Tensorflow可视化MNIST手写数字训练

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector#载入数据集
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
#运行次数
max_steps = 1001
#图片数量
image_num = 3000
#文件路径
DIR = "E:/Github/TensorFlow/trunk/Test/"#定义会话
sess = tf.Session()#载入图片
embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding')#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#命名空间
with tf.name_scope('input'):
#这里的none表示第一个维度可以是任意的长度
x = tf.placeholder(tf.float32,[None,784],name='x-input')
#正确的标签
y = tf.placeholder(tf.float32,[None,10],name='y-input')#显示图片
with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', image_shaped_input, 10)with tf.name_scope('layer'):
#创建一个简单神经网络
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'):
#交叉熵代价函数
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)#初始化变量
sess.run(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))#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))#把correct_prediction变为float32类型
tf.summary.scalar('accuracy',accuracy)#产生metadata文件
if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'):
tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv')
with open(DIR + 'projector/projector/metadata.tsv', 'w') as f:
labels = sess.run(tf.argmax(mnist.test.labels[:],1))
for i in range(image_num):
f.write(str(labels[i]) + '\n') #合并所有的summary
merged = tf.summary.merge_all() projector_writer = tf.summary.FileWriter(DIR + 'projector/projector',sess.graph)
saver = tf.train.Saver()
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embedding.name
embed.metadata_path = DIR + 'projector/projector/metadata.tsv'
embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png'
embed.sprite.single_image_dim.extend([28,28])
projector.visualize_embeddings(projector_writer,config)for i in range(max_steps):
#每个批次100个样本
batch_xs,batch_ys = mnist.train.next_batch(100)
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys},options=run_options,run_metadata=run_metadata)
summary = sess.run(merged,feed_dict={x:batch_xs,y:batch_ys},options=run_options,run_metadata=run_metadata)
projector_writer.add_run_metadata(run_metadata, 'step%03d' % i)
projector_writer.add_summary(summary, i) if i%100 == 0:
acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
print ("Iter " + str(i) + ", Testing Accuracy= " + str(acc))saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps)
projector_writer.close()
sess.close()