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Tensorflow实现MNIST数据自编码(1)
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自編碼網絡能夠自學習樣本特征的網絡，屬于無監督學習模型的網絡，可以從無標注的數據中學習特征，它可以給出比原始數據更好的特征描述，具有較強的特征學習能力。
主要的網絡結構就是高維特征樣本---》編碼成---》低維特征---》解碼回---》高維特征，下面以MNIST數據集為示例進行演示：
import?tensorflow?as?tf??????from?tensorflow.examples.tutorials.mnist?import?input_data??mnist?=?input_data.read_data_sets('/data/',one_hot=True)??????????learning_rate?=?0.01??n_hidden_1?=?256???????n_hidden_2?=?128???????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????batch_size?=?256????????display_step?=?5????????????with?tf.Session()?as?sess:??????sess.run(tf.global_variables_initializer())??????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)??????????????_,c?=?sess.run([optimizer,cost],feed_dict={x:batch_xs})????????????????if?epoch?%?display_step?==?0:??????????????????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}))??
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