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update samples from Release-169 as a part of 1.0.85 SDK release

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vizhur
2020-01-21 22:46:25 +00:00
parent 512630472b
commit 2b3a3b8f9a
57 changed files with 725 additions and 1881 deletions
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57
tutorials/img-classification-part2-deploy.ipynb
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@@ -62,18 +62,7 @@
" model_name=model_name,\n",
" tags={\"data\": \"mnist\", \"model\": \"classification\"},\n",
" description=\"Mnist handwriting recognition\",\n",
" workspace=ws)\n",
"\n",
"# download test data\n",
"import os\n",
"import urllib.request\n",
"\n",
"data_folder = os.path.join(os.getcwd(), 'data')\n",
"os.makedirs(data_folder, exist_ok = True)\n",
"\n",
"\n",
"urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'test-images.gz'))\n",
"urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'test-labels.gz'))"
" workspace=ws)"
]
},
{
@@ -150,10 +139,42 @@
"## Test model locally\n",
"\n",
"Before deploying, make sure your model is working locally by:\n",
"* Downloading the test data if you haven't already\n",
"* Loading test data\n",
"* Predicting test data\n",
"* Examining the confusion matrix\n",
"* Examining the confusion matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download test data\n",
"If you haven't already, download the test data to the **./data/** directory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# download test data\n",
"import os\n",
"import urllib.request\n",
"\n",
"data_folder = os.path.join(os.getcwd(), 'data')\n",
"os.makedirs(data_folder, exist_ok = True)\n",
"\n",
"\n",
"urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'test-images.gz'))\n",
"urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'test-labels.gz'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load test data\n",
"\n",
"Load the test data from the **./data/** directory created during the training tutorial."
@@ -190,10 +211,11 @@
"outputs": [],
"source": [
"import pickle\n",
"from sklearn.externals import joblib\n",
"import joblib\n",
"\n",
"clf = joblib.load( os.path.join(os.getcwd(), 'sklearn_mnist_model.pkl'))\n",
"y_hat = clf.predict(X_test)"
"y_hat = clf.predict(X_test)\n",
"print(y_hat)"
]
},
{
@@ -286,8 +308,7 @@
"import numpy as np\n",
"import os\n",
"import pickle\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import LogisticRegression\n",
"import joblib\n",
"\n",
"def init():\n",
" global model\n",
@@ -327,7 +348,7 @@
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies()\n",
"myenv.add_conda_package(\"scikit-learn\")\n",
"myenv.add_pip_package(\"scikit-learn\")\n",
"myenv.add_pip_package(\"azureml-defaults\")\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",