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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 0.17.0 documentation</title>
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<title>Layers — MLX 0.17.3 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 0.17.0 documentation - Home"/>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.17.0 documentation - Home"/>`);</script>
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<script>document.write(`<img src="../../_static/mlx_logo_dark.png" class="logo__image only-dark" alt="MLX 0.17.3 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.array.sqrt.html">mlx.core.array.sqrt</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.square.html">mlx.core.array.square</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.squeeze.html">mlx.core.array.squeeze</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.swapaxes.html">mlx.core.array.swapaxes</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.std.html">mlx.core.array.std</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.sum.html">mlx.core.array.sum</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.swapaxes.html">mlx.core.array.swapaxes</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.transpose.html">mlx.core.array.transpose</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.array.T.html">mlx.core.array.T</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.convolve.html">mlx.core.convolve</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv1d.html">mlx.core.conv1d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv2d.html">mlx.core.conv2d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv3d.html">mlx.core.conv3d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv_transpose1d.html">mlx.core.conv_transpose1d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv_transpose2d.html">mlx.core.conv_transpose2d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv_transpose3d.html">mlx.core.conv_transpose3d</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.conv_general.html">mlx.core.conv_general</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.cos.html">mlx.core.cos</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.hadamard_transform.html">mlx.core.hadamard_transform</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.identity.html">mlx.core.identity</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.inner.html">mlx.core.inner</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.isfinite.html">mlx.core.isfinite</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.isclose.html">mlx.core.isclose</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.isinf.html">mlx.core.isinf</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.ConvTranspose1d.html">mlx.nn.ConvTranspose1d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose2d.html">mlx.nn.ConvTranspose2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.ConvTranspose3d.html">mlx.nn.ConvTranspose3d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="_autosummary/mlx.nn.Dropout.html">mlx.nn.Dropout</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-l2"><a class="reference internal" href="../_autosummary/mlx.core.distributed.init.html">mlx.core.distributed.init</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.distributed.all_sum.html">mlx.core.distributed.all_sum</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.distributed.all_gather.html">mlx.core.distributed.all_gather</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.distributed.send.html">mlx.core.distributed.send</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.distributed.recv.html">mlx.core.distributed.recv</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../_autosummary/mlx.core.distributed.recv_like.html">mlx.core.distributed.recv_like</a></li>
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</ul>
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</li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-22" name="toctree-checkbox-22" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-22"><i class="fa-solid fa-chevron-down"></i></label><ul>
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@@ -862,127 +874,136 @@ document.write(`
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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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<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.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.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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<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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<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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<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-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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<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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<td><p>Apply 2D 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.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-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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<td><p>Apply 3D 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.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-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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<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.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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<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.GLU.html#mlx.nn.GLU" title="mlx.nn.GLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GLU</span></code></a>([axis])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GLU.html#mlx.nn.GLU" title="mlx.nn.GLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GLU</span></code></a>([axis])</p></td>
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<td><p>Applies the gated linear unit 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.GroupNorm.html#mlx.nn.GroupNorm" title="mlx.nn.GroupNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupNorm</span></code></a>(num_groups, dims[, eps, affine, ...])</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GroupNorm.html#mlx.nn.GroupNorm" title="mlx.nn.GroupNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GroupNorm</span></code></a>(num_groups, dims[, eps, affine, ...])</p></td>
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<td><p>Applies Group Normalization [1] to the inputs.</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.GRU.html#mlx.nn.GRU" title="mlx.nn.GRU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GRU</span></code></a>(input_size, hidden_size[, bias])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.GRU.html#mlx.nn.GRU" title="mlx.nn.GRU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GRU</span></code></a>(input_size, hidden_size[, bias])</p></td>
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<td><p>A gated recurrent unit (GRU) RNN layer.</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.HardShrink.html#mlx.nn.HardShrink" title="mlx.nn.HardShrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HardShrink</span></code></a>()</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.HardShrink.html#mlx.nn.HardShrink" title="mlx.nn.HardShrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HardShrink</span></code></a>()</p></td>
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<td><p>Applies the HardShrink 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.HardTanh.html#mlx.nn.HardTanh" title="mlx.nn.HardTanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HardTanh</span></code></a>()</p></td>
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||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.HardTanh.html#mlx.nn.HardTanh" title="mlx.nn.HardTanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HardTanh</span></code></a>()</p></td>
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<td><p>Applies the HardTanh 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.Hardswish.html#mlx.nn.Hardswish" title="mlx.nn.Hardswish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Hardswish</span></code></a>()</p></td>
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||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Hardswish.html#mlx.nn.Hardswish" title="mlx.nn.Hardswish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Hardswish</span></code></a>()</p></td>
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<td><p>Applies the hardswish function, element-wise.</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.InstanceNorm.html#mlx.nn.InstanceNorm" title="mlx.nn.InstanceNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InstanceNorm</span></code></a>(dims[, eps, affine])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.InstanceNorm.html#mlx.nn.InstanceNorm" title="mlx.nn.InstanceNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InstanceNorm</span></code></a>(dims[, eps, affine])</p></td>
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<td><p>Applies instance normalization [1] on the inputs.</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.LayerNorm.html#mlx.nn.LayerNorm" title="mlx.nn.LayerNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LayerNorm</span></code></a>(dims[, eps, affine, bias])</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LayerNorm.html#mlx.nn.LayerNorm" title="mlx.nn.LayerNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LayerNorm</span></code></a>(dims[, eps, affine, bias])</p></td>
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<td><p>Applies layer normalization [1] on the inputs.</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.LeakyReLU.html#mlx.nn.LeakyReLU" title="mlx.nn.LeakyReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a>([negative_slope])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.LeakyReLU.html#mlx.nn.LeakyReLU" title="mlx.nn.LeakyReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a>([negative_slope])</p></td>
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<td><p>Applies the Leaky Rectified 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.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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<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-odd"><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>
|
||||
<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>
|
||||
<td><p>An LSTM recurrent layer.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool1d.html#mlx.nn.MaxPool1d" title="mlx.nn.MaxPool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>(kernel_size[, stride, padding])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool1d.html#mlx.nn.MaxPool1d" title="mlx.nn.MaxPool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>(kernel_size[, stride, padding])</p></td>
|
||||
<td><p>Applies 1-dimensional max pooling.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool2d.html#mlx.nn.MaxPool2d" title="mlx.nn.MaxPool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>(kernel_size[, stride, padding])</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MaxPool2d.html#mlx.nn.MaxPool2d" title="mlx.nn.MaxPool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>(kernel_size[, stride, padding])</p></td>
|
||||
<td><p>Applies 2-dimensional max pooling.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Mish.html#mlx.nn.Mish" title="mlx.nn.Mish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Mish</span></code></a>()</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.Mish.html#mlx.nn.Mish" title="mlx.nn.Mish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Mish</span></code></a>()</p></td>
|
||||
<td><p>Applies the Mish function, element-wise.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MultiHeadAttention.html#mlx.nn.MultiHeadAttention" title="mlx.nn.MultiHeadAttention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiHeadAttention</span></code></a>(dims, num_heads[, ...])</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.MultiHeadAttention.html#mlx.nn.MultiHeadAttention" title="mlx.nn.MultiHeadAttention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiHeadAttention</span></code></a>(dims, num_heads[, ...])</p></td>
|
||||
<td><p>Implements the scaled dot product attention with multiple heads.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.PReLU.html#mlx.nn.PReLU" title="mlx.nn.PReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PReLU</span></code></a>([num_parameters, init])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.PReLU.html#mlx.nn.PReLU" title="mlx.nn.PReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PReLU</span></code></a>([num_parameters, init])</p></td>
|
||||
<td><p>Applies the element-wise parametric ReLU.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.QuantizedEmbedding.html#mlx.nn.QuantizedEmbedding" title="mlx.nn.QuantizedEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedEmbedding</span></code></a>(num_embeddings, dims[, ...])</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.QuantizedEmbedding.html#mlx.nn.QuantizedEmbedding" title="mlx.nn.QuantizedEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedEmbedding</span></code></a>(num_embeddings, dims[, ...])</p></td>
|
||||
<td><p>The same as <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> but with a quantized weight matrix.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.QuantizedLinear.html#mlx.nn.QuantizedLinear" title="mlx.nn.QuantizedLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a>(input_dims, output_dims[, ...])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.QuantizedLinear.html#mlx.nn.QuantizedLinear" title="mlx.nn.QuantizedLinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantizedLinear</span></code></a>(input_dims, output_dims[, ...])</p></td>
|
||||
<td><p>Applies an affine transformation to the input using a quantized weight matrix.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RMSNorm.html#mlx.nn.RMSNorm" title="mlx.nn.RMSNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RMSNorm</span></code></a>(dims[, eps])</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RMSNorm.html#mlx.nn.RMSNorm" title="mlx.nn.RMSNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RMSNorm</span></code></a>(dims[, eps])</p></td>
|
||||
<td><p>Applies Root Mean Square normalization [1] to the inputs.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ReLU.html#mlx.nn.ReLU" title="mlx.nn.ReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReLU</span></code></a>()</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ReLU.html#mlx.nn.ReLU" title="mlx.nn.ReLU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReLU</span></code></a>()</p></td>
|
||||
<td><p>Applies the Rectified Linear Unit.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ReLU6.html#mlx.nn.ReLU6" title="mlx.nn.ReLU6"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReLU6</span></code></a>()</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.ReLU6.html#mlx.nn.ReLU6" title="mlx.nn.ReLU6"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ReLU6</span></code></a>()</p></td>
|
||||
<td><p>Applies the Rectified Linear Unit 6.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RNN.html#mlx.nn.RNN" title="mlx.nn.RNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RNN</span></code></a>(input_size, hidden_size[, bias, ...])</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RNN.html#mlx.nn.RNN" title="mlx.nn.RNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RNN</span></code></a>(input_size, hidden_size[, bias, ...])</p></td>
|
||||
<td><p>An Elman recurrent layer.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RoPE.html#mlx.nn.RoPE" title="mlx.nn.RoPE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RoPE</span></code></a>(dims[, traditional, base, scale])</p></td>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.RoPE.html#mlx.nn.RoPE" title="mlx.nn.RoPE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RoPE</span></code></a>(dims[, traditional, base, scale])</p></td>
|
||||
<td><p>Implements the rotary positional encoding.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SELU.html#mlx.nn.SELU" title="mlx.nn.SELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SELU</span></code></a>()</p></td>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.nn.SELU.html#mlx.nn.SELU" title="mlx.nn.SELU"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SELU</span></code></a>()</p></td>
|
||||
<td><p>Applies the Scaled Exponential Linear Unit.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><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>
|
||||
<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>
|
||||
<td><p>A layer that calls the passed callables in order.</p></td>
|
||||
</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>
|
||||
<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>
|
||||
<td><p>Applies the Sigmoid Linear Unit.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Implements sinusoidal positional encoding.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the Softmin function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the Softshrink function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the Softsign function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the Softmax function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the Softplus function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the Step Activation Function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Applies the hyperbolic tangent function.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Implements a standard Transformer model.</p></td>
|
||||
</tr>
|
||||
<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>
|
||||
<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>
|
||||
<td><p>Upsample the input signal spatially.</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
|
||||
Reference in New Issue
Block a user