In this section, we will create a DBN with two RBM layers and run it on the MNIST dataset. We will modify the input parameters for the DeepBeliefNetwork(..) class:
name = 'dbn'rbm_layers = [256, 256]finetune_act_func ='relu'do_pretrain = Truerbm_learning_rate = [0.001, 0.001]rbm_num_epochs = [5, 5]rbm_gibbs_k= [1, 1]rbm_stddev= 0.1rbm_gauss_visible= Falsemomentum= 0.5rbm_batch_size= [32, 32]finetune_learning_rate = 0.01finetune_num_epochs = 1finetune_batch_size = 32finetune_opt = 'momentum'finetune_loss_func = 'softmax_cross_entropy'finetune_dropout = 1finetune_act_func = tf.nn.sigmoid
Notice that some of the parameters have two elements for array so we need to specify these parameters for two layers:
- rbm_layers ...
