import osimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 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_sizeexample_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 = 784n_output_units = n_input_units def 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_code2 def 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_decode2 with 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！') 
