自编码网络能够自学习样本特征的网络,属于无监督学习模型的网络,可以从无标注的数据中学习特征,它可以给出比原始数据更好的特征描述,具有较强的特征学习能力。
主要的网络结构就是高维特征样本---》编码成---》低维特征---》解码回---》高维特征,下面以MNIST数据集为示例进行演示:
- import tensorflow as tf
- #导入数据集合
- from tensorflow.examples.tutorials.mnist import input_data
- mnist = input_data.read_data_sets('/data/',one_hot=True)
- #整体流程,原始图片像素28*28-784
- #784-》256-》128-》128-》256-》784
- learning_rate = 0.01
- n_hidden_1 = 256 #第一层256个结点
- n_hidden_2 = 128 #第二层128个结点
- n_input = 784
- x = tf.placeholder('float',[None,n_input])
- y = x
- weights = {
- 'encoder_h1':tf.Variable(tf.random_normal([n_input,n_hidden_1])),
- 'encoder_h2':tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2])),
- 'decoder_h1':tf.Variable(tf.random_normal([n_hidden_2,n_hidden_1])),
- 'decoder_h2':tf.Variable(tf.random_normal([n_hidden_1,n_input])),
- }
- biases = {
- 'encoder_b1':tf.Variable(tf.zeros([n_hidden_1])),
- 'encoder_b2':tf.Variable(tf.zeros([n_hidden_2])),
- 'decoder_b1':tf.Variable(tf.zeros([n_hidden_1])),
- 'decoder_b2':tf.Variable(tf.zeros([n_input])),
- }
- def encoder(x):
- layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x,weights['encoder_h1']),biases['encoder_b1']))
- layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['encoder_h2']),biases['encoder_b2']))
- return layer_2
- def decoder(x):
- layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x,weights['decoder_h1']),biases['decoder_b1']))
- layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['decoder_h2']),biases['decoder_b2']))
- return layer_2
- pred = decoder(encoder(x))
- cost = tf.reduce_mean(tf.pow(y-pred,2))
- optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
- training_epochs = 20 #共迭代20次
- batch_size = 256 #每次取256个样本
- display_step = 5 #迭代5次输出一次信息
- #启动会话
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- total_batch = int(mnist.train.num_examples/batch_size)
- #开始训练
- for epoch in range(training_epochs):
- for i in range(total_batch):
- batch_xs,batch_ys = mnist.train.next_batch(batch_size)#取数据
- _,c = sess.run([optimizer,cost],feed_dict={x:batch_xs})#训练模型
- if epoch % display_step == 0:#输出日志信息
- print("Epoch:",'%4d' % (epoch+1),'cost=',"{:.9f}".format(c))
- print('Training Finished!')
- correct_prediction = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
- accuracy = tf.reduce_mean(tf.cast(correct_prediction,'float'))
- print('Accuracy:',1-accuracy.eval({x:mnist.test.images,y:mnist.test.images}))