标签:dev 一个 img func [1] 第一个 onclick flags ima
算的的上是自己搭建的第一个卷积神经网络。网络结构比较简单。
输入为单通道的mnist数据集。它是一张28*28,包含784个特征值的图片
我们第一层输入,使用5*5的卷积核进行卷积,输出32张特征图,然后使用2*2的池化核进行池化 输出14*14的图片
第二层 使用5*5的卷积和进行卷积,输出64张特征图,然后使用2*2的池化核进行池化 输出7*7的图片
第三层为全连接层 我们总结有 7*7*64 个输入,输出1024个节点 ,使用relu作为激活函数,增加一个keep_prob的dropout层
第四层为输出层,我们接收1024个输入,输出长度为10的one-hot向量。使用softmax作为激活函数
使用交叉熵作为损失函数
网络模型代码:
from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tempfile from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf FLAGS = None def weight_variable(shape): init=tf.truncated_normal(shape=shape,stddev=0.1,mean=1.) return tf.Variable(init) def bias_variable(shape): init=tf.constant(0.1,shape=shape) return tf.Variable(init) def conv2d(x,w): return tf.nn.conv2d(x,w,[1,1,1,1],padding="SAME") def max_pool_2x2(x): return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding="SAME") def deepnn(x): with tf.name_scope(‘reshape‘): x_image=tf.reshape(x,[-1,28,28,1]) #第一层卷积和池化 with tf.name_scope(‘conv1‘): #输入为1张图片 卷积核为5*5 生成32个特征图 w_conv1=weight_variable([5,5,1,32]) b_conv1=bias_variable([32]) h_conv1=tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1) with tf.name_scope(‘pool1‘): h_pool1=max_pool_2x2(h_conv1) #第二层卷积和池化 with tf.name_scope("conv2"): #输入为32张特征图,卷积核为5*5 输出64张特征图 w_conv2=weight_variable([5,5,32,64]) b_conv2=bias_variable([64]) h_conv2=tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2) with tf.name_scope("pool2"): h_pool2=max_pool_2x2(h_conv2) #第一层全连接层,将特征图展开为特征向量,与1024个节点连接 with tf.name_scope("fc1"): w_fc1=weight_variable([7*7*64,1024]) b_fc1=bias_variable([1024]) h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64]) h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1)+b_fc1) #dropout层,训练时随机让某些隐含层节点权重不工作 with tf.name_scope("dropout1"): keep_prob=tf.placeholder(tf.float32) h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob) #第二个全连接层,连接1024个节点,输出one-hot预测 with tf.name_scope("fc2"): w_fc2=weight_variable([1024,10]) b_fc2=bias_variable([10]) h_fc2=tf.matmul(h_fc1_drop,w_fc2)+b_fc2 return h_fc2,keep_prob
训练代码:
from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tempfile from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf import mnist_model FLAGS = None def main(_): mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) #设置输入变量 x=tf.placeholder(dtype=tf.float32,shape=[None,784]) #设置输出变量 y_real=tf.placeholder(dtype=tf.float32,shape=[None,10]) #实例化网络 y_pre,keep_prob=mnist_model.deepnn(x) #设置损失函数 with tf.name_scope("loss"): cross_entropy=tf.nn.softmax_cross_entropy_with_logits(logits=y_pre,labels=y_real) loss=tf.reduce_mean(cross_entropy) #设置优化器 with tf.name_scope("adam_optimizer"): train_step=tf.train.AdamOptimizer(1e-4).minimize(loss) #计算正确率: with tf.name_scope("accuracy"): correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y_real, 1)) correct_prediction = tf.cast(correct_prediction, tf.float32) accuracy = tf.reduce_mean(correct_prediction) #将神经网络图模型保存 graph_location=tempfile.mkdtemp() print(‘saving graph to %s‘%graph_location) train_writer=tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph()) #将训练的网络保存下来 saver=tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(5000): batch=mnist.train.next_batch(50) if i%100==0: train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_real: batch[1], keep_prob: 1.0}) print(‘step %d, training accuracy %g‘ % (i, train_accuracy)) sess.run(train_step,feed_dict={x: batch[0], y_real: batch[1], keep_prob: 0.5}) #在测试集上进行测试 test_accuracy = 0 for i in range(200): batch = mnist.test.next_batch(50) test_accuracy += accuracy.eval(feed_dict={x: batch[0], y_real: batch[1], keep_prob: 1.0}) / 200; print(‘test accuracy %g‘ % test_accuracy) save_path = saver.save(sess, "mnist_cnn_model.ckpt") if __name__ == ‘__main__‘: parser = argparse.ArgumentParser() parser.add_argument(‘--data_dir‘, type=str, default=‘./‘, help=‘Directory for storing input data‘) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
部分训练结果:
step 3600, training accuracy 0.98 step 3700, training accuracy 0.98 step 3800, training accuracy 0.96 step 3900, training accuracy 1 step 4000, training accuracy 0.98 step 4100, training accuracy 0.96 step 4200, training accuracy 1 step 4300, training accuracy 1 step 4400, training accuracy 0.98 step 4500, training accuracy 0.98 step 4600, training accuracy 0.98 step 4700, training accuracy 1 step 4800, training accuracy 0.98 step 4900, training accuracy 1 test accuracy 0.9862
标签:dev 一个 img func [1] 第一个 onclick flags ima
原文地址:https://www.cnblogs.com/superxuezhazha/p/9534583.html