You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by zh...@apache.org on 2019/04/03 04:10:02 UTC
[incubator-mxnet] branch master updated: Fixing unintentional
variable overloading (#14438)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 6478691 Fixing unintentional variable overloading (#14438)
6478691 is described below
commit 6478691dc0a11255ee569e0b8d95c1064a830de7
Author: zjost <zj...@gmail.com>
AuthorDate: Tue Apr 2 21:09:40 2019 -0700
Fixing unintentional variable overloading (#14438)
* Fixing unintentional variable overloading
* Adding to conbributor list
* Fixing unintentional variable overloading
* Adding to conbributor list
* Fixing unintentional variable overloading
* Adding to conbributor list
* Fixing unintentional variable overloading
* Adding to conbributor list
---
CONTRIBUTORS.md | 1 +
example/recommenders/matrix_fact.py | 8 ++++----
2 files changed, 5 insertions(+), 4 deletions(-)
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 2b74e12..5c5c217 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -235,6 +235,7 @@ List of Contributors
* [Xinyu Chen](https://github.com/xinyu-intel)
* [Zhennan Qin](https://github.com/ZhennanQin)
* [Zhiyuan Huang](https://github.com/huangzhiyuan)
+* [Zak Jost](https://github.com/zjost)
Label Bot
---------
diff --git a/example/recommenders/matrix_fact.py b/example/recommenders/matrix_fact.py
index 5f7d173..4a438c7 100644
--- a/example/recommenders/matrix_fact.py
+++ b/example/recommenders/matrix_fact.py
@@ -28,14 +28,14 @@ logging.basicConfig(level=logging.DEBUG)
def evaluate_network(network, data_iterator, ctx):
loss_acc = 0.
l2 = gluon.loss.L2Loss()
- for i, (users, items, scores) in enumerate(data_iterator):
+ for idx, (users, items, scores) in enumerate(data_iterator):
users_ = gluon.utils.split_and_load(users, ctx)
items_ = gluon.utils.split_and_load(items, ctx)
scores_ =gluon.utils.split_and_load(scores, ctx)
preds = [network(u, i) for u, i in zip(users_, items_)]
losses = [l2(p, s).asnumpy() for p, s in zip(preds, scores_)]
loss_acc += sum(losses).mean()/len(ctx)
- return loss_acc/(i+1)
+ return loss_acc/(idx+1)
def train(network, train_data, test_data, epochs, learning_rate=0.01, optimizer='sgd', ctx=mx.gpu(0), num_epoch_lr=5, factor=0.2):
@@ -56,7 +56,7 @@ def train(network, train_data, test_data, epochs, learning_rate=0.01, optimizer=
losses_output = []
for e in range(epochs):
loss_acc = 0.
- for i, (users, items, scores) in enumerate(train_data):
+ for idx, (users, items, scores) in enumerate(train_data):
users_ = gluon.utils.split_and_load(users, ctx)
items_ = gluon.utils.split_and_load(items, ctx)
@@ -71,7 +71,7 @@ def train(network, train_data, test_data, epochs, learning_rate=0.01, optimizer=
trainer.update(users.shape[0])
test_loss = evaluate_network(network, test_data, ctx)
- train_loss = loss_acc/(i+1)
+ train_loss = loss_acc/(idx+1)
print("Epoch [{}], Training RMSE {:.4f}, Test RMSE {:.4f}".format(e, train_loss, test_loss))
losses_output.append((train_loss, test_loss))
return losses_output
\ No newline at end of file