update documentation (#972)

Signed-off-by: xadupre <xadupre@microsoft.com>

.buildinfo

@@ -1,4 +1,4 @@
-# Sphinx build info version 1
-# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 62f8ee6a3f6aefe7ce3ae4611d664fc1
-tags: 645f666f9bcd5a90fca523b33c5a78b7
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: ee4940ae2593c690ef5ae868e708b4af
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/01727087b155e5345657ebbe183f11e3/plot_gbegin_cst.ipynb

@@ -1,144 +1,144 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Store arrays in one onnx graph\n\nOnce a model is converted it can be useful to store an\narray as a constant in the graph an retrieve it through\nan output. This allows the user to store training parameters\nor other informations like a vocabulary.\nLast sections shows how to remove an output or to promote\nan intermediate result to an output.\n\n## Train and convert a model\n\nWe download one model from the :epkg:`ONNX Zoo` but the model\ncould be trained and produced by another converter library.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import pprint\nimport numpy\nfrom onnx import load\nfrom onnxruntime import InferenceSession\nfrom sklearn.datasets import load_iris\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom skl2onnx import to_onnx\nfrom skl2onnx.helpers.onnx_helper import (\n    add_output_initializer, select_model_inputs_outputs)\n\n\ndata = load_iris()\nX, y = data.data.astype(numpy.float32), data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y)\nmodel = LogisticRegression(penalty='elasticnet', C=2.,\n                           solver='saga', l1_ratio=0.5)\nmodel.fit(X_train, y_train)\n\nonx = to_onnx(model, X_train[:1], target_opset=12,\n              options={'zipmap': False})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Add training parameter\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "new_onx = add_output_initializer(\n    onx,\n    ['C', 'l1_ratio'],\n    [numpy.array([model.C]), numpy.array([model.l1_ratio])])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Inference\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "sess = InferenceSession(new_onx.SerializeToString())\nprint(\"output names:\", [o.name for o in sess.get_outputs()])\nres = sess.run(None, {'X': X_test[:2]})\nprint(\"outputs\")\npprint.pprint(res)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The major draw back of this solution is increase the prediction\ntime as onnxruntime copies the constants for every prediction.\nIt is possible either to store those constant in a separate ONNX graph\nor to removes them.\n\n## Select outputs\n\nNext function removes unneeded outputs from a model,\nnot only the constants. Next model only keeps the probabilities.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "simple_onx = select_model_inputs_outputs(new_onx, ['probabilities'])\n\nsess = InferenceSession(simple_onx.SerializeToString())\nprint(\"output names:\", [o.name for o in sess.get_outputs()])\nres = sess.run(None, {'X': X_test[:2]})\nprint(\"outputs\")\npprint.pprint(res)\n\n# Function *select_model_inputs_outputs* add also promote an intermediate\n# result to an output.\n#"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This example only uses ONNX graph in memory and never saves or loads a\nmodel. This can be done by using the following snippets of code.\n\n## Save a model\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "with open(\"simplified_model.onnx\", \"wb\") as f:\n    f.write(simple_onx.SerializeToString())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load a model\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "model = load(\"simplified_model.onnx\", \"wb\")\n\nsess = InferenceSession(model.SerializeToString())\nprint(\"output names:\", [o.name for o in sess.get_outputs()])\nres = sess.run(None, {'X': X_test[:2]})\nprint(\"outputs\")\npprint.pprint(res)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Store arrays in one onnx graph\n\nOnce a model is converted it can be useful to store an\narray as a constant in the graph an retrieve it through\nan output. This allows the user to store training parameters\nor other informations like a vocabulary.\nLast sections shows how to remove an output or to promote\nan intermediate result to an output.\n\n## Train and convert a model\n\nWe download one model from the :epkg:`ONNX Zoo` but the model\ncould be trained and produced by another converter library.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pprint\nimport numpy\nfrom onnx import load\nfrom onnxruntime import InferenceSession\nfrom sklearn.datasets import load_iris\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom skl2onnx import to_onnx\nfrom skl2onnx.helpers.onnx_helper import (\n    add_output_initializer, select_model_inputs_outputs)\n\n\ndata = load_iris()\nX, y = data.data.astype(numpy.float32), data.target\nX_train, X_test, y_train, y_test = train_test_split(X, y)\nmodel = LogisticRegression(penalty='elasticnet', C=2.,\n                           solver='saga', l1_ratio=0.5)\nmodel.fit(X_train, y_train)\n\nonx = to_onnx(model, X_train[:1], target_opset=12,\n              options={'zipmap': False})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Add training parameter\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "new_onx = add_output_initializer(\n    onx,\n    ['C', 'l1_ratio'],\n    [numpy.array([model.C]), numpy.array([model.l1_ratio])])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Inference\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sess = InferenceSession(new_onx.SerializeToString())\nprint(\"output names:\", [o.name for o in sess.get_outputs()])\nres = sess.run(None, {'X': X_test[:2]})\nprint(\"outputs\")\npprint.pprint(res)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The major draw back of this solution is increase the prediction\ntime as onnxruntime copies the constants for every prediction.\nIt is possible either to store those constant in a separate ONNX graph\nor to removes them.\n\n## Select outputs\n\nNext function removes unneeded outputs from a model,\nnot only the constants. Next model only keeps the probabilities.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "simple_onx = select_model_inputs_outputs(new_onx, ['probabilities'])\n\nsess = InferenceSession(simple_onx.SerializeToString())\nprint(\"output names:\", [o.name for o in sess.get_outputs()])\nres = sess.run(None, {'X': X_test[:2]})\nprint(\"outputs\")\npprint.pprint(res)\n\n# Function *select_model_inputs_outputs* add also promote an intermediate\n# result to an output.\n#"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This example only uses ONNX graph in memory and never saves or loads a\nmodel. This can be done by using the following snippets of code.\n\n## Save a model\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "with open(\"simplified_model.onnx\", \"wb\") as f:\n    f.write(simple_onx.SerializeToString())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load a model\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model = load(\"simplified_model.onnx\", \"wb\")\n\nsess = InferenceSession(model.SerializeToString())\nprint(\"output names:\", [o.name for o in sess.get_outputs()])\nres = sess.run(None, {'X': X_test[:2]})\nprint(\"outputs\")\npprint.pprint(res)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }