BinaryCrossentropy
==================

Computes the cross-entropy loss between true labels and predicted labels.

**Abstract Signature:**

``BinaryCrossentropy(from_logits: bool = False, label_smoothing: float = 0.0, axis: int = -1, reduction: str = sum_over_batch_size, name: str = binary_crossentropy)``

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        <button class="op-tab-btn active" onclick="openOpTab(event, 'PyTorch_0')">PyTorch</button><button class="op-tab-btn " onclick="openOpTab(event, 'Keras_1')">Keras</button><button class="op-tab-btn " onclick="openOpTab(event, 'TensorFlow_2')">TensorFlow</button><button class="op-tab-btn " onclick="openOpTab(event, 'Apple MLX_3')">Apple MLX</button><button class="op-tab-btn " onclick="openOpTab(event, 'Flax NNX_4')">Flax NNX</button><button class="op-tab-btn " onclick="openOpTab(event, 'PaxML / Praxis_5')">PaxML / Praxis</button> 
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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.BCELoss</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.BCELoss.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Keras_1" class="op-tab-pane "><h4>Keras</h4><div class="op-detail-row"><span class="label">API:</span> <code>keras.losses.BinaryCrossentropy</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://keras.io/search.html?q=keras.losses.BinaryCrossentropy" target="_blank" class="op-doc-link">Official Docs ↗</a></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.BinaryCrossentropy</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://www.tensorflow.org/api_docs/python/tf/keras/losses/BinaryCrossentropy" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Apple MLX_3" class="op-tab-pane "><h4>Apple MLX</h4><div class="op-detail-row"><span class="label">API:</span> <code>mlx.nn.losses.BinaryCrossEntropy</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://ml-explore.github.io/mlx/build/html/python/_autosummary/mlx.nn.losses.BinaryCrossEntropy.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Flax NNX_4" class="op-tab-pane "><h4>Flax NNX</h4><div class="op-detail-row"><span class="label">API:</span> <code>optax.sigmoid_binary_cross_entropy</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Plugin (loss_wrapper)</span></div></div><div id="PaxML / Praxis_5" class="op-tab-pane "><h4>PaxML / Praxis</h4><div class="op-detail-row"><span class="label">API:</span> <code>optax.sigmoid_binary_cross_entropy</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://github.com/search?q=repo%3Agoogle%2Fpaxml+optax.sigmoid_binary_cross_entropy&type=code" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div> 
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