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use nd.softmaxoutput

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nttstar
2018-05-23 13:47:56 +08:00
parent ec5b0697e5
commit 6a5686a9cc
1 changed files with 12 additions and 5 deletions
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17
gluon/train.py
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@@ -555,8 +555,12 @@ def train_net(args):
fc7 = net(data, label)
else:
fc7 = net(data)
softmax = mx.symbol.SoftmaxOutput(data=fc7, label = label, name='softmax', normalization='valid')
sym = softmax
#sym = mx.symbol.SoftmaxOutput(data=fc7, label = label, name='softmax', normalization='valid')
ceop = gluon.loss.SoftmaxCrossEntropyLoss()
loss = ceop(fc7, label)
#loss = loss/args.per_batch_size
loss = mx.sym.mean(loss)
sym = mx.sym.Group( [mx.symbol.BlockGrad(fc7), mx.symbol.MakeLoss(loss, name='softmax')] )
def _batch_callback():
mbatch = global_step[0]
@@ -643,7 +647,8 @@ def train_net(args):
loss_weight = 1.0
if args.task=='age':
loss_weight = 1.0/AGE
loss = gluon.loss.SoftmaxCrossEntropyLoss(weight = loss_weight)
#loss = gluon.loss.SoftmaxCrossEntropyLoss(weight = loss_weight)
loss = nd.SoftmaxOutput
#loss = gluon.loss.SoftmaxCrossEntropyLoss()
while True:
#trainer = update_learning_rate(opt.lr, trainer, epoch, opt.lr_factor, lr_steps)
@@ -695,7 +700,9 @@ def train_net(args):
ag.backward(Ls)
#trainer.step(batch.data[0].shape[0], ignore_stale_grad=True)
#trainer.step(args.ctx_num)
trainer.step(batch.data[0].shape[0])
n = batch.data[0].shape[0]
#print(n,n)
trainer.step(n)
metric.update(label, outputs)
if i>0 and i%20==0:
name, acc = metric.get()
@@ -705,7 +712,7 @@ def train_net(args):
else:
logger.info('Epoch[%d] Batch [%d]\tSpeed: %f samples/sec\t%s=%f'%(
num_epochs, i, args.batch_size/(time.time()-btic), name[0], acc[0]))
metric.reset()
#metric.reset()
btic = time.time()
epoch_time = time.time()-tic