update samples from Release-158 as a part of SDK release

SECURITY.md

@@ -1,41 +0,0 @@
-<!-- BEGIN MICROSOFT SECURITY.MD V0.0.7 BLOCK -->
-
-## Security
-
-Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
-
-If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below.
-
-## Reporting Security Issues
-
-**Please do not report security vulnerabilities through public GitHub issues.**
-
-Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report).
-
-If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey).
-
-You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). 
-
-Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
-
-  * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
-  * Full paths of source file(s) related to the manifestation of the issue
-  * The location of the affected source code (tag/branch/commit or direct URL)
-  * Any special configuration required to reproduce the issue
-  * Step-by-step instructions to reproduce the issue
-  * Proof-of-concept or exploit code (if possible)
-  * Impact of the issue, including how an attacker might exploit the issue
-
-This information will help us triage your report more quickly.
-
-If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs.
-
-## Preferred Languages
-
-We prefer all communications to be in English.
-
-## Policy
-
-Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd).
-
-<!-- END MICROSOFT SECURITY.MD BLOCK -->

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py

@@ -1,10 +1,11 @@
-import keras
+from tensorflow.keras.models import Sequential
-from keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten
-from keras.layers import Dense, Dropout, Flatten
+from tensorflow.keras.layers import Conv2D, MaxPooling2D
-from keras.layers import Conv2D, MaxPooling2D
+from tensorflow.keras.layers import BatchNormalization
-from keras.layers.normalization import BatchNormalization
+from tensorflow.keras.losses import categorical_crossentropy
-from keras.utils import to_categorical
+from tensorflow.keras.optimizers import Adam
-from keras.callbacks import Callback
+from tensorflow.keras.utils import to_categorical
+from tensorflow.keras.callbacks import Callback
 
 import numpy as np
 import pandas as pd
@@ -64,8 +65,8 @@ model.add(Dense(128, activation='relu'))
 model.add(Dropout(0.3))
 model.add(Dense(num_classes, activation='softmax'))
 
-model.compile(loss=keras.losses.categorical_crossentropy,
+model.compile(loss=categorical_crossentropy,
-              optimizer=keras.optimizers.Adam(),
+              optimizer=Adam(),
               metrics=['accuracy'])
 
 # start an Azure ML run

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb

@@ -270,16 +270,19 @@
         "%%writefile conda_dependencies.yml\n",
         "\n",
         "dependencies:\n",
-        "- python=3.6.2\n",
+        "- python=3.8\n",
+        "- pip==20.2.4\n",
         "- pip:\n",
         "  - azureml-core\n",
         "  - azureml-dataset-runtime\n",
-        "  - keras==2.4.3\n",
+        "  - keras==2.6\n",
-        "  - tensorflow==2.4.3\n",
+        "  - tensorflow-gpu==2.6\n",
         "  - numpy\n",
         "  - scikit-learn\n",
         "  - pandas\n",
-        "  - matplotlib"
+        "  - matplotlib\n",
+        "  - protobuf==3.20.1\n",
+        "  - typing-extensions==4.3.0"
       ]
     },
     {