sync and update version number

contributing.md

@@ -73,4 +73,3 @@ Additional information regarding APIs, installation and dependencies and more ca
 * [coremltools](https://github.com/apple/coremltools)
 * [onnx-coreml](https://github.com/onnx/onnx-coreml)
 * [tf-coreml](https://github.com/tf-coreml/tf-coreml)
-

coremltools/__init__.py

@@ -22,7 +22,7 @@
 For more information: http://developer.apple.com/documentation/coreml
 """
 
-__version__ = '3.0'
+__version__ = '3.1'
 
 # This is the basic Core ML specification format understood by iOS 11.0
 SPECIFICATION_VERSION = 1

coremltools/models/model.py

@@ -372,7 +372,6 @@ def visualize_spec(self, port=None, input_shape_dict=None, title='CoreML Graph V
 
         Returns
         -------
-
         None
 
         Examples

coremltools/test/neural_network/test_keras2.py

@@ -329,7 +329,7 @@ def test_pooling(self):
         # Create a simple Keras model
         model = Sequential()
         model.add(Conv2D(input_shape=(64, 64, 3),
-                                filters=32, kernel_size=(5,5), strides=(1,1), activation=None, 
+                                filters=32, kernel_size=(5,5), strides=(1,1), activation=None,
                                 padding='valid', use_bias=True))
         model.add(MaxPooling2D(pool_size=(2,2)))
 
@@ -611,7 +611,7 @@ def test_sentiment_analysis(self):
         self.assertTrue(spec.HasField('neuralNetwork'))
 
         # Test the inputs and outputs
-        # We're giving state input and output so expect description to differ. 
+        # We're giving state input and output so expect description to differ.
         self.assertEquals(len(spec.description.input), len(input_names) + 2)
         self.assertEquals(len(spec.description.output), len(output_names) + 2)
 
@@ -657,7 +657,7 @@ def test_batchnorm(self):
         # Create a simple Keras model
         model = Sequential()
         model.add(Conv2D(input_shape=(64, 64, 3),
-                               filters=32, kernel_size=(5,5), strides=(1,1), activation=None, 
+                               filters=32, kernel_size=(5,5), strides=(1,1), activation=None,
                                padding='valid', use_bias=True))
         # epsilon in CoreML is currently fixed at 1e-5
         model.add(BatchNormalization(epsilon=1e-5))
@@ -731,8 +731,8 @@ def test_image_processing(self):
         # Create a simple Keras model
         model = Sequential()
         model.add(Conv2D(input_shape=(64, 64, 3),
-                         filters=32, kernel_size=(5,5), 
-                         activation=None, padding='valid', 
+                         filters=32, kernel_size=(5,5),
+                         activation=None, padding='valid',
                          strides=(1, 1), use_bias=True))
         input_names = ['input']
         output_names = ['output']
@@ -1270,7 +1270,7 @@ def test_updatable_model_flag_cce_string_sgd(self):
         self.assertEqual(sgdopt.miniBatchSize.defaultValue, 16)
         self.assertEqual(sgdopt.momentum.defaultValue, 0.0)
 
-    
+
     def test_updatable_model_flag_cce_sgd_string(self):
         """
         Tests the 'respect_trainable' flag when used along with string
@@ -1344,4 +1344,3 @@ def test_updatable_model_flag_cce_adam_string(self):
         self.assertAlmostEqual(adopt.miniBatchSize.defaultValue, 16)
         self.assertAlmostEqual(adopt.beta1.defaultValue, 0.90, places=5)
         self.assertAlmostEqual(adopt.beta2.defaultValue, 0.999, places=5)
-

docs/APIExamples.md

@@ -1,7 +1,8 @@
+docs/APIExamples.md
 # API Code snippets
 
 ## Converting between MLModel and Spec
- 
+
 ```python
 import coremltools
 
@@ -52,7 +53,7 @@ print_network_spec(spec)
 
 Another useful tool for visualizing CoreML models and models from other frameworks: [Netron](https://github.com/lutzroeder/netron)
 
-## Printing the pre-processing parameters 
+## Printing the pre-processing parameters
 
 This is useful for image based neural network models
 
@@ -77,7 +78,7 @@ print(nn.preprocessing)
 ## Changing MLMultiArray input/output datatypes
 
 [Here](https://github.com/apple/coremltools/blob/d07421460f9f0ad1a2e9cf8b5248670358a24a1a/mlmodel/format/FeatureTypes.proto#L106 ) is the list of supported datatypes.
-For instance, change the datatype from 'double' to 'float32': 
+For instance, change the datatype from 'double' to 'float32':
 
 ```python
 import coremltools
@@ -115,7 +116,7 @@ import PIL.Image
 
 model = coremltools.models.MLModel('path/to/the/saved/model.mlmodel')
 
-Height = 20  # use the correct input image height 
+Height = 20  # use the correct input image height
 Width = 60  # use the correct input image width
 
 
@@ -129,7 +130,7 @@ def load_image(path, resize_to=None):
     return img_np, img
 
 
-# load the image and resize using PIL utilities 
+# load the image and resize using PIL utilities
 _, img = load_image('/path/to/image.jpg', resize_to=(Width, Height))
 out_dict = model.predict({'image': img})
 
@@ -143,8 +144,8 @@ out_dict = model.predict({'image': pil_img})
 
 ## Building an mlmodel from scratch using Neural Network Builder
 
-We can use the neural network builder class to construct a CoreML model. Lets look at an example of 
-making a tiny 2 layer model with a convolution layer (with random weights) and an activation. 
+We can use the neural network builder class to construct a CoreML model. Lets look at an example of
+making a tiny 2 layer model with a convolution layer (with random weights) and an activation.
 
 ```python
 import coremltools