吴裕雄 PYTHON 神经网络——TENSORFLOW 双隐藏层自编码器设计处理MNIST手写数字数据集并使用TENSORBORD描绘神经网络数据2

import os
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataos.environ['TF_CPP_MIN_LOG_LEVEL'] = ''batch_size = 128 # batch容量
display_step = 1 # 展示间隔
learning_rate = 0.01 # 学习率
training_epochs = 20 # 训练轮数，1轮等于n_samples/batch_size
example_to_show = 10 # 展示图像数目n_hidden1_units = 256 # 第一隐藏层
n_hidden2_units = 128 # 第二隐藏层
n_input_units = 784
n_output_units = n_input_unitsdef variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.histogram('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev) # 注意，这是标量
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
def WeightsVariable(n_in,n_out,name_str):
return tf.Variable(tf.random_normal([n_in,n_out]),dtype=tf.float32,name=name_str)def biasesVariable(n_out,name_str):
return tf.Variable(tf.random_normal([n_out]),dtype=tf.float32,name=name_str)def encoder(x_origin,activate_func=tf.nn.sigmoid):
with tf.name_scope('Layer1'):
Weights = WeightsVariable(n_input_units,n_hidden1_units,'Weights')
biases = biasesVariable(n_hidden1_units,'biases')
x_code1 = activate_func(tf.add(tf.matmul(x_origin,Weights),biases))
variable_summaries(Weights)
variable_summaries(biases)
with tf.name_scope('Layer2'):
Weights = WeightsVariable(n_hidden1_units,n_hidden2_units,'Weights')
biases = biasesVariable(n_hidden2_units,'biases')
x_code2 = activate_func(tf.add(tf.matmul(x_code1,Weights),biases))
variable_summaries(Weights)
variable_summaries(biases)
return x_code2def decode(x_code,activate_func=tf.nn.sigmoid):
with tf.name_scope('Layer1'):
Weights = WeightsVariable(n_hidden2_units,n_hidden1_units,'Weights')
biases = biasesVariable(n_hidden1_units,'biases')
x_decode1 = activate_func(tf.add(tf.matmul(x_code,Weights),biases))
variable_summaries(Weights)
variable_summaries(biases)
with tf.name_scope('Layer2'):
Weights = WeightsVariable(n_hidden1_units,n_output_units,'Weights')
biases = biasesVariable(n_output_units,'biases')
x_decode2 = activate_func(tf.add(tf.matmul(x_decode1,Weights),biases))
variable_summaries(Weights)
variable_summaries(biases)
return x_decode2with tf.Graph().as_default():
with tf.name_scope('Input'):
X_input = tf.placeholder(tf.float32,[None,n_input_units])
with tf.name_scope('Encode'):
X_code = encoder(X_input)
with tf.name_scope('decode'):
X_decode = decode(X_code)
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.pow(X_input - X_decode,2))
with tf.name_scope('train'):
Optimizer = tf.train.RMSPropOptimizer(learning_rate)
train = Optimizer.minimize(loss)
# 标量汇总
with tf.name_scope('LossSummary'):
tf.summary.scalar('loss',loss)
tf.summary.scalar('learning_rate',learning_rate)
# 图像展示
with tf.name_scope('ImageSummary'):
image_original = tf.reshape(X_input,[-1, 28, 28, 1])
image_reconstruction = tf.reshape(X_decode, [-1, 28, 28, 1])
tf.summary.image('image_original', image_original, 9)
tf.summary.image('image_recinstruction', image_reconstruction, 9)
# 汇总
merged_summary = tf.summary.merge_all() init = tf.global_variables_initializer() writer = tf.summary.FileWriter(logdir='E:\\tensorboard\\logsssxx', graph=tf.get_default_graph())
writer.flush() mnist = input_data.read_data_sets('E:\\MNIST_data\\', one_hot=True) with tf.Session() as sess:
sess.run(init)
total_batch = int(mnist.train.num_examples / batch_size)
for epoch in range(training_epochs):
for i in range(total_batch):
batch_xs,batch_ys = mnist.train.next_batch(batch_size)
_,Loss = sess.run([train,loss],feed_dict={X_input: batch_xs})
Loss = sess.run(loss,feed_dict={X_input: batch_xs})
if epoch % display_step == 0:
print('Epoch: %04d' % (epoch + 1),'loss= ','{:.9f}'.format(Loss))
summary_str = sess.run(merged_summary,feed_dict={X_input: batch_xs})
writer.add_summary(summary_str,epoch)
writer.flush()
writer.close()
print('训练完毕！')