MLN repo autocleanup

tutorials/README.md

@@ -1,22 +1,22 @@
-## Azure Machine Learning service Tutorial
-
-Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the
-two sets of tutorial articles for:
-
- * [Image classification using MNIST dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml)
- * [Regression using NYC Taxi dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep)
-
-If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
-
-### Image classification
-
- * [Part 1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning.
- * [Part 2](img-classification-part2-deploy.ipynb): Deploy an image classification model from first tutorial in Azure Container Instance (ACI).
-
- ### Regression
- * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK. 
- * [Part 2](regression-part2-automated-ml.ipynb): Train a model using Automated Machine Learning.
-
- Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
-
+## Azure Machine Learning service Tutorial
+
+Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the
+two sets of tutorial articles for:
+
+ * [Image classification using MNIST dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml)
+ * [Regression using NYC Taxi dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep)
+
+If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
+
+### Image classification
+
+ * [Part 1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning.
+ * [Part 2](img-classification-part2-deploy.ipynb): Deploy an image classification model from first tutorial in Azure Container Instance (ACI).
+
+ ### Regression
+ * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK. 
+ * [Part 2](regression-part2-automated-ml.ipynb): Train a model using Automated Machine Learning.
+
+ Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png) 

tutorials/img-classification-part1-training.yml

@@ -1,7 +1,7 @@
-name: img-classification-part1-training
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - matplotlib
-  - sklearn
+name: img-classification-part1-training
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets
+  - matplotlib
+  - sklearn

tutorials/img-classification-part2-deploy.yml

@@ -1,6 +1,6 @@
-name: img-classification-part2-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - matplotlib
-  - sklearn
+name: img-classification-part2-deploy
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - sklearn

tutorials/regression-part1-data-prep.yml

@@ -1,5 +1,5 @@
-name: regression-part1-data-prep
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-dataprep[pandas]>=1.1.2,<1.2.0
+name: regression-part1-data-prep
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-dataprep[pandas]>=1.1.2,<1.2.0

tutorials/regression-part2-automated-ml.yml

@@ -1,10 +1,10 @@
-name: regression-part2-automated-ml
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - azureml-explain-model
-  - matplotlib
-  - pandas_ml
-  - seaborn
+name: regression-part2-automated-ml
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - azureml-explain-model
+  - matplotlib
+  - pandas_ml
+  - seaborn

tutorials/utils.py

@@ -1,27 +1,27 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License.
-
-import gzip
-import numpy as np
-import struct
-
-
-# load compressed MNIST gz files and return numpy arrays
-def load_data(filename, label=False):
-    with gzip.open(filename) as gz:
-        struct.unpack('I', gz.read(4))
-        n_items = struct.unpack('>I', gz.read(4))
-        if not label:
-            n_rows = struct.unpack('>I', gz.read(4))[0]
-            n_cols = struct.unpack('>I', gz.read(4))[0]
-            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
-            res = res.reshape(n_items[0], n_rows * n_cols)
-        else:
-            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
-            res = res.reshape(n_items[0], 1)
-    return res
-
-
-# one-hot encode a 1-D array
-def one_hot_encode(array, num_of_classes):
-    return np.eye(num_of_classes)[array.reshape(-1)]
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import gzip
+import numpy as np
+import struct
+
+
+# load compressed MNIST gz files and return numpy arrays
+def load_data(filename, label=False):
+    with gzip.open(filename) as gz:
+        struct.unpack('I', gz.read(4))
+        n_items = struct.unpack('>I', gz.read(4))
+        if not label:
+            n_rows = struct.unpack('>I', gz.read(4))[0]
+            n_cols = struct.unpack('>I', gz.read(4))[0]
+            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
+            res = res.reshape(n_items[0], n_rows * n_cols)
+        else:
+            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
+            res = res.reshape(n_items[0], 1)
+    return res
+
+
+# one-hot encode a 1-D array
+def one_hot_encode(array, num_of_classes):
+    return np.eye(num_of_classes)[array.reshape(-1)]