Attached model to graph
diff --git a/examples/largedataset_cnn/train_largedata.py b/examples/largedataset_cnn/train_largedata.py
index 69097a4..3da5aac 100755
--- a/examples/largedataset_cnn/train_largedata.py
+++ b/examples/largedataset_cnn/train_largedata.py
@@ -164,7 +164,7 @@
num_val_batch = val_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
- # attached model to graph
+ # Attached model to graph
model.set_optimizer(sgd)
model.compile([tx], is_train=True, use_graph=graph, sequential=sequential)
dev.SetVerbosity(verbosity)
@@ -207,7 +207,7 @@
train_loss += tensor.to_numpy(loss)[0]
if DIST:
- # Reduce the Evaluation Accuracy and Loss from Multiple Devices
+ # Reduce the evaluation accuracy and loss from multiple devices
reducer = tensor.Tensor((1,), dev, tensor.float32)
train_correct = reduce_variable(train_correct, sgd, reducer)
train_loss = reduce_variable(train_loss, sgd, reducer)
@@ -230,7 +230,7 @@
test_correct += accuracy(tensor.to_numpy(out_test), y)
if DIST:
- # Reduce the Evaulation Accuracy from Multiple Devices
+ # Reduce the evaulation accuracy from multiple devices
test_correct = reduce_variable(test_correct, sgd, reducer)
# Output the Evaluation Accuracy
