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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<div class="document">
<div class="page-content" role="main">
<div class="section" id="gluon-loss">
<h1>gluon.loss<a class="headerlink" href="#gluon-loss" title="Permalink to this headline"></a></h1>
<p>Gluon provides pre-defined loss functions in the <a class="reference internal" href="#module-mxnet.gluon.loss" title="mxnet.gluon.loss"><code class="xref py py-mod docutils literal notranslate"><span class="pre">mxnet.gluon.loss</span></code></a>
module.</p>
<span class="target" id="module-mxnet.gluon.loss"></span><p>losses for training neural networks</p>
<p><strong>Classes</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Loss</span></code></a>(weight, batch_axis, **kwargs)</p></td>
<td><p>Base class for loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.L2Loss" title="mxnet.gluon.loss.L2Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">L2Loss</span></code></a>([weight, batch_axis])</p></td>
<td><p>Calculates the mean squared error between <cite>label</cite> and <cite>pred</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.L1Loss" title="mxnet.gluon.loss.L1Loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">L1Loss</span></code></a>([weight, batch_axis])</p></td>
<td><p>Calculates the mean absolute error between <cite>label</cite> and <cite>pred</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss" title="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SigmoidBinaryCrossEntropyLoss</span></code></a>([…])</p></td>
<td><p>The cross-entropy loss for binary classification.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SigmoidBCELoss" title="mxnet.gluon.loss.SigmoidBCELoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SigmoidBCELoss</span></code></a></p></td>
<td><p>The cross-entropy loss for binary classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SoftmaxCrossEntropyLoss</span></code></a>([axis, …])</p></td>
<td><p>Computes the softmax cross entropy loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCELoss" title="mxnet.gluon.loss.SoftmaxCELoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SoftmaxCELoss</span></code></a></p></td>
<td><p>Computes the softmax cross entropy loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.KLDivLoss" title="mxnet.gluon.loss.KLDivLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KLDivLoss</span></code></a>([from_logits, axis, weight, …])</p></td>
<td><p>The Kullback-Leibler divergence loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.CTCLoss" title="mxnet.gluon.loss.CTCLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CTCLoss</span></code></a>([layout, label_layout, weight])</p></td>
<td><p>Connectionist Temporal Classification Loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.HuberLoss" title="mxnet.gluon.loss.HuberLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HuberLoss</span></code></a>([rho, weight, batch_axis])</p></td>
<td><p>Calculates smoothed L1 loss that is equal to L1 loss if absolute error exceeds rho but is equal to L2 loss otherwise.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.HingeLoss" title="mxnet.gluon.loss.HingeLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HingeLoss</span></code></a>([margin, weight, batch_axis])</p></td>
<td><p>Calculates the hinge loss function often used in SVMs:</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SquaredHingeLoss" title="mxnet.gluon.loss.SquaredHingeLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SquaredHingeLoss</span></code></a>([margin, weight, batch_axis])</p></td>
<td><p>Calculates the soft-margin loss function used in SVMs:</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.LogisticLoss" title="mxnet.gluon.loss.LogisticLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogisticLoss</span></code></a>([weight, batch_axis, label_format])</p></td>
<td><p>Calculates the logistic loss (for binary losses only):</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.TripletLoss" title="mxnet.gluon.loss.TripletLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TripletLoss</span></code></a>([margin, weight, batch_axis])</p></td>
<td><p>Calculates triplet loss given three input tensors and a positive margin.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.PoissonNLLLoss" title="mxnet.gluon.loss.PoissonNLLLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PoissonNLLLoss</span></code></a>([weight, from_logits, …])</p></td>
<td><p>For a target (Random Variable) in a Poisson distribution, the function calculates the Negative Log likelihood loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#mxnet.gluon.loss.CosineEmbeddingLoss" title="mxnet.gluon.loss.CosineEmbeddingLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CosineEmbeddingLoss</span></code></a>([weight, batch_axis, margin])</p></td>
<td><p>For a target label 1 or -1, vectors input1 and input2, the function computes the cosine distance between the vectors.</p></td>
</tr>
</tbody>
</table>
<dl class="class">
<dt id="mxnet.gluon.loss.Loss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">Loss</code><span class="sig-paren">(</span><em class="sig-param">weight</em>, <em class="sig-param">batch_axis</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.Loss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.block.HybridBlock</span></code></p>
<p>Base class for loss.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.Loss.hybrid_forward" title="mxnet.gluon.loss.Loss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, x, *args, **kwargs)</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="mxnet.gluon.loss.Loss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">x</em>, <em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#Loss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.Loss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.L2Loss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">L2Loss</code><span class="sig-paren">(</span><em class="sig-param">weight=1.0</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L2Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L2Loss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates the mean squared error between <cite>label</cite> and <cite>pred</cite>.</p>
<div class="math notranslate nohighlight">
\[L = \frac{1}{2} \sum_i \vert {label}_i - {pred}_i \vert^2.\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.L2Loss.hybrid_forward" title="mxnet.gluon.loss.L2Loss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same
number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li>
<li><p><strong>label</strong>: target tensor with the same size as pred.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.L2Loss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L2Loss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L2Loss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.L1Loss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">L1Loss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L1Loss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L1Loss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates the mean absolute error between <cite>label</cite> and <cite>pred</cite>.</p>
<div class="math notranslate nohighlight">
\[L = \sum_i \vert {label}_i - {pred}_i \vert.\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.L1Loss.hybrid_forward" title="mxnet.gluon.loss.L1Loss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same
number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li>
<li><p><strong>label</strong>: target tensor with the same size as pred.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.L1Loss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#L1Loss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.L1Loss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SigmoidBinaryCrossEntropyLoss</code><span class="sig-paren">(</span><em class="sig-param">from_sigmoid=False</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SigmoidBinaryCrossEntropyLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>The cross-entropy loss for binary classification. (alias: SigmoidBCELoss)</p>
<p>BCE loss is useful when training logistic regression. If <cite>from_sigmoid</cite>
is False (default), this loss computes:</p>
<div class="math notranslate nohighlight">
\[ \begin{align}\begin{aligned}prob = \frac{1}{1 + \exp(-{pred})}\\L = - \sum_i {label}_i * \log({prob}_i) * pos\_weight +
(1 - {label}_i) * \log(1 - {prob}_i)\end{aligned}\end{align} \]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward" title="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, …])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p>If <cite>from_sigmoid</cite> is True, this loss computes:</p>
<div class="math notranslate nohighlight">
\[L = - \sum_i {label}_i * \log({pred}_i) * pos\_weight +
(1 - {label}_i) * \log(1 - {pred}_i)\]</div>
<p>A tensor <cite>pos_weight &gt; 1</cite> decreases the false negative count, hence increasing
the recall.
Conversely setting <cite>pos_weight &lt; 1</cite> decreases the false positive count and
increases the precision.</p>
<p><cite>pred</cite> and <cite>label</cite> can have arbitrary shape as long as they have the same
number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>from_sigmoid</strong> (bool, default is <cite>False</cite>) – Whether the input is from the output of sigmoid. Set this to false will make
the loss calculate sigmoid and BCE together, which is more numerically
stable through log-sum-exp trick.</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li>
<li><p><strong>label</strong>: target tensor with values in range <cite>[0, 1]</cite>. Must have the
same size as <cite>pred</cite>.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
<li><p><strong>pos_weight</strong>: a weighting tensor of positive examples. Must be a vector with length
equal to the number of classes.For example, if pred has shape (64, 10),
pos_weight should have shape (1, 10).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em>, <em class="sig-param">pos_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SigmoidBinaryCrossEntropyLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="attribute">
<dt id="mxnet.gluon.loss.SigmoidBCELoss">
<code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SigmoidBCELoss</code><a class="headerlink" href="#mxnet.gluon.loss.SigmoidBCELoss" title="Permalink to this definition"></a></dt>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code>(F, pred, label[, …])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><strong>Methods</strong></p>
<dd><p>alias of <a class="reference internal" href="#mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss" title="mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.SigmoidBinaryCrossEntropyLoss</span></code></a></p>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.SoftmaxCrossEntropyLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SoftmaxCrossEntropyLoss</code><span class="sig-paren">(</span><em class="sig-param">axis=-1</em>, <em class="sig-param">sparse_label=True</em>, <em class="sig-param">from_logits=False</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SoftmaxCrossEntropyLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Computes the softmax cross entropy loss. (alias: SoftmaxCELoss)</p>
<p>If <cite>sparse_label</cite> is <cite>True</cite> (default), label should contain integer
category indicators:</p>
<div class="math notranslate nohighlight">
\[ \begin{align}\begin{aligned}\DeclareMathOperator{softmax}{softmax}\\p = \softmax({pred})\\L = -\sum_i \log p_{i,{label}_i}\end{aligned}\end{align} \]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>label</cite>’s shape should be <cite>pred</cite>’s shape with the <cite>axis</cite> dimension removed.
i.e. for <cite>pred</cite> with shape (1,2,3,4) and <cite>axis = 2</cite>, <cite>label</cite>’s shape should
be (1,2,4).</p>
<p>If <cite>sparse_label</cite> is <cite>False</cite>, <cite>label</cite> should contain probability distribution
and <cite>label</cite>’s shape should be the same with <cite>pred</cite>:</p>
<div class="math notranslate nohighlight">
\[ \begin{align}\begin{aligned}p = \softmax({pred})\\L = -\sum_i \sum_j {label}_j \log p_{ij}\end{aligned}\end{align} \]</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>default -1</em>) – The axis to sum over when computing softmax and entropy.</p></li>
<li><p><strong>sparse_label</strong> (<em>bool</em><em>, </em><em>default True</em>) – Whether label is an integer array instead of probability distribution.</p></li>
<li><p><strong>from_logits</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether input is a log probability (usually from log_softmax) instead
of unnormalized numbers.</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: the prediction tensor, where the <cite>batch_axis</cite> dimension
ranges over batch size and <cite>axis</cite> dimension ranges over the number
of classes.</p></li>
<li><p><strong>label</strong>: the truth tensor. When <cite>sparse_label</cite> is True, <cite>label</cite>’s
shape should be <cite>pred</cite>’s shape with the <cite>axis</cite> dimension removed.
i.e. for <cite>pred</cite> with shape (1,2,3,4) and <cite>axis = 2</cite>, <cite>label</cite>’s shape
should be (1,2,4) and values should be integers between 0 and 2. If
<cite>sparse_label</cite> is False, <cite>label</cite>’s shape must be the same as <cite>pred</cite>
and values should be floats in the range <cite>[0, 1]</cite>.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as label. For example, if label has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SoftmaxCrossEntropyLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="attribute">
<dt id="mxnet.gluon.loss.SoftmaxCELoss">
<code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SoftmaxCELoss</code><a class="headerlink" href="#mxnet.gluon.loss.SoftmaxCELoss" title="Permalink to this definition"></a></dt>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><strong>Methods</strong></p>
<dd><p>alias of <a class="reference internal" href="#mxnet.gluon.loss.SoftmaxCrossEntropyLoss" title="mxnet.gluon.loss.SoftmaxCrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.SoftmaxCrossEntropyLoss</span></code></a></p>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.KLDivLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">KLDivLoss</code><span class="sig-paren">(</span><em class="sig-param">from_logits=True</em>, <em class="sig-param">axis=-1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#KLDivLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.KLDivLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>The Kullback-Leibler divergence loss.</p>
<p>KL divergence measures the distance between contiguous distributions. It
can be used to minimize information loss when approximating a distribution.
If <cite>from_logits</cite> is True (default), loss is defined as:</p>
<div class="math notranslate nohighlight">
\[L = \sum_i {label}_i * \big[\log({label}_i) - {pred}_i\big]\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.KLDivLoss.hybrid_forward" title="mxnet.gluon.loss.KLDivLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p>If <cite>from_logits</cite> is False, loss is defined as:</p>
<div class="math notranslate nohighlight">
\[ \begin{align}\begin{aligned}\DeclareMathOperator{softmax}{softmax}\\prob = \softmax({pred})\\L = \sum_i {label}_i * \big[\log({label}_i) - \log({prob}_i)\big]\end{aligned}\end{align} \]</div>
<p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same
number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>from_logits</strong> (bool, default is <cite>True</cite>) – Whether the input is log probability (usually from log_softmax) instead
of unnormalized numbers.</p></li>
<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>default -1</em>) – The dimension along with to compute softmax. Only used when <cite>from_logits</cite>
is False.</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape. If <cite>from_logits</cite> is
True, <cite>pred</cite> should be log probabilities. Otherwise, it should be
unnormalized predictions, i.e. from a dense layer.</p></li>
<li><p><strong>label</strong>: truth tensor with values in range <cite>(0, 1)</cite>. Must have
the same size as <cite>pred</cite>.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">References</p>
<p><a class="reference external" href="https://en.wikipedia.org/wiki/Kullback-Leibler_divergence">Kullback-Leibler divergence</a></p>
<dl class="method">
<dt id="mxnet.gluon.loss.KLDivLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#KLDivLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.KLDivLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.CTCLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">CTCLoss</code><span class="sig-paren">(</span><em class="sig-param">layout='NTC'</em>, <em class="sig-param">label_layout='NT'</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CTCLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CTCLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Connectionist Temporal Classification Loss.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>layout</strong> (<em>str</em><em>, </em><em>default 'NTC'</em>) – Layout of prediction tensor. ‘N’, ‘T’, ‘C’ stands for batch size,
sequence length, and alphabet_size respectively.</p></li>
<li><p><strong>label_layout</strong> (<em>str</em><em>, </em><em>default 'NT'</em>) – Layout of the labels. ‘N’, ‘T’ stands for batch size, and sequence
length respectively.</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
</ul>
</dd>
</dl>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.CTCLoss.hybrid_forward" title="mxnet.gluon.loss.CTCLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, …])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: unnormalized prediction tensor (before softmax).
Its shape depends on <cite>layout</cite>. If <cite>layout</cite> is ‘TNC’, pred
should have shape <cite>(sequence_length, batch_size, alphabet_size)</cite>.
Note that in the last dimension, index <cite>alphabet_size-1</cite> is reserved
for internal use as blank label. So <cite>alphabet_size</cite> is one plus the
actual alphabet size.</p></li>
<li><p><strong>label</strong>: zero-based label tensor. Its shape depends on <cite>label_layout</cite>.
If <cite>label_layout</cite> is ‘TN’, <cite>label</cite> should have shape
<cite>(label_sequence_length, batch_size)</cite>.</p></li>
<li><p><strong>pred_lengths</strong>: optional (default None), used for specifying the
length of each entry when different <cite>pred</cite> entries in the same batch
have different lengths. <cite>pred_lengths</cite> should have shape <cite>(batch_size,)</cite>.</p></li>
<li><p><strong>label_lengths</strong>: optional (default None), used for specifying the
length of each entry when different <cite>label</cite> entries in the same batch
have different lengths. <cite>label_lengths</cite> should have shape <cite>(batch_size,)</cite>.</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: output loss has shape <cite>(batch_size,)</cite>.</p></li>
</ul>
</dd>
</dl>
<p><strong>Example</strong>: suppose the vocabulary is <cite>[a, b, c]</cite>, and in one batch we
have three sequences ‘ba’, ‘cbb’, and ‘abac’. We can index the labels as
<cite>{‘a’: 0, ‘b’: 1, ‘c’: 2, blank: 3}</cite>. Then <cite>alphabet_size</cite> should be 4,
where label 3 is reserved for internal use by <cite>CTCLoss</cite>. We then need to
pad each sequence with <cite>-1</cite> to make a rectangular <cite>label</cite> tensor:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">2</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="o">-</span><span class="mi">1</span><span class="p">],</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">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
</pre></div>
</div>
<p class="rubric">References</p>
<p><a class="reference external" href="http://www.cs.toronto.edu/~graves/icml_2006.pdf">Connectionist Temporal Classification: Labelling Unsegmented
Sequence Data with Recurrent Neural Networks</a></p>
<dl class="method">
<dt id="mxnet.gluon.loss.CTCLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">pred_lengths=None</em>, <em class="sig-param">label_lengths=None</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CTCLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CTCLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.HuberLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">HuberLoss</code><span class="sig-paren">(</span><em class="sig-param">rho=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HuberLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HuberLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates smoothed L1 loss that is equal to L1 loss if absolute error
exceeds rho but is equal to L2 loss otherwise. Also called SmoothedL1 loss.</p>
<div class="math notranslate nohighlight">
\[\begin{split}L = \sum_i \begin{cases} \frac{1}{2 {rho}} ({label}_i - {pred}_i)^2 &amp;
\text{ if } |{label}_i - {pred}_i| &lt; {rho} \\
|{label}_i - {pred}_i| - \frac{{rho}}{2} &amp;
\text{ otherwise }
\end{cases}\end{split}\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.HuberLoss.hybrid_forward" title="mxnet.gluon.loss.HuberLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same
number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>rho</strong> (<em>float</em><em>, </em><em>default 1</em>) – Threshold for trimmed mean estimator.</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li>
<li><p><strong>label</strong>: target tensor with the same size as pred.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.HuberLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HuberLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HuberLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.HingeLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">HingeLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HingeLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HingeLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates the hinge loss function often used in SVMs:</p>
<div class="math notranslate nohighlight">
\[L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.HingeLoss.hybrid_forward" title="mxnet.gluon.loss.HingeLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p>where <cite>pred</cite> is the classifier prediction and <cite>label</cite> is the target tensor
containing values -1 or 1. <cite>label</cite> and <cite>pred</cite> must have the same number of
elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>margin</strong> (<em>float</em>) – The margin in hinge loss. Defaults to 1.0</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape.</p></li>
<li><p><strong>label</strong>: truth tensor with values -1 or 1. Must have the same size
as pred.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.HingeLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#HingeLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.HingeLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.SquaredHingeLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">SquaredHingeLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SquaredHingeLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SquaredHingeLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates the soft-margin loss function used in SVMs:</p>
<div class="math notranslate nohighlight">
\[L = \sum_i max(0, {margin} - {pred}_i \cdot {label}_i)^2\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward" title="mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p>where <cite>pred</cite> is the classifier prediction and <cite>label</cite> is the target tensor
containing values -1 or 1. <cite>label</cite> and <cite>pred</cite> can have arbitrary shape as
long as they have the same number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>margin</strong> (<em>float</em>) – The margin in hinge loss. Defaults to 1.0</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li>
<li><p><strong>label</strong>: truth tensor with values -1 or 1. Must have the same size
as pred.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#SquaredHingeLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.SquaredHingeLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.LogisticLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">LogisticLoss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">label_format='signed'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#LogisticLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.LogisticLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates the logistic loss (for binary losses only):</p>
<div class="math notranslate nohighlight">
\[L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.LogisticLoss.hybrid_forward" title="mxnet.gluon.loss.LogisticLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, label[, sample_weight])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p>where <cite>pred</cite> is the classifier prediction and <cite>label</cite> is the target tensor
containing values -1 or 1 (0 or 1 if <cite>label_format</cite> is binary).
<cite>label</cite> and <cite>pred</cite> can have arbitrary shape as long as they have the same number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
<li><p><strong>label_format</strong> (<em>str</em><em>, </em><em>default 'signed'</em>) – Can be either ‘signed’ or ‘binary’. If the label_format is ‘signed’, all label values should
be either -1 or 1. If the label_format is ‘binary’, all label values should be either
0 or 1.</p></li>
<li><p><strong>Inputs</strong> – <ul>
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape.</p></li>
<li><p><strong>label</strong>: truth tensor with values -1/1 (label_format is ‘signed’)
or 0/1 (label_format is ‘binary’). Must have the same size as pred.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</p></li>
<li><p><strong>Outputs</strong> – <ul>
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,). Dimenions other than
batch_axis are averaged out.</p></li>
</ul>
</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.LogisticLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#LogisticLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.LogisticLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.TripletLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">TripletLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=1</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#TripletLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.TripletLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>Calculates triplet loss given three input tensors and a positive margin.
Triplet loss measures the relative similarity between a positive
example, a negative example, and prediction:</p>
<div class="math notranslate nohighlight">
\[L = \sum_i \max(\Vert {pos_i}_i - {pred} \Vert_2^2 -
\Vert {neg_i}_i - {pred} \Vert_2^2 + {margin}, 0)\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.TripletLoss.hybrid_forward" title="mxnet.gluon.loss.TripletLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, positive, negative)</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>positive</cite>, <cite>negative</cite>, and ‘pred’ can have arbitrary shape as long as they
have the same number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>margin</strong> (<em>float</em>) – Margin of separation between correct and incorrect pair.</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: prediction tensor with arbitrary shape</p></li>
<li><p><strong>positive</strong>: positive example tensor with arbitrary shape. Must have
the same size as pred.</p></li>
<li><p><strong>negative</strong>: negative example tensor with arbitrary shape Must have
the same size as pred.</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: loss tensor with shape (batch_size,).</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.TripletLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">positive</em>, <em class="sig-param">negative</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#TripletLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.TripletLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.PoissonNLLLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">PoissonNLLLoss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">from_logits=True</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">compute_full=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#PoissonNLLLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.PoissonNLLLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>For a target (Random Variable) in a Poisson distribution, the function calculates the Negative
Log likelihood loss.
PoissonNLLLoss measures the loss accrued from a poisson regression prediction made by the model.</p>
<div class="math notranslate nohighlight">
\[L = \text{pred} - \text{target} * \log(\text{pred}) +\log(\text{target!})\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward" title="mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, pred, target[, …])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>target</cite>, ‘pred’ can have arbitrary shape as long as they have the same number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>from_logits</strong> (<em>boolean</em><em>, </em><em>default True</em>) – indicating whether log(predicted) value has already been computed. If True, the loss is computed as
<span class="math notranslate nohighlight">\(\exp(\text{pred}) - \text{target} * \text{pred}\)</span>, and if False, then loss is computed as
<span class="math notranslate nohighlight">\(\text{pred} - \text{target} * \log(\text{pred}+\text{epsilon})\)</span>.The default value</p></li>
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
<li><p><strong>compute_full</strong> (<em>boolean</em><em>, </em><em>default False</em>) – Indicates whether to add an approximation(Stirling factor) for the Factorial term in the formula for the loss.
The Stirling factor is:
<span class="math notranslate nohighlight">\(\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})\)</span></p></li>
<li><p><strong>epsilon</strong> (<em>float</em><em>, </em><em>default 1e-08</em>) – This is to avoid calculating log(0) which is not defined.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>pred</strong>: Predicted value</p></li>
<li><p><strong>target</strong>: Random variable(count or number) which belongs to a Poisson distribution.</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as pred. For example, if pred has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: Average loss (shape=(1,1)) of the loss tensor with shape (batch_size,).</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">pred</em>, <em class="sig-param">target</em>, <em class="sig-param">sample_weight=None</em>, <em class="sig-param">epsilon=1e-08</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#PoissonNLLLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.PoissonNLLLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mxnet.gluon.loss.CosineEmbeddingLoss">
<em class="property">class </em><code class="sig-prename descclassname">mxnet.gluon.loss.</code><code class="sig-name descname">CosineEmbeddingLoss</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">batch_axis=0</em>, <em class="sig-param">margin=0</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CosineEmbeddingLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CosineEmbeddingLoss" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#mxnet.gluon.loss.Loss" title="mxnet.gluon.loss.Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.gluon.loss.Loss</span></code></a></p>
<p>For a target label 1 or -1, vectors input1 and input2, the function computes the cosine distance
between the vectors. This can be interpreted as how similar/dissimilar two input vectors are.</p>
<div class="math notranslate nohighlight">
\[\begin{split}L = \sum_i \begin{cases} 1 - {cos\_sim({input1}_i, {input2}_i)} &amp; \text{ if } {label}_i = 1\\
{cos\_sim({input1}_i, {input2}_i)} &amp; \text{ if } {label}_i = -1 \end{cases}\\
cos\_sim(input1, input2) = \frac{{input1}_i.{input2}_i}{||{input1}_i||.||{input2}_i||}\end{split}\]</div>
<p><strong>Methods</strong></p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward" title="mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hybrid_forward</span></code></a>(F, input1, input2, label[, …])</p></td>
<td><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p></td>
</tr>
</tbody>
</table>
<p><cite>input1</cite>, <cite>input2</cite> can have arbitrary shape as long as they have the same number of elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>weight</strong> (<em>float</em><em> or </em><em>None</em>) – Global scalar weight for loss.</p></li>
<li><p><strong>batch_axis</strong> (<em>int</em><em>, </em><em>default 0</em>) – The axis that represents mini-batch.</p></li>
<li><p><strong>margin</strong> (<em>float</em>) – Margin of separation between correct and incorrect pair.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>input1</strong>: a tensor with arbitrary shape</p></li>
<li><p><strong>input2</strong>: another tensor with same shape as pred to which input1 is
compared for similarity and loss calculation</p></li>
<li><p><strong>label</strong>: A 1-D tensor indicating for each pair input1 and input2, target label is 1 or -1</p></li>
<li><p><strong>sample_weight</strong>: element-wise weighting tensor. Must be broadcastable
to the same shape as input1. For example, if input1 has shape (64, 10)
and you want to weigh each sample in the batch separately,
sample_weight should have shape (64, 1).</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><ul class="simple">
<li><p><strong>loss</strong>: The loss tensor with shape (batch_size,).</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward">
<code class="sig-name descname">hybrid_forward</code><span class="sig-paren">(</span><em class="sig-param">F</em>, <em class="sig-param">input1</em>, <em class="sig-param">input2</em>, <em class="sig-param">label</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/gluon/loss.html#CosineEmbeddingLoss.hybrid_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.gluon.loss.CosineEmbeddingLoss.hybrid_forward" title="Permalink to this definition"></a></dt>
<dd><p>Overrides to construct symbolic graph for this <cite>Block</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> (<a class="reference internal" href="../../symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em> or </em><a class="reference internal" href="../../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The first input tensor.</p></li>
<li><p><strong>*args</strong> (<em>list of Symbol</em><em> or </em><em>list of NDArray</em>) – Additional input tensors.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
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