# load labels labels = imt.readlabels(trfile) m = len(labels) # split to train and cv sets trsplit = int(np.round(m*splitsize)) iterm=range(m) nparams = len(paramc)*len(paramg) scores = np.zeros(nparams) # load in training set # note: this takes up a bit of memory print('Loading training data...') trsetfull = imt.loadimgfromcsv(trfile,colstart=1)/255.0 print('...done') trset = trsetfull[:trsplit] cvset = trsetfull[trsplit:] trlabels = labels[:trsplit] cvlabels = labels[trsplit:] c=5.0 g=0.05 #counter=0 #paramholder=np.zeros([nparams,2]) #bestscore=-1 #for c in paramc: # for g in paramg: # model=SVC(C=c,gamma=g) # print('Fitting model with params c: %f, g: %f ...'%(c,g)) # model=model.fit(trset,trlabels)
layermodels = [] starttime = time.clock() for i in range(len(hlayers)): print('Training hidden layer %d' % i) # create denoising autoencoder, consider more options da = nnt.dAE(alayers[i], alayers[i + 1], noise=noisel[i], errtype=etype) for j in range(epochs): print('Starting epoch %d...' % j) for z in range(nblocks): # load up images if i == 0: x = imt.loadimgfromcsv(trfile, range(blocksplits[z], blocksplits[z + 1]), colstart=1) / 255.0 else: x = imt.loadimgfromcsv(acttmpfile, range(blocksplits[z], blocksplits[z + 1]), colstart=0, headlines=0) nx = x.shape[0] for zz in range(nx): da.GD(x[zz].reshape(x.shape[1], 1), alpha=lr) epochend = time.clock() etime = (epochend - starttime) / 60 print('...finished in %2f minutes' % etime) if i < len(hlayers) - 1: # save out activations
# load labels labels = imt.readlabels(trfile) m = len(labels) # split to train and cv sets trsplit = int(np.round(m * splitsize)) iterm = range(m) nparams = len(paramc) * len(paramg) scores = np.zeros(nparams) # load in training set # note: this takes up a bit of memory print('Loading training data...') trsetfull = imt.loadimgfromcsv(trfile, colstart=1) / 255.0 print('...done') trset = trsetfull[:trsplit] cvset = trsetfull[trsplit:] trlabels = labels[:trsplit] cvlabels = labels[trsplit:] c = 5.0 g = 0.05 #counter=0 #paramholder=np.zeros([nparams,2]) #bestscore=-1 #for c in paramc: # for g in paramg: # model=SVC(C=c,gamma=g) # print('Fitting model with params c: %f, g: %f ...'%(c,g)) # model=model.fit(trset,trlabels)
try: layermodels=pickle.load(open('data/pretune.p','r')) except: layermodels=[] starttime=time.clock() for i in range(len(hlayers)): print('Training hidden layer %d'%i) # create denoising autoencoder, consider more options da=nnt.dAE(alayers[i],alayers[i+1],noise=noisel[i],errtype=etype) for j in range(epochs): print('Starting epoch %d...'%j) for z in range(nblocks): # load up images if i==0: x=imt.loadimgfromcsv(trfile,range(blocksplits[z],blocksplits[z+1]),colstart=1)/255.0 else: x=imt.loadimgfromcsv(acttmpfile,range(blocksplits[z],blocksplits[z+1]),colstart=0,headlines=0) nx=x.shape[0] for zz in range(nx): da.GD(x[zz].reshape(x.shape[1],1),alpha=lr) epochend=time.clock() etime=(epochend-starttime)/60 print('...finished in %2f minutes' %etime) if i<len(hlayers)-1: # save out activations print('Saving out activations...') writeto=acttmpfileroot+'_%d.csv'%i f=open(writeto,'wb') csvf=csv.writer(f) for c in range(nblocks):