Update the allowable values for the argument and README.md for two CNN examples
diff --git a/examples/cnn/train_cnn.py b/examples/cnn/train_cnn.py
index 6345fdb..0fbf8f3 100644
--- a/examples/cnn/train_cnn.py
+++ b/examples/cnn/train_cnn.py
@@ -272,10 +272,10 @@
description='Training using the autograd and graph.')
parser.add_argument(
'model',
- choices=['resnet', 'xceptionnet', 'cnn', 'mlp', 'alexnet'],
+ choices=['cnn', 'resnet', 'xceptionnet', 'mlp', 'alexnet'],
default='cnn')
parser.add_argument('data',
- choices=['cifar10', 'cifar100', 'mnist'],
+ choices=['mnist', 'cifar10', 'cifar100'],
default='mnist')
parser.add_argument('-p',
choices=['float32', 'float16'],
diff --git a/examples/cnn/train_mpi.py b/examples/cnn/train_mpi.py
index 7c09d12..4f71dad 100644
--- a/examples/cnn/train_mpi.py
+++ b/examples/cnn/train_mpi.py
@@ -31,9 +31,9 @@
parser = argparse.ArgumentParser(
description='Training using the autograd and graph.')
parser.add_argument('model',
- choices=['resnet', 'xceptionnet', 'cnn', 'mlp'],
+ choices=['cnn', 'resnet', 'xceptionnet', 'mlp'],
default='cnn')
- parser.add_argument('data', choices=['cifar10', 'cifar100', 'mnist'], default='mnist')
+ parser.add_argument('data', choices=['mnist', 'cifar10', 'cifar100'], default='mnist')
parser.add_argument('-p',
choices=['float32', 'float16'],
default='float32',
diff --git a/examples/largedataset_cnn/README.md b/examples/largedataset_cnn/README.md
index d872044..915d361 100644
--- a/examples/largedataset_cnn/README.md
+++ b/examples/largedataset_cnn/README.md
@@ -21,16 +21,16 @@
Examples inside this folder show how to train CNN models using SINGA for image classification.
-It read the dataset from the filesystem defined by `process_data.py`. Hence, users can modify `process_data.py`
+It reads the dataset from the filesystem defined by `process_data.py`. Hence, users can modify `process_data.py`
for their perference of dataset format.
-In the current setting, 'classes.txt' contains the names of the classes at each line. For example, if it is food dataset containing three classes, 'classes.txt' may read like this:
+In the current setting, 'classes.txt' contains the names of the classes at each line. For example, if it is a food dataset containing three classes, 'classes.txt' may read like this:
Fish_and_chips
bagel_and_croissant
bak_kut_teh
-Then, the directory '/Data/' contains all the folders for images of different classes, while each of the folder names should be the same as that appeared in 'classes.txt'. The name of an image file should not be a concern, but it should be placed inside the folder of the class it belongs to. For the same example above, the folder structure may look like this:
+Then, the directory '/Data/' contains all the folders for images of different classes, while each folder name should be the same as that appeared in 'classes.txt'. The name of an image file should not be a concern, but it should be placed inside the folder of the class it belongs to. For the same example above, the folder structure may look like this:
Data/
Fish_and_chips/
