#!/usr/bin/env python
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data
# In[2]:
mnist = input_data.read_data_sets(‘MNIST_data‘, one_hot=True)
# 每个批次的大小batch_size = 100# 计算一共有多少个批次n_batch = mnist.train.num_examples // batch_size
# 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
# 初始化权值def weight_variable(shape, name): initial = tf.truncated_normal(shape, stddev=0.1) # 生成一个截断的正态分布 return tf.Variable(initial, name=name)
# 初始化偏置def bias_variable(shape, name): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial, name=name)
# 卷积层def conv2d(x, W): # x input tensor of shape `[batch, in_height, in_width, in_channels]` # W filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels] # `strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长 # padding: A `string` from: `"SAME", "VALID"` return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=‘SAME‘)
# 池化层def max_pool_2x2(x): # ksize [1,x,y,1] return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)
# 命名空间with tf.name_scope(‘input‘): # 定义两个placeholder x = tf.placeholder(tf.float32, [None, 784], name=‘x-input‘) y = tf.placeholder(tf.float32, [None, 10], name=‘y-input‘) with tf.name_scope(‘x_image‘): # 改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]` x_image = tf.reshape(x, [-1, 28, 28, 1], name=‘x_image‘)
with tf.name_scope(‘Conv1‘): # 初始化第一个卷积层的权值和偏置 with tf.name_scope(‘W_conv1‘): W_conv1 = weight_variable([5, 5, 1, 32], name=‘W_conv1‘) # 5*5的采样窗口，32个卷积核从1个平面抽取特征 with tf.name_scope(‘b_conv1‘): b_conv1 = bias_variable([32], name=‘b_conv1‘) # 每一个卷积核一个偏置值
# 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数 with tf.name_scope(‘conv2d_1‘): conv2d_1 = conv2d(x_image, W_conv1) + b_conv1 with tf.name_scope(‘relu‘): h_conv1 = tf.nn.relu(conv2d_1) with tf.name_scope(‘h_pool1‘): h_pool1 = max_pool_2x2(h_conv1) # 进行max-pooling
with tf.name_scope(‘Conv2‘): # 初始化第二个卷积层的权值和偏置 with tf.name_scope(‘W_conv2‘): W_conv2 = weight_variable([5, 5, 32, 64], name=‘W_conv2‘) # 5*5的采样窗口，64个卷积核从32个平面抽取特征 with tf.name_scope(‘b_conv2‘): b_conv2 = bias_variable([64], name=‘b_conv2‘) # 每一个卷积核一个偏置值
# 把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数 with tf.name_scope(‘conv2d_2‘): conv2d_2 = conv2d(h_pool1, W_conv2) + b_conv2 with tf.name_scope(‘relu‘): h_conv2 = tf.nn.relu(conv2d_2) with tf.name_scope(‘h_pool2‘): h_pool2 = max_pool_2x2(h_conv2) # 进行max-pooling
# 28*28的图片第一次卷积后还是28*28，第一次池化后变为14*14# 第二次卷积后为14*14，第二次池化后变为了7*7# 进过上面操作后得到64张7*7的平面
with tf.name_scope(‘fc1‘): # 初始化第一个全连接层的权值 with tf.name_scope(‘W_fc1‘): W_fc1 = weight_variable([7 * 7 * 64, 1024], name=‘W_fc1‘) # 上一场有7*7*64个神经元，全连接层有1024个神经元 with tf.name_scope(‘b_fc1‘): b_fc1 = bias_variable([1024], name=‘b_fc1‘) # 1024个节点
# 把池化层2的输出扁平化为1维 with tf.name_scope(‘h_pool2_flat‘): h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64], name=‘h_pool2_flat‘) # 求第一个全连接层的输出 with tf.name_scope(‘wx_plus_b1‘): wx_plus_b1 = tf.matmul(h_pool2_flat, W_fc1) + b_fc1 with tf.name_scope(‘relu‘): h_fc1 = tf.nn.relu(wx_plus_b1)
# keep_prob用来表示神经元的输出概率 with tf.name_scope(‘keep_prob‘): keep_prob = tf.placeholder(tf.float32, name=‘keep_prob‘) with tf.name_scope(‘h_fc1_drop‘): h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name=‘h_fc1_drop‘)
with tf.name_scope(‘fc2‘): # 初始化第二个全连接层 with tf.name_scope(‘W_fc2‘): W_fc2 = weight_variable([1024, 10], name=‘W_fc2‘) with tf.name_scope(‘b_fc2‘): b_fc2 = bias_variable([10], name=‘b_fc2‘) with tf.name_scope(‘wx_plus_b2‘): wx_plus_b2 = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 with tf.name_scope(‘softmax‘): # 计算输出 prediction = tf.nn.softmax(wx_plus_b2)
# 交叉熵代价函数with tf.name_scope(‘cross_entropy‘): cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction), name=‘cross_entropy‘) tf.summary.scalar(‘cross_entropy‘, cross_entropy)
# 使用AdamOptimizer进行优化with tf.name_scope(‘train‘): train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 求准确率with tf.name_scope(‘accuracy‘): with tf.name_scope(‘correct_prediction‘): # 结果存放在一个布尔列表中 correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 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)) tf.summary.scalar(‘accuracy‘, accuracy)
# 合并所有的summarymerged = tf.summary.merge_all()
with tf.Session() as sess: sess.run(tf.global_variables_initializer()) train_writer = tf.summary.FileWriter(‘logs/train‘, sess.graph) test_writer = tf.summary.FileWriter(‘logs/test‘, sess.graph) for i in range(1001): # 训练模型 batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.5}) # 记录训练集计算的参数 summary = sess.run(merged, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0}) train_writer.add_summary(summary, i) # 记录测试集计算的参数 batch_xs, batch_ys = mnist.test.next_batch(batch_size) summary = sess.run(merged, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0}) test_writer.add_summary(summary, i)
if i % 100 == 0: test_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[:10000], y: mnist.train.labels[:10000], keep_prob: 1.0}) print("Iter " + str(i) + ", Testing Accuracy= " + str(test_acc) + ", Training Accuracy= " + str(train_acc))
原文地址：https://www.cnblogs.com/rongye/p/10010116.html
时间： 2024-10-29 00:58:39