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docs update
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docs/build/html/python/nn/layers.html
vendored
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docs/build/html/python/nn/layers.html
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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<title>Layers — MLX 0.17.3 documentation</title>
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<title>Layers — MLX 0.18.0 documentation</title>
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<link rel="preload" as="script" href="../../_static/scripts/pydata-sphinx-theme.js?digest=5b4479735964841361fd" />
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<img src="../../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.17.3 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.18.0 documentation - Home"/>`);</script>
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</a></div>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.pad.html">mlx.core.pad</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.power.html">mlx.core.power</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.prod.html">mlx.core.prod</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.put_along_axis.html">mlx.core.put_along_axis</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.quantize.html">mlx.core.quantize</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.quantized_matmul.html">mlx.core.quantized_matmul</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.radians.html">mlx.core.radians</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.linalg.norm.html">mlx.core.linalg.norm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.linalg.cholesky.html">mlx.core.linalg.cholesky</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.linalg.cholesky_inv.html">mlx.core.linalg.cholesky_inv</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.linalg.cross.html">mlx.core.linalg.cross</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.linalg.qr.html">mlx.core.linalg.qr</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.linalg.svd.html">mlx.core.linalg.svd</a></li>
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</ul>
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@@ -518,6 +520,7 @@
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.AvgPool1d.html">mlx.nn.AvgPool1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.AvgPool2d.html">mlx.nn.AvgPool2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.BatchNorm.html">mlx.nn.BatchNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.CELU.html">mlx.nn.CELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Conv1d.html">mlx.nn.Conv1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Conv3d.html">mlx.nn.Conv3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Dropout2d.html">mlx.nn.Dropout2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Dropout3d.html">mlx.nn.Dropout3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Embedding.html">mlx.nn.Embedding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.ELU.html">mlx.nn.ELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.GELU.html">mlx.nn.GELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.GLU.html">mlx.nn.GLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.GroupNorm.html">mlx.nn.GroupNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.LeakyReLU.html">mlx.nn.LeakyReLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Linear.html">mlx.nn.Linear</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.LogSigmoid.html">mlx.nn.LogSigmoid</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.LogSoftmax.html">mlx.nn.LogSoftmax</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.LSTM.html">mlx.nn.LSTM</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.MaxPool1d.html">mlx.nn.MaxPool1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.MaxPool2d.html">mlx.nn.MaxPool2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.RoPE.html">mlx.nn.RoPE</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.SELU.html">mlx.nn.SELU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Sequential.html">mlx.nn.Sequential</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Sigmoid.html">mlx.nn.Sigmoid</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.SiLU.html">mlx.nn.SiLU</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.SinusoidalPositionalEncoding.html">mlx.nn.SinusoidalPositionalEncoding</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Softmin.html">mlx.nn.Softmin</a></li>
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</li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="functions.html">Functions</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-14"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.elu.html">mlx.nn.elu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.celu.html">mlx.nn.celu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.gelu.html">mlx.nn.gelu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.gelu_approx.html">mlx.nn.gelu_approx</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary_functions/mlx.nn.gelu_fast_approx.html">mlx.nn.gelu_fast_approx</a></li>
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@@ -865,36 +873,42 @@ document.write(`
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.BatchNorm.html#mlx.nn.BatchNorm" title="mlx.nn.BatchNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BatchNorm</span></code></a>(num_features[, eps, momentum, ...])</p></td>
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<td><p>Applies Batch Normalization over a 2D or 3D input.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv1d.html#mlx.nn.Conv1d" title="mlx.nn.Conv1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv1d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.CELU.html#mlx.nn.CELU" title="mlx.nn.CELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CELU</span></code></a>([alpha])</p></td>
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<td><p>Applies the Continuously Differentiable Exponential Linear Unit.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv1d.html#mlx.nn.Conv1d" title="mlx.nn.Conv1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv1d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
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<td><p>Applies a 1-dimensional convolution over the multi-channel input sequence.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv2d.html#mlx.nn.Conv2d" title="mlx.nn.Conv2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv2d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv2d.html#mlx.nn.Conv2d" title="mlx.nn.Conv2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv2d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
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<td><p>Applies a 2-dimensional convolution over the multi-channel input image.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv3d.html#mlx.nn.Conv3d" title="mlx.nn.Conv3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv3d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Conv3d.html#mlx.nn.Conv3d" title="mlx.nn.Conv3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Conv3d</span></code></a>(in_channels, out_channels, kernel_size)</p></td>
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<td><p>Applies a 3-dimensional convolution over the multi-channel input image.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose1d.html#mlx.nn.ConvTranspose1d" title="mlx.nn.ConvTranspose1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a>(in_channels, out_channels, ...)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose1d.html#mlx.nn.ConvTranspose1d" title="mlx.nn.ConvTranspose1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a>(in_channels, out_channels, ...)</p></td>
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<td><p>Applies a 1-dimensional transposed convolution over the multi-channel input sequence.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose2d.html#mlx.nn.ConvTranspose2d" title="mlx.nn.ConvTranspose2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a>(in_channels, out_channels, ...)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose2d.html#mlx.nn.ConvTranspose2d" title="mlx.nn.ConvTranspose2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a>(in_channels, out_channels, ...)</p></td>
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<td><p>Applies a 2-dimensional transposed convolution over the multi-channel input image.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose3d.html#mlx.nn.ConvTranspose3d" title="mlx.nn.ConvTranspose3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a>(in_channels, out_channels, ...)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose3d.html#mlx.nn.ConvTranspose3d" title="mlx.nn.ConvTranspose3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a>(in_channels, out_channels, ...)</p></td>
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<td><p>Applies a 3-dimensional transposed convolution over the multi-channel input image.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout.html#mlx.nn.Dropout" title="mlx.nn.Dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout</span></code></a>([p])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout.html#mlx.nn.Dropout" title="mlx.nn.Dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout</span></code></a>([p])</p></td>
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<td><p>Randomly zero a portion of the elements during training.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout2d.html#mlx.nn.Dropout2d" title="mlx.nn.Dropout2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout2d</span></code></a>([p])</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout2d.html#mlx.nn.Dropout2d" title="mlx.nn.Dropout2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout2d</span></code></a>([p])</p></td>
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<td><p>Apply 2D channel-wise dropout during training.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout3d.html#mlx.nn.Dropout3d" title="mlx.nn.Dropout3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout3d</span></code></a>([p])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Dropout3d.html#mlx.nn.Dropout3d" title="mlx.nn.Dropout3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dropout3d</span></code></a>([p])</p></td>
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<td><p>Apply 3D channel-wise dropout during training.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Embedding.html#mlx.nn.Embedding" title="mlx.nn.Embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Embedding</span></code></a>(num_embeddings, dims)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Embedding.html#mlx.nn.Embedding" title="mlx.nn.Embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Embedding</span></code></a>(num_embeddings, dims)</p></td>
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<td><p>Implements a simple lookup table that maps each input integer to a high-dimensional vector.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ELU.html#mlx.nn.ELU" title="mlx.nn.ELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ELU</span></code></a>([alpha])</p></td>
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<td><p>Applies the Exponential Linear Unit.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GELU.html#mlx.nn.GELU" title="mlx.nn.GELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GELU</span></code></a>([approx])</p></td>
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<td><p>Applies the Gaussian Error Linear Units.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Linear.html#mlx.nn.Linear" title="mlx.nn.Linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Linear</span></code></a>(input_dims, output_dims[, bias])</p></td>
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<td><p>Applies an affine transformation to the input.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LogSigmoid.html#mlx.nn.LogSigmoid" title="mlx.nn.LogSigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogSigmoid</span></code></a>()</p></td>
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<td><p>Applies the Log Sigmoid function.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LogSoftmax.html#mlx.nn.LogSoftmax" title="mlx.nn.LogSoftmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LogSoftmax</span></code></a>()</p></td>
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<td><p>Applies the Log Softmax function.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LSTM.html#mlx.nn.LSTM" title="mlx.nn.LSTM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LSTM</span></code></a>(input_size, hidden_size[, bias])</p></td>
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<td><p>An LSTM recurrent layer.</p></td>
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</tr>
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@@ -973,37 +993,40 @@ document.write(`
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Sequential.html#mlx.nn.Sequential" title="mlx.nn.Sequential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sequential</span></code></a>(*modules)</p></td>
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<td><p>A layer that calls the passed callables in order.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SiLU.html#mlx.nn.SiLU" title="mlx.nn.SiLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SiLU</span></code></a>()</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Sigmoid.html#mlx.nn.Sigmoid" title="mlx.nn.Sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sigmoid</span></code></a>()</p></td>
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<td><p>Applies the sigmoid function, element-wise.</p></td>
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</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SiLU.html#mlx.nn.SiLU" title="mlx.nn.SiLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SiLU</span></code></a>()</p></td>
|
||||
<td><p>Applies the Sigmoid Linear Unit.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SinusoidalPositionalEncoding.html#mlx.nn.SinusoidalPositionalEncoding" title="mlx.nn.SinusoidalPositionalEncoding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SinusoidalPositionalEncoding</span></code></a>(dims[, ...])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SinusoidalPositionalEncoding.html#mlx.nn.SinusoidalPositionalEncoding" title="mlx.nn.SinusoidalPositionalEncoding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SinusoidalPositionalEncoding</span></code></a>(dims[, ...])</p></td>
|
||||
<td><p>Implements sinusoidal positional encoding.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softmin.html#mlx.nn.Softmin" title="mlx.nn.Softmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softmin</span></code></a>()</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softmin.html#mlx.nn.Softmin" title="mlx.nn.Softmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softmin</span></code></a>()</p></td>
|
||||
<td><p>Applies the Softmin function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softshrink.html#mlx.nn.Softshrink" title="mlx.nn.Softshrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softshrink</span></code></a>([lambd])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softshrink.html#mlx.nn.Softshrink" title="mlx.nn.Softshrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softshrink</span></code></a>([lambd])</p></td>
|
||||
<td><p>Applies the Softshrink function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softsign.html#mlx.nn.Softsign" title="mlx.nn.Softsign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softsign</span></code></a>()</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softsign.html#mlx.nn.Softsign" title="mlx.nn.Softsign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softsign</span></code></a>()</p></td>
|
||||
<td><p>Applies the Softsign function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softmax.html#mlx.nn.Softmax" title="mlx.nn.Softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softmax</span></code></a>()</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softmax.html#mlx.nn.Softmax" title="mlx.nn.Softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softmax</span></code></a>()</p></td>
|
||||
<td><p>Applies the Softmax function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softplus.html#mlx.nn.Softplus" title="mlx.nn.Softplus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softplus</span></code></a>()</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Softplus.html#mlx.nn.Softplus" title="mlx.nn.Softplus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Softplus</span></code></a>()</p></td>
|
||||
<td><p>Applies the Softplus function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Step.html#mlx.nn.Step" title="mlx.nn.Step"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Step</span></code></a>([threshold])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Step.html#mlx.nn.Step" title="mlx.nn.Step"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Step</span></code></a>([threshold])</p></td>
|
||||
<td><p>Applies the Step Activation Function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Tanh.html#mlx.nn.Tanh" title="mlx.nn.Tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tanh</span></code></a>()</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Tanh.html#mlx.nn.Tanh" title="mlx.nn.Tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tanh</span></code></a>()</p></td>
|
||||
<td><p>Applies the hyperbolic tangent function.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Transformer.html#mlx.nn.Transformer" title="mlx.nn.Transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformer</span></code></a>(dims, num_heads, ...)</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Transformer.html#mlx.nn.Transformer" title="mlx.nn.Transformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformer</span></code></a>(dims, num_heads, ...)</p></td>
|
||||
<td><p>Implements a standard Transformer model.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Upsample.html#mlx.nn.Upsample" title="mlx.nn.Upsample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Upsample</span></code></a>(scale_factor[, mode, align_corners])</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Upsample.html#mlx.nn.Upsample" title="mlx.nn.Upsample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Upsample</span></code></a>(scale_factor[, mode, align_corners])</p></td>
|
||||
<td><p>Upsample the input signal spatially.</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
|
||||
Reference in New Issue
Block a user