist.test.images, mnist.test.labels)) print("Training Finished !!!")