def constrain_weight(self, weight_arr):
square_weight_arr = weight_arr * weight_arr
while nd.nansum(square_weight_arr) > self.__weight_limit:
weight_arr = weight_arr * 0.9
square_weight_arr = weight_arr * weight_arr
return weight_arr
def log_pdf(self, y):
return nd.sum(
nd.nansum(y * nd.log_softmax(self.unnormalized_mean),
axis=0,
exclude=True))
def softmax_cross_entropy(yhat_linear, y): # 交叉熵损失
# return - nd.nansum(y * nd.log_softmax(yhat_linear), axis=0, exclude=True)
return -nd.nansum(
y * nd.log(transform_softmax(yhat_linear)), axis=0, exclude=True)
def train(pool_size, epochs, train_data, val_data, ctx, netEn, netDe, netD, trainerEn, trainerDe, trainerD, lambda1, batch_size, expname, append=True, useAE = False):
text_file = open(expname + "_validtest.txt", "w")
text_file.close()
#netGT, netDT, _, _ = set_test_network(opt.depth, ctx, opt.lr, opt.beta1,opt.ndf, opt.ngf, opt.append)
GAN_loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
L1_loss = gluon.loss.L2Loss()
image_pool = imagePool.ImagePool(pool_size)
metric = mx.metric.CustomMetric(facc)
metric2 = mx.metric.MSE()
loss_rec_G = []
loss_rec_D = []
loss_rec_R = []
acc_rec = []
stamp = datetime.now().strftime('%Y_%m_%d-%H_%M')
logging.basicConfig(level=logging.DEBUG)
for epoch in range(epochs):
tic = time.time()
btic = time.time()
train_data.reset()
iter = 0
#print('learning rate : '+str(trainerD.learning_rate ))
for batch in train_data:
############################
# (1) Update D network: maximize log(D(x, y)) + log(1 - D(x, G(x, z)))
###########################
real_in = batch.data[0].as_in_context(ctx)
real_out = batch.data[1].as_in_context(ctx)
soft_zero = 1e-10
fake_latent= netEn(real_in)
fake_latent = np.squeeze(fake_latent)
mu_lv = nd.split(fake_latent, axis=1, num_outputs=2)
mu = (mu_lv[0])
lv = (mu_lv[1])
KL = 0.5*nd.nansum(1+lv-mu*mu-nd.exp(lv+soft_zero))
eps = nd.random_normal(loc=0, scale=1, shape=(batch_size, 2048), ctx=ctx)
z = mu + nd.exp(0.5*lv)*eps
z = nd.expand_dims(nd.expand_dims(z,2),2)
y = netDe(z)
fake_out = y
logloss = nd.nansum(real_in*nd.log(y+soft_zero)+ (1-real_in)*nd.log(1-y+soft_zero))
loss = -logloss-KL
fake_concat = nd.concat(real_in, fake_out, dim=1) if append else fake_out
with autograd.record():
# Train with fake image
# Use image pooling to utilize history imagesi
output = netD(fake_concat)
fake_label = nd.zeros(output.shape, ctx=ctx)
errD_fake = GAN_loss(output, fake_label)
metric.update([fake_label, ], [output, ])
real_concat = nd.concat(real_in, real_out, dim=1) if append else real_out
output = netD(real_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errD_real = GAN_loss(output, real_label)
errD = (errD_real + errD_fake) * 0.5
errD.backward()
metric.update([real_label, ], [output, ])
trainerD.step(batch.data[0].shape[0])
############################
# (2) Update G network: maximize log(D(x, G(x, z))) - lambda1 * L1(y, G(x, z))
###########################
with autograd.record():
fake_latent= np.squeeze(netEn(real_in))
mu_lv = nd.split(fake_latent, axis=1, num_outputs=2)
mu = mu_lv[0]
lv = mu_lv[1]
KL = 0.5*nd.nansum(1+lv-mu*mu-nd.exp(lv+soft_zero))
eps = nd.random_normal(loc=0, scale=1, shape=(batch_size, 2048), ctx=ctx)
#KL = 0.5*nd.nansum(1+lv-mu*mu-nd.exp(lv+soft_zero))
z = mu + nd.exp(0.5*lv)*eps
z = nd.expand_dims(nd.expand_dims(z,2),2)
y = netDe(z)
fake_out = y
logloss = nd.nansum((real_in+1)*0.5*nd.log(0.5*(y+1)+soft_zero)+ (1-0.5*(real_in+1))*nd.log(1-0.5*(y+1)+soft_zero))
loss =-logloss-KL
fake_concat = nd.concat(real_in, fake_out, dim=1) if append else fake_out
output = netD(fake_concat)
real_label = nd.ones(output.shape, ctx=ctx)
errG = GAN_loss(output, real_label) + loss*lambda1 #L1_loss(real_out, fake_out) * lambda1
errR = logloss#L1_loss(real_out, fake_out)
errG.backward()
trainerDe.step(batch.data[0].shape[0])
trainerEn.step(batch.data[0].shape[0])
loss_rec_G.append(nd.mean(errG).asscalar()-nd.mean(errR).asscalar()*lambda1)
loss_rec_D.append(nd.mean(errD).asscalar())
loss_rec_R.append(nd.mean(errR).asscalar())
name, acc = metric.get()
acc_rec.append(acc)
# Print log infomation every ten batches
if iter % 10 == 0:
name, acc = metric.get()
logging.info('speed: {} samples/s'.format(batch_size / (time.time() - btic)))
#print(errD)
logging.info('discriminator loss = %f, generator loss = %f, binary training acc = %f reconstruction error= %f at iter %d epoch %d'
% (nd.mean(errD).asscalar(),
nd.mean(errG).asscalar(), acc,nd.mean(errR).asscalar() ,iter, epoch))
iter = iter + 1
btic = time.time()
name, acc = metric.get()
metric.reset()
train_data.reset()
logging.info('\nbinary training acc at epoch %d: %s=%f' % (epoch, name, acc))
logging.info('time: %f' % (time.time() - tic))
if epoch%10 ==0:
text_file = open(expname + "_validtest.txt", "a")
filename = "checkpoints/"+expname+"_"+str(epoch)+"_D.params"
netD.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_En.params"
netEn.save_params(filename)
filename = "checkpoints/"+expname+"_"+str(epoch)+"_De.params"
netDe.save_params(filename)
fake_img1 = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
fake_img2 = nd.concat(real_in[1],real_out[1], fake_out[1], dim=1)
fake_img3 = nd.concat(real_in[2],real_out[2], fake_out[2], dim=1)
fake_img4 = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
val_data.reset()
text_file = open(expname + "_validtest.txt", "a")
for vbatch in val_data:
real_in = vbatch.data[0].as_in_context(ctx)
real_out = vbatch.data[1].as_in_context(ctx)
fake_latent= netEn(real_in)
mu_lv = nd.split(fake_latent, axis=1, num_outputs=2)
mu = mu_lv[0]
lv = mu_lv[1]
eps = nd.random_normal(loc=0, scale=1, shape=(batch_size/5, 2048,1,1), ctx=ctx)
z = mu + nd.exp(0.5*lv)*eps
y = netDe(z)
fake_out = y
KL = 0.5*nd.sum(1+lv-mu*mu-nd.exp(lv),axis=1)
logloss = nd.sum(real_in*nd.log(y+soft_zero)+ (1-real_in)*nd.log(1-y+soft_zero), axis=1)
loss = logloss+KL
metric2.update([fake_out, ], [real_out, ])
_, acc2 = metric2.get()
text_file.write("%s %s %s\n" % (str(epoch), nd.mean(errR).asscalar(), str(acc2)))
metric2.reset()
fake_img1T = nd.concat(real_in[0],real_out[0], fake_out[0], dim=1)
fake_img2T = nd.concat(real_in[1],real_out[1], fake_out[1], dim=1)
fake_img3T = nd.concat(real_in[2],real_out[2], fake_out[2], dim=1)
#fake_img4T = nd.concat(real_in[3],real_out[3], fake_out[3], dim=1)
fake_img = nd.concat(fake_img1,fake_img2, fake_img3,fake_img1T,fake_img2T, fake_img3T,dim=2)
visual.visualize(fake_img)
plt.savefig('outputs/'+expname+'_'+str(epoch)+'.png')
text_file.close()
return([loss_rec_D,loss_rec_G, loss_rec_R, acc_rec])
def softmax_cross_entropy(yhat_linear, y):
return -nd.nansum(
y * nd.log_softmax(yhat_linear), axis=0, exclude=True)