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<div class="section" id="torch-from-numpy">
<h1>torch.from_numpy<a class="headerlink" href="#torch-from-numpy" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.from_numpy">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">from_numpy</code><span class="sig-paren">(</span><em class="sig-param">ndarray</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.from_numpy" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> from a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.19)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p>
<p>The returned tensor and <code class="xref py py-attr docutils literal notranslate"><span class="pre">ndarray</span></code> share the same memory. Modifications to
the tensor will be reflected in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">ndarray</span></code> and vice versa. The returned
tensor is not resizable.</p>
<p>It currently accepts <code class="xref py py-attr docutils literal notranslate"><span class="pre">ndarray</span></code> with dtypes of <code class="docutils literal notranslate"><span class="pre">numpy.float64</span></code>,
<code class="docutils literal notranslate"><span class="pre">numpy.float32</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.float16</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.complex64</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.complex128</span></code>,
<code class="docutils literal notranslate"><span class="pre">numpy.int64</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.int32</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.int16</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.int8</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.uint8</span></code>,
and <code class="docutils literal notranslate"><span class="pre">numpy.bool</span></code>.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">t</span>
<span class="go">tensor([ 1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">array([-1, 2, 3])</span>
</pre></div>
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