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<title>Load and predict with ONNX Runtime and a very simple model &#8212; ONNX Runtime 1.14.0 documentation</title>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-load-and-predict-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
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<section class="sphx-glr-example-title" id="load-and-predict-with-onnx-runtime-and-a-very-simple-model">
<span id="l-example-simple-usage"></span><span id="sphx-glr-auto-examples-plot-load-and-predict-py"></span><h1>Load and predict with ONNX Runtime and a very simple model<a class="headerlink" href="#load-and-predict-with-onnx-runtime-and-a-very-simple-model" title="Permalink to this heading"></a></h1>
<p>This example demonstrates how to load a model and compute
the output for an input vector. It also shows how to
retrieve the definition of its inputs and outputs.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span>
<span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</span>
<span class="kn">from</span> <span class="nn">onnxruntime.datasets</span> <span class="kn">import</span> <span class="n">get_example</span>
</pre></div>
</div>
<p>Let’s load a very simple model.
The model is available on github <a class="reference external" href="https://github.com/onnx/onnx/blob/main/onnx/backend/test/data/node/test_sigmoid">onnx…test_sigmoid</a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">example1</span> <span class="o">=</span> <span class="n">get_example</span><span class="p">(</span><span class="s2">&quot;sigmoid.onnx&quot;</span><span class="p">)</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="n">example1</span><span class="p">,</span> <span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</span><span class="p">())</span>
</pre></div>
</div>
<p>Let’s see the input name and shape.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">input_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;input name&quot;</span><span class="p">,</span> <span class="n">input_name</span><span class="p">)</span>
<span class="n">input_shape</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;input shape&quot;</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">)</span>
<span class="n">input_type</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">type</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;input type&quot;</span><span class="p">,</span> <span class="n">input_type</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>input name x
input shape [3, 4, 5]
input type tensor(float)
</pre></div>
</div>
<p>Let’s see the output name and shape.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">output_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output name&quot;</span><span class="p">,</span> <span class="n">output_name</span><span class="p">)</span>
<span class="n">output_shape</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output shape&quot;</span><span class="p">,</span> <span class="n">output_shape</span><span class="p">)</span>
<span class="n">output_type</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">type</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output type&quot;</span><span class="p">,</span> <span class="n">output_type</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>output name y
output shape [3, 4, 5]
output type tensor(float)
</pre></div>
</div>
<p>Let’s compute its outputs (or predictions if it is a machine learned model).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy.random</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[array([[[0.7250963 , 0.6541972 , 0.5819463 , 0.5665157 , 0.5459631 ],
[0.6202056 , 0.7197919 , 0.5753145 , 0.63737607, 0.61815476],
[0.67895865, 0.71502703, 0.7221614 , 0.7055453 , 0.7035813 ],
[0.7045368 , 0.69941163, 0.59531343, 0.52075315, 0.6484108 ]],
[[0.6701564 , 0.57214713, 0.6167228 , 0.7285589 , 0.5704626 ],
[0.6747384 , 0.52562577, 0.67170435, 0.53544027, 0.65662634],
[0.67381144, 0.5500633 , 0.6245512 , 0.5259149 , 0.559829 ],
[0.7012721 , 0.5680853 , 0.54782236, 0.70639503, 0.50378627]],
[[0.52244467, 0.6723233 , 0.546823 , 0.62568605, 0.5762193 ],
[0.52736926, 0.6422948 , 0.70951426, 0.6668659 , 0.672336 ],
[0.6420765 , 0.6381056 , 0.6666118 , 0.5383738 , 0.61810255],
[0.7150735 , 0.57898074, 0.59422445, 0.64117074, 0.7272807 ]]],
dtype=float32)]
</pre></div>
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