[singa] branch dev updated: Update the MLP example

[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 b637f7d  Update the MLP example
     new a2606a7  Merge pull request #835 from KimballCai/dev
b637f7d is described below

commit b637f7dcdf2fd905e4af789c0642a4fdc349e66a
Author: KimballCai <qi...@u.nus.edu>
AuthorDate: Wed Mar 10 21:30:14 2021 +0800

    Update the MLP example
---
 examples/mlp/model.py  | 8 ++++++--
 examples/mlp/native.py | 6 ++++--
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/examples/mlp/model.py b/examples/mlp/model.py
index 73cd99f..9a6f3a7 100644
--- a/examples/mlp/model.py
+++ b/examples/mlp/model.py
@@ -76,7 +76,10 @@ def create_model(pretrained=False, **kwargs):
     """Constructs a CNN model.
 
     Args:
-        pretrained (bool): If True, returns a model pre-trained
+        pretrained (bool): If True, returns a pre-trained model.
+    
+    Returns:
+        The created CNN model.
     """
     model = MLP(**kwargs)
 
@@ -111,6 +114,7 @@ if __name__ == "__main__":
     f = lambda x: (5 * x + 1)
     bd_x = np.linspace(-1.0, 1, 200)
     bd_y = f(bd_x)
+
     # generate the training data
     x = np.random.uniform(-1, 1, 400)
     y = f(x) + 2 * np.random.randn(len(x))
@@ -129,7 +133,7 @@ if __name__ == "__main__":
     ty = tensor.Tensor((400,), dev, tensor.int32)
     model = MLP(data_size=2, perceptron_size=3, num_classes=2)
 
-    # attached model to graph
+    # attach model to graph
     model.set_optimizer(sgd)
     model.compile([tx], is_train=True, use_graph=args.graph, sequential=True)
     model.train()
diff --git a/examples/mlp/native.py b/examples/mlp/native.py
index 03ff7fd..a82ec3b 100644
--- a/examples/mlp/native.py
+++ b/examples/mlp/native.py
@@ -53,9 +53,11 @@ if __name__ == "__main__":
     f = lambda x: (5 * x + 1)
     bd_x = np.linspace(-1.0, 1, 200)
     bd_y = f(bd_x)
+
     # generate the training data
     x = np.random.uniform(-1, 1, 400)
     y = f(x) + 2 * np.random.randn(len(x))
+
     # convert training data to 2d space
     label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)])
     data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32)
@@ -64,12 +66,12 @@ if __name__ == "__main__":
         """
         Converts a class vector (integers) to binary class matrix.
 
-        Args
+        Args:
             y: class vector to be converted into a matrix
                 (integers from 0 to num_classes).
             num_classes: total number of classes.
 
-        Return
+        Returns:
             A binary matrix representation of the input.
         """
         y = np.array(y, dtype="int")