直接上代码：
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
max_step=1000
learning_rate=0.001
dropout=0.9
data_dir='../dataset/mnist/'
log_dir='./tfboard3'
mnist=input_data.read_data_sets(data_dir,one_hot=True)
sess=tf.InteractiveSession()
with tf.name_scope('input'):
x=tf.placeholder(tf.float32,[None,784],name='X_input')
y=tf.placeholder(tf.float32,[None,10],name='y_input')
with tf.name_scope('input_reshape'):
image_shaped_input=tf.reshape(x,[-1,28,28,1])
tf.summary.image('input',image_shaped_input)
def weight_variable(shape):
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)
def variable_summaries(var):
with tf.name_scope('summeries'):
mean=tf.reduce_mean(var)
tf.summary.scalar('mean',mean)
with tf.name_scope('stddev'):
stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
tf.summary.scalar('stddev',stddev)
tf.summary.scalar('max',tf.reduce_max(var))
tf.summary.scalar('min',tf.reduce_min(var))
tf.summary.histogram('histogram',var)
def nn_layer(input_tensor,input_dim,output_dim,layer_name,act=tf.nn.relu):
with tf.name_scope(layer_name):
with tf.name_scope('weight'):
weights=weight_variable([input_dim,output_dim])
variable_summaries(weights)
with tf.name_scope('biases'):#same layer ,all iamges share one biase
biases=bias_variable([output_dim])
variable_summaries(biases)
with tf.name_scope('wx_plus_b'):
preactivate=tf.matmul(input_tensor,weights)+biases
tf.summary.histogram('preactivate',preactivate)
activations=act(preactivate,name='activate')
return activations #not really calcute
hidden1=nn_layer(x,784,500,'layer1')
with tf.name_scope('dropout'):
keep_prob=tf.placeholder(tf.float32)
tf.summary.scalar('keep_prob',keep_prob)
droped=tf.nn.dropout(hidden1,keep_prob)
#calcute relu,print(name,value) to see
y1=nn_layer(droped,500,10,'layer2',act=tf.identity)
with tf.name_scope('cross_entropy'):
diff=tf.nn.softmax_cross_entropy_with_logits(logits=y1,labels=y)
with tf.name_scope('total'):
cross_entropy=tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy',cross_entropy)
with tf.name_scope('train'):
train_step=tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
with tf.name_scope('accuracy_prediction'):
correct_prediction=tf.equal(tf.arg_max(y1,1),tf.arg_max(y,1))
with tf.name_scope('accuracy'):
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
tf.summary.scalar('accuracy',accuracy)
merged=tf.summary.merge_all() #learning this write way
train_writer=tf.summary.FileWriter(log_dir+'/trian',sess.graph)
test_writer=tf.summary.FileWriter(log_dir+'/test')#save events,but not tfboard
tf.global_variables_initializer().run()
def feed_dict(train):
if train:
xs,ys=mnist.train.next_batch(100)
k=dropout
else:
xs,ys=mnist.test.images,mnist.test.labels
k=1.0#key=placeholder_name ,learn 3 elements
return {x:xs,y:ys,keep_prob:k}
saver=tf.train.Saver()
for i in range(max_step):
if i%10==0:
summary,acc=sess.run([merged,accuracy],feed_dict=feed_dict(False))
test_writer.add_summary(summary,i)#learn %s can value int,float
print('accuracy at step %s:%s' %(i,acc))
else:
if i%100==99:#???
run_options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata=tf.RunMetadata()#???
summary,_=sess.run([merged,train_step],feed_dict=feed_dict(True),
options=run_options,run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata,'step%03d' %i)#??
train_writer.add_summary(summary,i)
saver.save(sess,log_dir+'/model.ckpt',global_step=i)
print('adding run metadata for %s' %i)
else:
summary,_=sess.run([merged,train_step],feed_dict=feed_dict(True))
train_writer.add_summary(summary,i)
train_writer.close()
test_writer.close()
在tensorboard的显示结果
参考链接：
https://blog.csdn.net/smf0504/article/details/56369758