TripletMarginLoss
=================

Creates a criterion that measures the triplet loss.

**Abstract Signature:**

``TripletMarginLoss(anchor: Tensor, positive: Tensor, negative: Tensor, margin: float = 1.0, p: float = 2.0, eps: float = 1e-06, swap: bool = False)``

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        <div id="PyTorch_0" class="op-tab-pane active"><h4>PyTorch</h4><div class="op-detail-row"><span class="label">API:</span> <code>torch.nn.functional.triplet_margin_loss</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Direct Mapping</span></div><div class="op-detail-row"><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.triplet_margin_loss.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="JAX (Core)_1" class="op-tab-pane "><h4>JAX (Core)</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Plugin (triplet_loss_impl)</span></div></div><div id="TensorFlow_2" class="op-tab-pane "><h4>TensorFlow</h4><div class="op-detail-row"><span class="label">API:</span> <code>tf.keras.losses.triplet_semihard_loss</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro 'tf.maximum(tf.norm({anchor}-{positive}, ord={p}) - tf.norm({anchor}-{negative}, ord={p}) + {margin}, 0)'</span></div></div> 
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