import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#载入数据集
mnist=input_data.read_data_sets("MNIST_data",one_hot=True)
#批次大小
batch_size=64
#计算一个周期一共有多少个批次
n_batch=mnist.train.num_examples
#参数概要
def variable_summaries(var):
with tf.name_scope('summaries'):
mean=tf.reduce_mean(var)
#average
tf.summary.scalar('mean',mean)
with tf.name_scope('stddev'):
stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
#标准差
tf.summary.scalar('stddev',stddev)
#最大值
tf.summary.scalar('max',tf.reduce_max(var))
#最小值
tf.summary.scalar('min',tf.reduce_min(var))
#直方图
tf.summary.histogram('histogram',var)
#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('layer'):
#创建一个简单的神经网络:784-10
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'):
#二次代价函数
loss=tf.losses.mean_squared_error(y,prediction)
tf.summary.scalar('loss',loss)#直接记录,无需统计标准差什么的
with tf.name_scope('train'):
#使用梯度下降法
train_step=tf.train.GradientDescentOptimizer(0.3).minimize(loss)
#初始化变量
init=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))
with tf.name_scope('accuracy'):
#求准确率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
tf.summary.scalar('accuracy',accuracy)#直接记录,无需统计标准差什么的
#合并所有的summary
merged=tf.summary.merge_all()
with tf.Session() as sess:
sess.run(init)
writer=tf.summary.FileWriter('logs/',sess.graph)
for epoch in range(51):
for batch in range(n_batch):
batch_xs,batch_ys=mnist.train.next_batch(batch_size)
summary,_=sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
writer.add_summary(summary,epoch)
acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
print("Iter "+str(epoch)+",Testing Accuuracy "+str(acc))
tensorflow速度复习-记录数据
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转载自blog.csdn.net/cj1064789374/article/details/88199704
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