Tensorboard学习

对官网Deep MNIST for Experts的例程做了可视化修改

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


import numpy as np
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])


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 conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[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')


x_image = tf.reshape(x, [-1,28,28,1])




with tf.name_scope('layer1'):
with tf.name_scope('weights'):
W_conv1 = weight_variable([5, 5, 1, 32])
tf.summary.histogram('layer1'+"/weights",W_conv1)
with tf.name_scope('biases'):
b_conv1 = bias_variable([32])
tf.summary.histogram('layer1'+"/biases",b_conv1)
with tf.name_scope('wx+b'):
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
tf.summary.histogram('layer1'+"/wx+b",h_conv1)
with tf.name_scope('h_pool'):
h_pool1 = max_pool_2x2(h_conv1)
tf.summary.histogram('layer1'+"/h_pool",h_pool1)










with tf.name_scope('layer2'):
with tf.name_scope('weights'):
W_conv2 = weight_variable([5, 5, 32, 64])
tf.summary.histogram('layer2'+"/weights",W_conv2)
with tf.name_scope('biases'):
b_conv2 = bias_variable([64])
tf.summary.histogram('layer2'+"/biases",b_conv2)
with tf.name_scope('wx+b'):
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
tf.summary.histogram('layer2'+"/wx+b",h_conv2)
with tf.name_scope('h_pool'):
h_pool2 = max_pool_2x2(h_conv2)
tf.summary.histogram('layer2'+"/h_pool",h_pool2)




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)


keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)




with tf.name_scope('train'):  
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):  

correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
tf.summary.scalar('accuracy',accuracy) 


saver = tf.train.Saver()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("",sess.graph)  
sess.run(tf.global_variables_initializer())
'''
saver.restore(sess,"/model.ckpt")
print "Model retored."
'''
for i in range(20000):
  batch = mnist.train.next_batch(50)
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print "step %d, training accuracy %g"%(i, train_accuracy)
    
    result = sess.run(merged,feed_dict={x: batch[0], y_: batch[1]}) 
    writer.add_summary(result,i)
  if i%1000 == 0:
    save_path = saver.save(sess, "/model.ckpt")
    print "Model saved in file: ", save_path
  
'''
print "test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
'''
accuracyResult = list(range(10))
for i in range(10):
    batch = mnist.test.next_batch(1000)
    accuracyResult[i] = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
print "Test accuracy:", numpy.mean(accuracyResult)

效果如下：