如果按n→m→k 结构,实际上我们是先训练网络n→m→n,得到n→m的变换,然后再训练m→k→m,得到m→k的变换。最终堆叠成SAE,即为n→m→k的结果,整个过程就像一层层往上盖房子,这便是大名鼎鼎的 layer-wise unsuperwised pre-training (逐层非监督预训练),正是导致深度学习(神经网络)在2006年第3次兴起
也就是我们这边要学的稀疏自编码就是为了对网络的每一层进行参数初始化,仅仅是为了获得初始的参数值而已(这就是所谓的无监督参数初始化,或者称之为“无监督 pre-training”)。 B、比如采用自编码,我们可以把网络从第一层开始自编码训练,在每一层学习到的隐藏特征表示后作为下一层的输入,然后下一层再进行自编码...
total_batch = int(mnist.train.num_examples/batch_size) # 训练数据 training_epochs为5组实验 for epoch in range(training_epochs): # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x)=1 min(x)=0 # 运行初始化和误差...
init=tf.initialize_all_variables()# 训练集可视化操作withtf.Session()assess:sess.run(init)total_batch=int(mnist.train.num_examples/batch_size)# 训练数据 training_epochs为5组实验forepochinrange(training_epochs):# Loop over all batchesforiinrange(total_batch):batch_xs,batch_ys=mnist.train.next_...
% Hint: If you are debugging your code, performing gradient checkingonsmaller models%andsmaller training sets (e.g., using only10training examplesand1-2hidden%units) may speed things up.% First, lets make sure your numerical gradient computationiscorrectfora% simplefunction. After you have impl...
%% STEP2: Train the first sparse autoencoder% This trains the first sparse autoencoderonthe unlabelled STL training%images.% If you've correctly implemented sparseAutoencoderCost.m, you don't need%tochange anything here.%Randomly initialize the parameters ...
For example, if SparsityProportion is set to 0.1, this is equivalent to saying that each neuron in the hidden layer should have an average output of 0.1 over the training examples. This value must be between 0 and 1. The ideal value varies depending on the nature of the problem. N...
training_epochs = 5 #5组训练 batch_size = 256 #batch大小 display_step = 1 examples_to_show = 10 #显示10个样本 # 神经网络输入设置 n_input = 784 #MNIST输入数据集(28*28) # 隐藏层设置 n_hidden_1 = 256 #第一层特征数量 n_hidden_2 = 128 #第二层特征数量 ...
BootMAE改进了原始的蒙版自编码器(MAE),具有两个核心设计:1)动量编码器,提供在线特征作为额外的BERT预测目标;2)目标感知解码器,试图减轻编码器在BERT预训练中记忆目标特定信息的压力。第一个设计的动机是观察到使用预训练的MAE提取特征作为蒙版标记的BERT预测目标可以实现更好的预训练性能。因此,我们在原始MAE编码器...
Generate the training data. Get rng(0,'twister'); % For reproducibility n = 1000; r = linspace(-10,10,n)'; x = 1 + r*5e-2 + sin(r)./r + 0.2*randn(n,1); Train autoencoder using the training data. Get hiddenSize = 25; autoenc = trainAutoencoder(x',hiddenSize,... ...