[singa] branch dev updated: Update inline comments for cifar_distributed_cnn resnet_cifar10

[zh...@apache.org]

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zhaojing pushed a commit to branch dev
in repository https://gitbox.apache.org/repos/asf/singa.git


The following commit(s) were added to refs/heads/dev by this push:
     new b1a8f2e7 Update inline comments for cifar_distributed_cnn resnet_cifar10
     new f1327525 Merge pull request #996 from KimballCai/dev
b1a8f2e7 is described below

commit b1a8f2e70c162d9d7587a787a3e86ee9d2375455
Author: qingpeng <qi...@u.nus.edu>
AuthorDate: Wed Aug 31 21:09:42 2022 +0800

    Update inline comments for cifar_distributed_cnn resnet_cifar10
---
 examples/cifar_distributed_cnn/autograd/resnet_cifar10.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py b/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py
index 0d6379b5..a8e6efd9 100644
--- a/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py
+++ b/examples/cifar_distributed_cnn/autograd/resnet_cifar10.py
@@ -138,7 +138,7 @@ def reduce_variable(variable, dist_opt, reducer):
     return output
 
 
-# Function to sychronize SINGA TENSOR initial model parameters
+# Function to synchronize SINGA TENSOR initial model parameters
 def synchronize(tensor, dist_opt):
     dist_opt.all_reduce(tensor.data)
     dist_opt.wait()
@@ -159,7 +159,7 @@ def train_cifar10(DIST=False,
                   nccl_id=None,
                   partial_update=False):
 
-    # Define the hypermeters for the train_cifar10
+    # Define the hyperparameters for the train_cifar10
     sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
     max_epoch = 5
     batch_size = 32
@@ -199,7 +199,7 @@ def train_cifar10(DIST=False,
     idx = np.arange(train_x.shape[0], dtype=np.int32)
 
     if DIST:
-        #Sychronize the initial parameters
+        # Synchronize the initial parameters
         autograd.training = True
         x = np.random.randn(batch_size, 3, IMG_SIZE,
                             IMG_SIZE).astype(np.float32)
@@ -220,7 +220,7 @@ def train_cifar10(DIST=False,
         if ((DIST == False) or (sgd.global_rank == 0)):
             print('Starting Epoch %d:' % (epoch))
 
-        #Training phase
+        # Training phase
         autograd.training = True
         train_correct = np.zeros(shape=[1], dtype=np.float32)
         test_correct = np.zeros(shape=[1], dtype=np.float32)
@@ -258,11 +258,11 @@ def train_cifar10(DIST=False,
                   flush=True)
 
         if partial_update:
-            # Sychronize parameters before evaluation phase
+            # Synchronize parameters before evaluation phase
             for p in param:
                 synchronize(p, sgd)
 
-        #Evaulation phase
+        # Evaluation phase
         autograd.training = False
         for b in range(num_test_batch):
             x = test_x[b * batch_size:(b + 1) * batch_size]
@@ -275,7 +275,7 @@ def train_cifar10(DIST=False,
                                      to_categorical(y, num_classes))
 
         if DIST:
-            # Reduce the evaulation accuracy from multiple devices
+            # Reduce the evaluation accuracy from multiple devices
             test_correct = reduce_variable(test_correct, sgd, reducer)
 
         # Output the evaluation accuracy