Exemple #1
0
learn_rate=8e-3
updts_old=0.
epoch=0.
updts_new=0.
epochs=40
cost=np.zeros(epochs)
num_layers=1
modes=np.arange(1,4)
for k in range(epochs):
 #np.random.shuffle(modes)
	nu_max=np.asarray(np.arange(1980,3000,4))
	np.random.shuffle(nu_max)
	for j in range(nu_max.size):
		start_time=timeit.default_timer()
		if (updts_old==0.):
			[c,updts_old]=train_spectra(nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=0,bhid=0,bvis=0)
				#cost[i-min_modes,j]=c.eval()
		else:
			Wlist=[]
			blist=[]
			bvislist=[]
			for item in range(len(updts_old)):
				Wlist.append(updts_old[item][0][1])
				blist.append(updts_old[item][1][1])
				bvislist.append(updts_old[item][2][1])
			updts_old=0.
			[c,updts_old]=train_spectra(nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=Wlist,bhid=blist,bvis=bvislist)
				#cost[i-min_modes,j]=c.eval()
		end_time=timeit.default_timer()
		print("Sample %d completed in %d s time"%(j,(end_time-start_time)))
		cost[k]+=np.mean([item.eval() for item in c])
Exemple #2
0
def train_func():
    import train_deep_spectra
    from train_deep_spectra import train_spectra
    import numpy as np
    import pandas as pd
    import cPickle as cp
    import timeit
    #from theano import shared

    #import os
    #procno=int(os.environ["PBS_VNODENAME"])
    num_modes=3
    min_modes=12
    #cost=np.zeros(num_modes*nu_max.size)
    #cost=cost.reshape(num_modes,nu_max.size)
    import theano
    nhidden=1000
    learn_rate=3e-3
    updts_old=0.
    epoch=0.
    updts_new=0.
    epochs=20
    cost_perepoch=np.zeros(epochs)
    num_layers=1
    modes=np.arange(1,4)
    freqs=np.arange(1956,3000,4)
    sig_noise=np.load('/home/rakesh/sig_noise.npy')
    sig=np.load('/home/rakesh/sig.npy')
    sig_size=np.size(sig[0,:])
    sig_t=theano.shared(np.asarray(sig,dtype=theano.config.floatX),borrow=True)
    sig_noise_t=theano.shared(np.asarray(sig_noise,dtype=theano.config.floatX),borrow=True)
    cost=[]
    for k in range(epochs):
     #np.random.shuffle(modes)
     
            start_time=timeit.default_timer()
            nu_max=np.asarray(np.arange(1956,3000,4))
            np.random.shuffle(nu_max)
            for j in range(nu_max.size):
                    #for i in range(num_modes):
                    p=np.ravel(np.where(freqs==nu_max[j]))
                    start_sample_time=timeit.default_timer()
                    if (updts_old==0.):
                            #st=timeit.default_timer()
                            [c,updts_old]=train_spectra(sig_t[p[0],:],sig_noise_t[p[0],:],sig_size,nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=0,bhid=0,bvis=0)
                            #en=timeit.default_timer()
                            #print("takes %3.4f s in one train"%(en-st))
                                    #cost[i-min_modes,j]=c.eval()
                    else:
                            Wlist=[]
                            blist=[]
                            bvislist=[]
                            for item in range(len(updts_old)):
                                    Wlist.append(updts_old[item][0][1])
                                    blist.append(updts_old[item][1][1])
                                    bvislist.append(updts_old[item][2][1])
                            updts_old=0.
                            #st=timeit.default_timer()
                            [c,updts_old]=train_spectra(sig_t[p[0],:],sig_noise_t[p[0],:],sig_size,nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=Wlist,bhid=blist,bvis=bvislist)
                            #en=timeit.default_timer()
                            #print("takes %3.4f s in one train"%(en-st))
                            #cost[k]+=np.mean([item.eval() for item in c])
                            #cost[k]+=c
                    cost.append(c)
                    end_sample_time=timeit.default_timer()
                    print("Sample %d takes %3.4f s time"%(j,(end_sample_time-start_sample_time)))
            cost_perepoch[k]+=np.mean([item[0].eval() for item in cost])
            end_time=timeit.default_timer()
            print("Completed training in epoch %3.4fs"%(end_time-start_time))

            
    df=pd.DataFrame(cost_perepoch/k)
    df.to_csv('/home/rakesh/Code/Code_gpuopt/Results/cost.csv')
    f=file('/home/rakesh/Code/Code_gpuopt/Results/nnparams.sav','wb')
    cp.dump(updts_old,f,protocol=cp.HIGHEST_PROTOCOL)
    f.close()
Exemple #3
0
sig_t=theano.shared(np.asarray(sig,dtype=theano.config.floatX),borrow=True)
sig_noise_t=theano.shared(np.asarray(sig_noise,dtype=theano.config.floatX),borrow=True)
cost=[]
for k in range(epochs):
 #np.random.shuffle(modes)
 
	start_time=timeit.default_timer()
	nu_max=np.asarray(np.arange(1956,3000,4))
	np.random.shuffle(nu_max)
	for j in range(nu_max.size):
		#for i in range(num_modes):
		p=np.ravel(np.where(freqs==nu_max[j]))
		start_sample_time=timeit.default_timer()
		if (updts_old==0.):
			#st=timeit.default_timer()
                        [c,updts_old]=train_spectra(sig_t[p[0],:],sig_noise_t[p[0],:],sig_size,nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=0,bhid=0,bvis=0)
			#en=timeit.default_timer()
			#print("takes %3.4f s in one train"%(en-st))
				#cost[i-min_modes,j]=c.eval()
		else:
			Wlist=[]
			blist=[]
			bvislist=[]
			for item in range(len(updts_old)):
				Wlist.append(updts_old[item][0][1])
				blist.append(updts_old[item][1][1])
				bvislist.append(updts_old[item][2][1])
			updts_old=0.
			#st=timeit.default_timer()
                        [c,updts_old]=train_spectra(sig_t[p[0],:],sig_noise_t[p[0],:],sig_size,nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=Wlist,bhid=blist,bvis=bvislist)
			#en=timeit.default_timer()
def run_train(nnparams=0):
    import train_deep_spectra
    from train_deep_spectra import train_spectra
    import numpy as np
    import pandas as pd
    import cPickle as cp
    import timeit
    from theano import tensor as T
    #from theano import shared

    #import os
    #procno=int(os.environ["PBS_VNODENAME"])
    num_modes=3
    min_modes=12
    #cost=np.zeros(num_modes*nu_max.size)
    #cost=cost.reshape(num_modes,nu_max.size)
    import theano
    nhidden=1000
    learn_rate=3e-3
    updts_old=nnparams
    epoch=0.
    updts_new=0.
    epochs=1
    cost_perepoch=np.zeros(epochs)
    num_layers=1
    modes=np.arange(1,4)
    freqs=np.arange(1956,3000,4)
    sig_noise=np.load('/home/rakesh/sig_noise.npy')
    sig=np.load('/home/rakesh/sig.npy')
    sig_size=np.size(sig[0,:])
    sig_t=theano.shared(np.asarray(sig,dtype=theano.config.floatX),borrow=True)
    sig_noise_t=theano.shared(np.asarray(sig_noise,dtype=theano.config.floatX),borrow=True)
    for k in range(epochs):
     #np.random.shuffle(modes)
            cost=[]
            start_time=timeit.default_timer()
            nu_max=np.asarray(np.arange(1956,3000,4))
            np.random.shuffle(nu_max)
            for j in range(nu_max.size):
                    #for i in range(num_modes):
                    p=np.ravel(np.where(freqs==nu_max[j]))
                    start_sample_time=timeit.default_timer()
                    if (updts_old==0.):
                            #st=timeit.default_timer()
                            [c,updts_old]=train_spectra(sig_t[p[0],:],sig_noise_t[p[0],:],sig_size,nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=0,bhid=0,bvis=0)
                            #en=timeit.default_timer()
                            #print("takes %3.4f s in one train"%(en-st))
                                    #cost[i-min_modes,j]=c.eval()
                    else:
                            Wlist=[]
                            blist=[]
                            bvislist=[]
                            for item in range(len(updts_old)):
                                    Wlist.append(updts_old[item][0][1])
                                    blist.append(updts_old[item][1][1])
                                    bvislist.append(updts_old[item][2][1])
                            updts_old=0.
                            #st=timeit.default_timer()
                            [c,updts_old]=train_spectra(sig_t[p[0],:],sig_noise_t[p[0],:],sig_size,nu_max[j],modes[0],nhidden,learn_rate,num_layers=num_layers,W=Wlist,bhid=blist,bvis=bvislist)
                            #en=timeit.default_timer()
                            #print("takes %3.4f s in one train"%(en-st))
                            #cost[k]+=np.mean([item.eval() for item in c])
                            #cost[k]+=c
                    cost.append(c[0])
                    end_sample_time=timeit.default_timer()
                    print("Sample %f takes %3.4f s time"%(j,(end_sample_time-start_sample_time)))
           # cost_perepoch[k]+=np.mean([item.eval() for item in cost])

            end_time=timeit.default_timer()
            #print(sum(cost))
            #cost_perepoch[k]=sum(cost)/(j+1)
            print("Completed training in epoch %d in %3.4fs"%((k+1),(end_time-start_time)))

    return(np.mean(cost),updts_old)