szha closed pull request #9805: Enable the reporting of cross-entropy or nll
loss value when training CNN network using the models defined by
example/image-classification
URL: https://github.com/apache/incubator-mxnet/pull/9805
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diff --git a/example/image-classification/common/fit.py
b/example/image-classification/common/fit.py
index d9f96d0eba..0e0cd521f2 100755
--- a/example/image-classification/common/fit.py
+++ b/example/image-classification/common/fit.py
@@ -117,6 +117,8 @@ def add_fit_args(parser):
help='load the model on an epoch using the
model-load-prefix')
train.add_argument('--top-k', type=int, default=0,
help='report the top-k accuracy. 0 means no report.')
+ train.add_argument('--loss', type=str, default='',
+ help='show the cross-entropy or nll loss. ce strands
for cross-entropy, nll-loss stands for likelihood loss')
train.add_argument('--test-io', type=int, default=0,
help='1 means test reading speed without training')
train.add_argument('--dtype', type=str, default='float32',
@@ -260,6 +262,23 @@ def fit(args, network, data_loader, **kwargs):
eval_metrics.append(mx.metric.create(
'top_k_accuracy', top_k=args.top_k))
+ supported_loss = ['ce', 'nll_loss']
+ if len(args.loss) > 0:
+ # ce or nll loss is only applicable to softmax output
+ loss_type_list = args.loss.split(',')
+ if 'softmax_output' in network.list_outputs():
+ for loss_type in loss_type_list:
+ loss_type = loss_type.strip()
+ if loss_type == 'nll':
+ loss_type = 'nll_loss'
+ if loss_type not in supported_loss:
+ logging.warning(loss_type + ' is not an valid loss type,
only cross-entropy or ' \
+ 'negative likelihood loss is supported!')
+ else:
+ eval_metrics.append(mx.metric.create(loss_type))
+ else:
+ logging.warning("The output is not softmax_output, loss argument
will be skipped!")
+
# callbacks that run after each batch
batch_end_callbacks = [mx.callback.Speedometer(
args.batch_size, args.disp_batches)]
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