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update samples from Release-159 as a part of SDK release

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amlrelsa-ms
2022-09-13 00:52:37 +00:00
parent 9743c0a1fa
commit 859897e3c0
3 changed files with 17 additions and 54 deletions
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41
SECURITY.md
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@@ -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 -->

19
how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py
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@@ -1,10 +1,11 @@
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.utils import to_categorical
from keras.callbacks import Callback
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.losses import categorical_crossentropy
from tensorflow.keras.optimizers import Adam
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,
optimizer=keras.optimizers.Adam(),
model.compile(loss=categorical_crossentropy,
optimizer=Adam(),
metrics=['accuracy'])
# start an Azure ML run

11
how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb
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@@ -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",
" - tensorflow==2.4.3\n",
" - keras==2.6\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"
]
},
{