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<div class="section" id="module-torch">
<span id="torch"></span><h1>torch<a class="headerlink" href="#module-torch" title="Permalink to this headline">¶</a></h1>
<p>The torch package contains data structures for multi-dimensional
tensors and mathematical operations over these are defined.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.</p>
<p>It has a CUDA counterpart, that enables you to run your tensor computations
on an NVIDIA GPU with compute capability >= 3.0.</p>
<div class="section" id="tensors">
<h2>Tensors<a class="headerlink" href="#tensors" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torch.is_tensor">
<code class="descclassname">torch.</code><code class="descname">is_tensor</code><span class="sig-paren">(</span><em>obj</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#is_tensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.is_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns True if <cite>obj</cite> is a PyTorch tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>obj</strong> (<em>Object</em>) – Object to test</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.is_storage">
<code class="descclassname">torch.</code><code class="descname">is_storage</code><span class="sig-paren">(</span><em>obj</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#is_storage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.is_storage" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns True if <cite>obj</cite> is a PyTorch storage object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>obj</strong> (<em>Object</em>) – Object to test</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.set_default_dtype">
<code class="descclassname">torch.</code><code class="descname">set_default_dtype</code><span class="sig-paren">(</span><em>d</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#set_default_dtype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.set_default_dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the default floating point dtype to <code class="xref py py-attr docutils literal notranslate"><span class="pre">d</span></code>. This type will be
used as default floating point type for type inference in
<a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a>.</p>
<p>The default floating point dtype is initially <code class="docutils literal notranslate"><span class="pre">torch.float32</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>d</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>) – the floating point dtype to make the default</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># initial default for floating point is torch.float32</span>
<span class="go">torch.float32</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_dtype</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># a new floating point tensor</span>
<span class="go">torch.float64</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.get_default_dtype">
<code class="descclassname">torch.</code><code class="descname">get_default_dtype</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → torch.dtype<a class="headerlink" href="#torch.get_default_dtype" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the current default floating point <a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="c1"># initial default for floating point is torch.float32</span>
<span class="go">torch.float32</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_dtype</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="c1"># default is now changed to torch.float64</span>
<span class="go">torch.float64</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_tensor_type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">)</span> <span class="c1"># setting tensor type also affects this</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> <span class="c1"># changed to torch.float32, the dtype for torch.FloatTensor</span>
<span class="go">torch.float32</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.set_default_tensor_type">
<code class="descclassname">torch.</code><code class="descname">set_default_tensor_type</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#set_default_tensor_type"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.set_default_tensor_type" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the default <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> type to floating point tensor type
<a class="reference internal" href="#torch.t" title="torch.t"><code class="xref py py-attr docutils literal notranslate"><span class="pre">t</span></code></a>. This type will also be used as default floating point type for
type inference in <a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a>.</p>
<p>The default floating point tensor type is initially <code class="docutils literal notranslate"><span class="pre">torch.FloatTensor</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>t</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.7)"><em>type</em></a><em> or </em><em>string</em>) – the floating point tensor type or its name</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># initial default for floating point is torch.float32</span>
<span class="go">torch.float32</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_default_tensor_type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">DoubleTensor</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">dtype</span> <span class="c1"># a new floating point tensor</span>
<span class="go">torch.float64</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.numel">
<code class="descclassname">torch.</code><code class="descname">numel</code><span class="sig-paren">(</span><em>input</em><span class="sig-paren">)</span> → int<a class="headerlink" href="#torch.numel" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the total number of elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>input</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor</td>
</tr>
</tbody>
</table>
<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">torch</span><span class="o">.</span><span class="n">randn</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="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">numel</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">120</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">numel</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">16</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.set_printoptions">
<code class="descclassname">torch.</code><code class="descname">set_printoptions</code><span class="sig-paren">(</span><em>precision=None</em>, <em>threshold=None</em>, <em>edgeitems=None</em>, <em>linewidth=None</em>, <em>profile=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor_str.html#set_printoptions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.set_printoptions" title="Permalink to this definition">¶</a></dt>
<dd><p>Set options for printing. Items shamelessly taken from NumPy</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>precision</strong> – Number of digits of precision for floating point output
(default = 4).</li>
<li><strong>threshold</strong> – Total number of array elements which trigger summarization
rather than full <cite>repr</cite> (default = 1000).</li>
<li><strong>edgeitems</strong> – Number of array items in summary at beginning and end of
each dimension (default = 3).</li>
<li><strong>linewidth</strong> – The number of characters per line for the purpose of
inserting line breaks (default = 80). Thresholded matrices will
ignore this parameter.</li>
<li><strong>profile</strong> – Sane defaults for pretty printing. Can override with any of
the above options. (any one of <cite>default</cite>, <cite>short</cite>, <cite>full</cite>)</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="torch.set_flush_denormal">
<code class="descclassname">torch.</code><code class="descname">set_flush_denormal</code><span class="sig-paren">(</span><em>mode</em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#torch.set_flush_denormal" title="Permalink to this definition">¶</a></dt>
<dd><p>Disables denormal floating numbers on CPU.</p>
<p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if your system supports flushing denormal numbers and it
successfully configures flush denormal mode. <a class="reference internal" href="#torch.set_flush_denormal" title="torch.set_flush_denormal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_flush_denormal()</span></code></a>
is only supported on x86 architectures supporting SSE3.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – Controls whether to enable flush denormal mode or not</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_flush_denormal</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1e-323</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">tensor([ 0.], dtype=torch.float64)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_flush_denormal</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1e-323</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">tensor(9.88131e-324 *</span>
<span class="go"> [ 1.0000], dtype=torch.float64)</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="creation-ops">
<span id="tensor-creation-ops"></span><h3>Creation Ops<a class="headerlink" href="#creation-ops" title="Permalink to this headline">¶</a></h3>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Random sampling creation ops are listed under <a class="reference internal" href="#random-sampling"><span class="std std-ref">Random sampling</span></a> and
include:
<a class="reference internal" href="#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand()</span></code></a>
<a class="reference internal" href="#torch.rand_like" title="torch.rand_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand_like()</span></code></a>
<a class="reference internal" href="#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn()</span></code></a>
<a class="reference internal" href="#torch.randn_like" title="torch.randn_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn_like()</span></code></a>
<a class="reference internal" href="#torch.randint" title="torch.randint"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randint()</span></code></a>
<a class="reference internal" href="#torch.randint_like" title="torch.randint_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randint_like()</span></code></a>
<a class="reference internal" href="#torch.randperm" title="torch.randperm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randperm()</span></code></a>
You may also use <a class="reference internal" href="#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a> with the <a class="reference internal" href="#inplace-random-sampling"><span class="std std-ref">In-place random sampling</span></a>
methods to create <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s with values sampled from a broader
range of distributions.</p>
</div>
<dl class="function">
<dt id="torch.tensor">
<code class="descclassname">torch.</code><code class="descname">tensor</code><span class="sig-paren">(</span><em>data</em>, <em>dtype=None</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a tensor with <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>. If you have a Tensor
<code class="docutils literal notranslate"><span class="pre">data</span></code> and want to avoid a copy, use <a class="reference internal" href="tensors.html#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.requires_grad_()</span></code></a>
or <a class="reference internal" href="autograd.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.detach()</span></code></a>.
If you have a NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> and want to avoid a copy, use
<a class="reference internal" href="#torch.from_numpy" title="torch.from_numpy"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.from_numpy()</span></code></a>.</p>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">When data is a tensor <cite>x</cite>, <a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> reads out ‘the data’ from whatever it is passed,
and constructs a leaf variable. Therefore <code class="docutils literal notranslate"><span class="pre">torch.tensor(x)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.clone().detach()</span></code>
and <code class="docutils literal notranslate"><span class="pre">torch.tensor(x,</span> <span class="pre">requires_grad=True)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.clone().detach().requires_grad_(True)</span></code>.
The equivalents using <code class="docutils literal notranslate"><span class="pre">clone()</span></code> and <code class="docutils literal notranslate"><span class="pre">detach()</span></code> are recommended.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (<em>array_like</em>) – Initial data for the tensor. Can be a list, tuple,
NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, scalar, and other types.</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, infers data type from <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.2</span><span class="p">,</span> <span class="mf">3.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.9</span><span class="p">,</span> <span class="mf">5.2</span><span class="p">]])</span>
<span class="go">tensor([[ 0.1000, 1.2000],</span>
<span class="go"> [ 2.2000, 3.1000],</span>
<span class="go"> [ 4.9000, 5.2000]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="c1"># Type inference on data</span>
<span class="go">tensor([ 0, 1])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.11111</span><span class="p">,</span> <span class="mf">0.222222</span><span class="p">,</span> <span class="mf">0.3333333</span><span class="p">]],</span>
<span class="go"> dtype=torch.float64,</span>
<span class="go"> device=torch.device('cuda:0')) # creates a torch.cuda.DoubleTensor</span>
<span class="go">tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0')</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.14159</span><span class="p">)</span> <span class="c1"># Create a scalar (zero-dimensional tensor)</span>
<span class="go">tensor(3.1416)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([])</span> <span class="c1"># Create an empty tensor (of size (0,))</span>
<span class="go">tensor([])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.sparse_coo_tensor">
<code class="descclassname">torch.</code><code class="descname">sparse_coo_tensor</code><span class="sig-paren">(</span><em>indices</em>, <em>values</em>, <em>size=None</em>, <em>dtype=None</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.sparse_coo_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices</span></code>
with the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code>. A sparse tensor can be <cite>uncoalesced</cite>, in that case, there are duplicate
coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
<a class="reference external" href="https://pytorch.org/docs/stable/sparse.html">torch.sparse</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>indices</strong> (<em>array_like</em>) – Initial data for the tensor. Can be a list, tuple,
NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, scalar, and other types. Will be cast to a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.LongTensor</span></code>
internally. The indices are the coordinates of the non-zero values in the matrix, and thus
should be two-dimensional where the first dimension is the number of tensor dimensions and
the second dimension is the number of non-zero values.</li>
<li><strong>values</strong> (<em>array_like</em>) – Initial values for the tensor. Can be a list, tuple,
NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, scalar, and other types.</li>
<li><strong>size</strong> (list, tuple, or <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Size</span></code>, optional) – Size of the sparse tensor. If not
provided the size will be inferred as the minimum size big enough to hold all non-zero
elements.</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if None, infers data type from <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">i</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="go"> [2, 0, 2]])</span>
<span class="gp">>>> </span><span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</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">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
<span class="go">tensor(indices=tensor([[0, 1, 1],</span>
<span class="go"> [2, 0, 2]]),</span>
<span class="go"> values=tensor([3., 4., 5.]),</span>
<span class="go"> size=(2, 4), nnz=3, layout=torch.sparse_coo)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="c1"># Shape inference</span>
<span class="go">tensor(indices=tensor([[0, 1, 1],</span>
<span class="go"> [2, 0, 2]]),</span>
<span class="go"> values=tensor([3., 4., 5.]),</span>
<span class="go"> size=(2, 3), nnz=3, layout=torch.sparse_coo)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
<span class="go"> dtype=torch.float64,</span>
<span class="go"> device=torch.device('cuda:0'))</span>
<span class="go">tensor(indices=tensor([[0, 1, 1],</span>
<span class="go"> [2, 0, 2]]),</span>
<span class="go"> values=tensor([3., 4., 5.]),</span>
<span class="go"> device='cuda:0', size=(2, 4), nnz=3, dtype=torch.float64,</span>
<span class="go"> layout=torch.sparse_coo)</span>
<span class="go"># Create an empty sparse tensor with the following invariants:</span>
<span class="go"># 1. sparse_dim + dense_dim = len(SparseTensor.shape)</span>
<span class="go"># 2. SparseTensor._indices().shape = (sparse_dim, nnz)</span>
<span class="go"># 3. SparseTensor._values().shape = (nnz, SparseTensor.shape[sparse_dim:])</span>
<span class="go">#</span>
<span class="go"># For instance, to create an empty sparse tensor with nnz = 0, dense_dim = 0 and</span>
<span class="go"># sparse_dim = 1 (hence indices is a 2D tensor of shape = (1, 0))</span>
<span class="gp">>>> </span><span class="n">S</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="p">[],</span> <span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="go">tensor(indices=tensor([], size=(1, 0)),</span>
<span class="go"> values=tensor([], size=(0,)),</span>
<span class="go"> size=(1,), nnz=0, layout=torch.sparse_coo)</span>
<span class="go"># and to create an empty sparse tensor with nnz = 0, dense_dim = 1 and</span>
<span class="go"># sparse_dim = 1</span>
<span class="gp">>>> </span><span class="n">S</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</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="go">tensor(indices=tensor([], size=(1, 0)),</span>
<span class="go"> values=tensor([], size=(0, 2)),</span>
<span class="go"> size=(1, 2), nnz=0, layout=torch.sparse_coo)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.as_tensor">
<code class="descclassname">torch.</code><code class="descname">as_tensor</code><span class="sig-paren">(</span><em>data</em>, <em>dtype=None</em>, <em>device=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.as_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert the data into a <cite>torch.Tensor</cite>. If the data is already a <cite>Tensor</cite> with the same <cite>dtype</cite> and <cite>device</cite>,
no copy will be performed, otherwise a new <cite>Tensor</cite> will be returned with computational graph retained if data
<cite>Tensor</cite> has <code class="docutils literal notranslate"><span class="pre">requires_grad=True</span></code>. Similarly, if the data is an <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> of the corresponding <cite>dtype</cite> and
the <cite>device</cite> is the cpu, no copy will be performed.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (<em>array_like</em>) – Initial data for the tensor. Can be a list, tuple,
NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, scalar, and other types.</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, infers data type from <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<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">as_tensor</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>
<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">as_tensor</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</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>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.from_numpy">
<code class="descclassname">torch.</code><code class="descname">from_numpy</code><span class="sig-paren">(</span><em>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://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.15)"><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>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>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.zeros">
<code class="descclassname">torch.</code><code class="descname">zeros</code><span class="sig-paren">(</span><em>*sizes</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.zeros" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with the scalar value <cite>0</cite>, with the shape defined
by the variable argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">sizes</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>sizes</strong> (<em>int...</em>) – a sequence of integers defining the shape of the output tensor.
Can be a variable number of arguments or a collection like a list or tuple.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>).</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</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="go">tensor([[ 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0.]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([ 0., 0., 0., 0., 0.])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.zeros_like">
<code class="descclassname">torch.</code><code class="descname">zeros_like</code><span class="sig-paren">(</span><em>input</em>, <em>dtype=None</em>, <em>layout=None</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.zeros_like" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with the scalar value <cite>0</cite>, with the same size as
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>. <code class="docutils literal notranslate"><span class="pre">torch.zeros_like(input)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.zeros(input.size(),</span> <span class="pre">dtype=input.dtype,</span> <span class="pre">layout=input.layout,</span> <span class="pre">device=input.device)</span></code>.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">As of 0.4, this function does not support an <code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> keyword. As an alternative,
the old <code class="docutils literal notranslate"><span class="pre">torch.zeros_like(input,</span> <span class="pre">out=output)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.zeros(input.size(),</span> <span class="pre">out=output)</span></code>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the size of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> will determine size of the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned Tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the dtype of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the layout of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the device of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</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">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([[ 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0.]])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.ones">
<code class="descclassname">torch.</code><code class="descname">ones</code><span class="sig-paren">(</span><em>*sizes</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.ones" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with the scalar value <cite>1</cite>, with the shape defined
by the variable argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">sizes</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>sizes</strong> (<em>int...</em>) – a sequence of integers defining the shape of the output tensor.
Can be a variable number of arguments or a collection like a list or tuple.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>).</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">ones</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="go">tensor([[ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([ 1., 1., 1., 1., 1.])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.ones_like">
<code class="descclassname">torch.</code><code class="descname">ones_like</code><span class="sig-paren">(</span><em>input</em>, <em>dtype=None</em>, <em>layout=None</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.ones_like" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with the scalar value <cite>1</cite>, with the same size as
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>. <code class="docutils literal notranslate"><span class="pre">torch.ones_like(input)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.ones(input.size(),</span> <span class="pre">dtype=input.dtype,</span> <span class="pre">layout=input.layout,</span> <span class="pre">device=input.device)</span></code>.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">As of 0.4, this function does not support an <code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> keyword. As an alternative,
the old <code class="docutils literal notranslate"><span class="pre">torch.ones_like(input,</span> <span class="pre">out=output)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.ones(input.size(),</span> <span class="pre">out=output)</span></code>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>input</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the size of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> will determine size of the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned Tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the dtype of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the layout of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the device of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</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">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([[ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.]])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.arange">
<code class="descclassname">torch.</code><code class="descname">arange</code><span class="sig-paren">(</span><em>start=0</em>, <em>end</em>, <em>step=1</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.arange" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a 1-D tensor of size <span class="math">\(\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor\)</span>
with values from the interval <code class="docutils literal notranslate"><span class="pre">[start,</span> <span class="pre">end)</span></code> taken with common difference
<code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code> beginning from <cite>start</cite>.</p>
<p>Note that non-integer <code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code> is subject to floating point rounding errors when
comparing against <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>; to avoid inconsistency, we advise adding a small epsilon to <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>
in such cases.</p>
<div class="math">
\[\text{out}_{{i+1}} = \text{out}_{i} + \text{step}
\]</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>start</strong> (<em>Number</em>) – the starting value for the set of points. Default: <code class="docutils literal notranslate"><span class="pre">0</span></code>.</li>
<li><strong>end</strong> (<em>Number</em>) – the ending value for the set of points</li>
<li><strong>step</strong> (<em>Number</em>) – the gap between each pair of adjacent points. Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). If <cite>dtype</cite> is not given, infer the data type from the other input
arguments. If any of <cite>start</cite>, <cite>end</cite>, or <cite>stop</cite> are floating-point, the
<cite>dtype</cite> is inferred to be the default dtype, see
<a class="reference internal" href="#torch.get_default_dtype" title="torch.get_default_dtype"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_default_dtype()</span></code></a>. Otherwise, the <cite>dtype</cite> is inferred to
be <cite>torch.int64</cite>.</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([ 0, 1, 2, 3, 4])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go">tensor([ 1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go">tensor([ 1.0000, 1.5000, 2.0000])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.range">
<code class="descclassname">torch.</code><code class="descname">range</code><span class="sig-paren">(</span><em>start=0</em>, <em>end</em>, <em>step=1</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.range" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a 1-D tensor of size <span class="math">\(\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1\)</span>
with values from <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code> to <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code> with step <code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code>. Step is
the gap between two values in the tensor.</p>
<div class="math">
\[\text{out}_{i+1} = \text{out}_i + \text{step}.
\]</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">This function is deprecated in favor of <a class="reference internal" href="#torch.arange" title="torch.arange"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arange()</span></code></a>.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the starting value for the set of points. Default: <code class="docutils literal notranslate"><span class="pre">0</span></code>.</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the ending value for the set of points</li>
<li><strong>step</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the gap between each pair of adjacent points. Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>).</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go">tensor([ 1., 2., 3., 4.])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go">tensor([ 1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000, 4.0000])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.linspace">
<code class="descclassname">torch.</code><code class="descname">linspace</code><span class="sig-paren">(</span><em>start</em>, <em>end</em>, <em>steps=100</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.linspace" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a one-dimensional tensor of <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code>
equally spaced points between <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>.</p>
<p>The output tensor is 1-D of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the starting value for the set of points</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the ending value for the set of points</li>
<li><strong>steps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – number of points to sample between <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code>
and <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>. Default: <code class="docutils literal notranslate"><span class="pre">100</span></code>.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>).</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([-10., -5., 0., 5., 10.])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">start</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([-10., -5., 0., 5., 10.])</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.logspace">
<code class="descclassname">torch.</code><code class="descname">logspace</code><span class="sig-paren">(</span><em>start</em>, <em>end</em>, <em>steps=100</em>, <em>out=None</em>, <em>dtype=None</em>, <em>layout=torch.strided</em>, <em>device=None</em>, <em>requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.logspace" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a one-dimensional tensor of <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code> points
logarithmically spaced between <span class="math">\(10^{\text{start}}\)</span> and <span class="math">\(10^{\text{end}}\)</span>.</p>
<p>The output tensor is 1-D of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the starting value for the set of points</li>
<li><strong>end</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a>) – the ending value for the set of points</li>
<li><strong>steps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – number of points to sample between <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code>
and <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>. Default: <code class="docutils literal notranslate"><span class="pre">100</span></code>.</li>
<li><strong>out</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor</li>
<li><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>).</li>
<li><strong>layout</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</li>
<li><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">start</span><span class="o">=-</span><span class="mi">10</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([ 1.0000e-10, 1.0000e-05, 1.0000e+00, 1.0000e+05, 1.0000e+10])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>