fix breaking change from opendatasets

Author: sdgilley

articles/machine-learning/tutorial-deploy-models-with-aml.md

@@ -191,11 +191,12 @@ Load the test data from the **./data/** directory created during the training tu
 ```python
 from utils import load_data
 import os
+import glob
 
 data_folder = os.path.join(os.getcwd(), 'data')
 # note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster
-X_test = load_data(os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'), False) / 255.0
-y_test = load_data(os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'), True).reshape(-1)
+X_test = load_data(glob.glob(os.path.join(data_folder,"**/t10k-images-idx3-ubyte.gz"), recursive=True)[0], False) / 255.0
+y_test = load_data(glob.glob(os.path.join(data_folder,"**/t10k-labels-idx1-ubyte.gz"), recursive=True)[0], True).reshape(-1)
 ```
 
 ### Predict test data

articles/machine-learning/tutorial-train-models-with-aml.md

@@ -185,12 +185,15 @@ Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30
 ```python
 # make sure utils.py is in the same directory as this code
 from utils import load_data
+import glob
+
 
 # note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.
-X_train = load_data(os.path.join(data_folder, "train-images-idx3-ubyte.gz"), False) / 255.0
-X_test = load_data(os.path.join(data_folder, "t10k-images-idx3-ubyte.gz"), False) / 255.0
-y_train = load_data(os.path.join(data_folder, "train-labels-idx1-ubyte.gz"), True).reshape(-1)
-y_test = load_data(os.path.join(data_folder, "t10k-labels-idx1-ubyte.gz"), True).reshape(-1)
+X_train = load_data(glob.glob(os.path.join(data_folder,"**/train-images-idx3-ubyte.gz"), recursive=True)[0], False) / 255.0
+X_test = load_data(glob.glob(os.path.join(data_folder,"**/t10k-images-idx3-ubyte.gz"), recursive=True)[0], False) / 255.0
+y_train = load_data(glob.glob(os.path.join(data_folder,"**/train-labels-idx1-ubyte.gz"), recursive=True)[0], True).reshape(-1)
+y_test = load_data(glob.glob(os.path.join(data_folder,"**/t10k-labels-idx1-ubyte.gz"), recursive=True)[0], True).reshape(-1)
+
 
 # now let's show some randomly chosen images from the traininng set.
 count = 0