#act = ['softplus'] act = ['softplus','sigmoid','tanh'] #act = ['softplus','softplus','softplus','softplus'] test = autoencoder(units,act) test.generate_encoder() test.generate_decoder(act) #ts = test.init_network() #ba = batch.knn_batch(data,5) #ba.extend(batch.knn_batch(data,8)) #ba.extend(batch.knn_batch(data,15)) ba = batch.rand_batch(data,n_batch) #ba = batch.seq_batch(data,n_batch) print ts.run(test.layers[0].W) #test.pre_train(data) #print ts.run(test.layers[0].W) p = test.pre_train_rbm(data,n_iters=100) print p[1] print ts.run(test.layers[0].W) #test.train(data,batch=ba,display=True,n_iters=1000,noise=False,noise_level=0.25)
if(options.class_label=='all'): data = arr else: data = np.asarray([arr for (arr,lab) in zip(arr,lab) if(lab==int(options.class_label))]).astype("float32") if(bool(options.rem_mean)): m_d = np.cumsum(data,axis=0)[-1]/data.shape[0] data = data-m_d print options auto = autoencoder(units,action) auto.generate_encoder(euris=options.euris) auto.generate_decoder(symmetric=options.symm) if(options.pre_train == 'rbm'): auto.pre_train_rbm(data,n_iters=10,adapt_learn=True,learning_rate=float(options.pre_learn_rate)) elif(options.pre_train == 'standard'): auto.pre_train(data) #auto.pre_train_rbm(data,n_iters=10,learning_rate=float(options.pre_learn_rate),adapt_learn=True) if(not options.batch): bat = None else: from tools.data_manipulation.batch import rand_batch bat = rand_batch(data,int(options.n_batch)) auto.train(data,n_iters=int(options.iters),record_weight=True,w_file=options.w_file,use_dropout=options.drop_out,keep_prob=k,reg_weight=options.reg_w,reg_lambda=options.reg_lambda,model_name=options.model_name,batch=bat,display=False,noise=True,gradient=options.gradient,learning_rate=float(options.learn_rate))
import autoencoder import math import numpy as np import sys from tools.data_manipulation import batch data = np.loadtxt("../datasets/multi_pie.dat") data = data+abs(np.min(data)) data = data/np.max(data) data = data.astype("float32") bat = batch.rand_batch(data,1000) #units = [data.shape[1],int(math.ceil(data.shape[1]*1.2))+5,int(max(math.ceil(data.shape[1]/4),int_dim+2)+3), # int(max(math.ceil(data.shape[1]/10),int_dim+1)),int_dim] units = [5600,1100,200,int(sys.argv[1])] act = ['sigmoid','sigmoid','sigmoid'] #act = ['relu','relu','relu','relu'] auto = autoencoder.autoencoder(units,act) auto.generate_encoder(euris=True) auto.generate_decoder(symmetric=True) #auto.pre_train(data,n_iters=5000) session = auto.init_network() ic,bc = auto.train(data,n_iters=5000,record_weight=False,w_file='./pie_weights_20',use_dropout=True,keep_prob=0.5,reg_weight=False,reg_lambda=0.0,model_name=sys.argv[2],batch=bat,display=False,noise=False,gradient='adam',learning_rate=0.0000125)
#act = ['softplus'] act = ['softplus','sigmoid','tanh'] #act = ['softplus','softplus','softplus','softplus'] test = autoencoder(units,act) test.generate_encoder() test.generate_decoder(act) ts = test.init_network() #ba = batch.knn_batch(data,5) #ba.extend(batch.knn_batch(data,8)) #ba.extend(batch.knn_batch(data,15)) ba = batch.rand_batch(data,n_batch) #ba = batch.seq_batch(data,n_batch) #print ts.run(test.layers[0].W) #test.pre_train(data) #print ts.run(test.layers[0].W) test.pre_train(data,n_iters=2000) test.train(data,batch=ba,display=True,n_iters=1000,noise=False,noise_level=0.25)