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< h1 > Neural Networks< / h1 >
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< li class = "toc-h2 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#quick-start-with-neural-networks" > Quick Start with Neural Networks< / a > < / li >
< li class = "toc-h2 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#the-module-class" > The Module Class< / a > < ul class = "visible nav section-nav flex-column" >
< li class = "toc-h3 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#parameters" > Parameters< / a > < / li >
< li class = "toc-h3 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#updating-the-parameters" > Updating the parameters< / a > < / li >
< / ul >
< / li >
< li class = "toc-h2 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#value-and-grad" > Value and grad< / a > < / li >
< li class = "toc-h2 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#neural-network-layers" > Neural Network Layers< / a > < ul class = "visible nav section-nav flex-column" >
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< section id = "neural-networks" >
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< span id = "nn" > < / span > < h1 > Neural Networks< a class = "headerlink" href = "#neural-networks" title = "Permalink to this heading" > #< / a > < / h1 >
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< p > Writing arbitrarily complex neural networks in MLX can be done using only
< a class = "reference internal" href = "_autosummary/mlx.core.array.html#mlx.core.array" title = "mlx.core.array" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > mlx.core.array< / span > < / code > < / a > and < a class = "reference internal" href = "_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title = "mlx.core.value_and_grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.core.value_and_grad()< / span > < / code > < / a > . However, this requires the
user to write again and again the same simple neural network operations as well
as handle all the parameter state and initialization manually and explicitly.< / p >
< p > The module < code class = "xref py py-mod docutils literal notranslate" > < span class = "pre" > mlx.nn< / span > < / code > solves this problem by providing an intuitive way of
composing neural network layers, initializing their parameters, freezing them
for finetuning and more.< / p >
< section id = "quick-start-with-neural-networks" >
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< h2 > Quick Start with Neural Networks< a class = "headerlink" href = "#quick-start-with-neural-networks" title = "Permalink to this heading" > #< / a > < / h2 >
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< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > mlx.core< / span > < span class = "k" > as< / span > < span class = "nn" > mx< / span >
< span class = "kn" > import< / span > < span class = "nn" > mlx.nn< / span > < span class = "k" > as< / span > < span class = "nn" > nn< / span >
< span class = "k" > class< / span > < span class = "nc" > MLP< / span > < span class = "p" > (< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Module< / span > < span class = "p" > ):< / span >
< span class = "k" > def< / span > < span class = "fm" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > in_dims< / span > < span class = "p" > :< / span > < span class = "nb" > int< / span > < span class = "p" > ,< / span > < span class = "n" > out_dims< / span > < span class = "p" > :< / span > < span class = "nb" > int< / span > < span class = "p" > ):< / span >
< span class = "nb" > super< / span > < span class = "p" > ()< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layers< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Linear< / span > < span class = "p" > (< / span > < span class = "n" > in_dims< / span > < span class = "p" > ,< / span > < span class = "mi" > 128< / span > < span class = "p" > ),< / span >
< span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Linear< / span > < span class = "p" > (< / span > < span class = "mi" > 128< / span > < span class = "p" > ,< / span > < span class = "mi" > 128< / span > < span class = "p" > ),< / span >
< span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Linear< / span > < span class = "p" > (< / span > < span class = "mi" > 128< / span > < span class = "p" > ,< / span > < span class = "n" > out_dims< / span > < span class = "p" > ),< / span >
< span class = "p" > ]< / span >
< span class = "k" > def< / span > < span class = "fm" > __call__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "p" > ,< / span > < span class = "n" > l< / span > < span class = "ow" > in< / span > < span class = "nb" > enumerate< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > layers< / span > < span class = "p" > ):< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > maximum< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ,< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span > < span class = "k" > if< / span > < span class = "n" > i< / span > < span class = "o" > > < / span > < span class = "mi" > 0< / span > < span class = "k" > else< / span > < span class = "n" > x< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > l< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > x< / span >
< span class = "c1" > # The model is created with all its parameters but nothing is initialized< / span >
< span class = "c1" > # yet because MLX is lazily evaluated< / span >
< span class = "n" > mlp< / span > < span class = "o" > =< / span > < span class = "n" > MLP< / span > < span class = "p" > (< / span > < span class = "mi" > 2< / span > < span class = "p" > ,< / span > < span class = "mi" > 10< / span > < span class = "p" > )< / span >
< span class = "c1" > # We can access its parameters by calling mlp.parameters()< / span >
< span class = "n" > params< / span > < span class = "o" > =< / span > < span class = "n" > mlp< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ()< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s2" > " layers" < / span > < span class = "p" > ][< / span > < span class = "mi" > 0< / span > < span class = "p" > ][< / span > < span class = "s2" > " weight" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > )< / span >
< span class = "c1" > # Printing a parameter will cause it to be evaluated and thus initialized< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > params< / span > < span class = "p" > [< / span > < span class = "s2" > " layers" < / span > < span class = "p" > ][< / span > < span class = "mi" > 0< / span > < span class = "p" > ])< / span >
< span class = "c1" > # We can also force evaluate all parameters to initialize the model< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > (< / span > < span class = "n" > mlp< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ())< / span >
< span class = "c1" > # A simple loss function.< / span >
< span class = "c1" > # NOTE: It doesn' t matter how it uses the mlp model. It currently captures< / span >
< span class = "c1" > # it from the local scope. It could be a positional argument or a< / span >
< span class = "c1" > # keyword argument.< / span >
< span class = "k" > def< / span > < span class = "nf" > l2_loss< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > ):< / span >
< span class = "n" > y_hat< / span > < span class = "o" > =< / span > < span class = "n" > mlp< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "p" > (< / span > < span class = "n" > y_hat< / span > < span class = "o" > -< / span > < span class = "n" > y< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > square< / span > < span class = "p" > ()< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > ()< / span >
< span class = "c1" > # Calling `nn.value_and_grad` instead of `mx.value_and_grad` returns the< / span >
< span class = "c1" > # gradient with respect to `mlp.trainable_parameters()`< / span >
< span class = "n" > loss_and_grad< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > value_and_grad< / span > < span class = "p" > (< / span > < span class = "n" > mlp< / span > < span class = "p" > ,< / span > < span class = "n" > l2_loss< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / section >
< section id = "the-module-class" >
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< span id = "module-class" > < / span > < h2 > The Module Class< a class = "headerlink" href = "#the-module-class" title = "Permalink to this heading" > #< / a > < / h2 >
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< p > The workhorse of any neural network library is the < a class = "reference internal" href = "nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > Module< / span > < / code > < / a > class. In
MLX the < a class = "reference internal" href = "nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > Module< / span > < / code > < / a > class is a container of < a class = "reference internal" href = "_autosummary/mlx.core.array.html#mlx.core.array" title = "mlx.core.array" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > mlx.core.array< / span > < / code > < / a > or
< a class = "reference internal" href = "nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > Module< / span > < / code > < / a > instances. Its main function is to provide a way to
recursively < strong > access< / strong > and < strong > update< / strong > its parameters and those of its
submodules.< / p >
< section id = "parameters" >
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< h3 > Parameters< a class = "headerlink" href = "#parameters" title = "Permalink to this heading" > #< / a > < / h3 >
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< p > A parameter of a module is any public member of type < a class = "reference internal" href = "_autosummary/mlx.core.array.html#mlx.core.array" title = "mlx.core.array" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > mlx.core.array< / span > < / code > < / a > (its
name should not start with < code class = "docutils literal notranslate" > < span class = "pre" > _< / span > < / code > ). It can be arbitrarily nested in other
< a class = "reference internal" href = "nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > Module< / span > < / code > < / a > instances or lists and dictionaries.< / p >
< p > < a class = "reference internal" href = "nn/module.html#mlx.nn.Module.parameters" title = "mlx.nn.Module.parameters" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > Module.parameters()< / span > < / code > < / a > can be used to extract a nested dictionary with all
the parameters of a module and its submodules.< / p >
< p > A < a class = "reference internal" href = "nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > Module< / span > < / code > < / a > can also keep track of “frozen” parameters.
< a class = "reference internal" href = "nn/module.html#mlx.nn.Module.trainable_parameters" title = "mlx.nn.Module.trainable_parameters" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > Module.trainable_parameters()< / span > < / code > < / a > returns only the subset of
< a class = "reference internal" href = "nn/module.html#mlx.nn.Module.parameters" title = "mlx.nn.Module.parameters" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > Module.parameters()< / span > < / code > < / a > that is not frozen. When using
< a class = "reference internal" href = "_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title = "mlx.nn.value_and_grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.nn.value_and_grad()< / span > < / code > < / a > the gradients returned will be with respect to these
trainable parameters.< / p >
< / section >
< section id = "updating-the-parameters" >
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< h3 > Updating the parameters< a class = "headerlink" href = "#updating-the-parameters" title = "Permalink to this heading" > #< / a > < / h3 >
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< p > MLX modules allow accessing and updating individual parameters. However, most
times we need to update large subsets of a module’
performed by < a class = "reference internal" href = "nn/module.html#mlx.nn.Module.update" title = "mlx.nn.Module.update" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > Module.update()< / span > < / code > < / a > .< / p >
< / section >
< / section >
< section id = "value-and-grad" >
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< h2 > Value and grad< a class = "headerlink" href = "#value-and-grad" title = "Permalink to this heading" > #< / a > < / h2 >
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< p > Using a < a class = "reference internal" href = "nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-class docutils literal notranslate" > < span class = "pre" > Module< / span > < / code > < / a > does not preclude using MLX’
transformations (< a class = "reference internal" href = "_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title = "mlx.core.value_and_grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.core.value_and_grad()< / span > < / code > < / a > , < a class = "reference internal" href = "_autosummary/mlx.core.grad.html#mlx.core.grad" title = "mlx.core.grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.core.grad()< / span > < / code > < / a > , etc.). However,
these function transformations assume pure functions, namely the parameters
should be passed as an argument to the function being transformed.< / p >
< p > There is an easy pattern to achieve that with MLX modules< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "o" > ...< / span >
< span class = "k" > def< / span > < span class = "nf" > f< / span > < span class = "p" > (< / span > < span class = "n" > params< / span > < span class = "p" > ,< / span > < span class = "n" > other_inputs< / span > < span class = "p" > ):< / span >
< span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > update< / span > < span class = "p" > (< / span > < span class = "n" > params< / span > < span class = "p" > )< / span > < span class = "c1" > # < ---- Necessary to make the model use the passed parameters< / span >
< span class = "k" > return< / span > < span class = "n" > model< / span > < span class = "p" > (< / span > < span class = "n" > other_inputs< / span > < span class = "p" > )< / span >
< span class = "n" > f< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > trainable_parameters< / span > < span class = "p" > (),< / span > < span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > zeros< / span > < span class = "p" > ((< / span > < span class = "mi" > 10< / span > < span class = "p" > ,)))< / span >
< / pre > < / div >
< / div >
< p > However, < a class = "reference internal" href = "_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title = "mlx.nn.value_and_grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.nn.value_and_grad()< / span > < / code > < / a > provides precisely this pattern and only
computes the gradients with respect to the trainable parameters of the model.< / p >
< p > In detail:< / p >
< ul class = "simple" >
< li > < p > it wraps the passed function with a function that calls < a class = "reference internal" href = "nn/module.html#mlx.nn.Module.update" title = "mlx.nn.Module.update" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > Module.update()< / span > < / code > < / a >
to make sure the model is using the provided parameters.< / p > < / li >
< li > < p > it calls < a class = "reference internal" href = "_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title = "mlx.core.value_and_grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.core.value_and_grad()< / span > < / code > < / a > to transform the function into a function
that also computes the gradients with respect to the passed parameters.< / p > < / li >
< li > < p > it wraps the returned function with a function that passes the trainable
parameters as the first argument to the function returned by
< a class = "reference internal" href = "_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title = "mlx.core.value_and_grad" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > mlx.core.value_and_grad()< / span > < / code > < / a > < / p > < / li >
< / ul >
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< table class = "autosummary longtable table autosummary" >
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< tbody >
< tr class = "row-odd" > < td > < p > < a class = "reference internal" href = "_autosummary/mlx.nn.value_and_grad.html#mlx.nn.value_and_grad" title = "mlx.nn.value_and_grad" > < code class = "xref py py-obj docutils literal notranslate" > < span class = "pre" > value_and_grad< / span > < / code > < / a > (model, < / p > < / td >
< td > < p > Transform the passed function < code class = "docutils literal notranslate" > < span class = "pre" > fn< / span > < / code > to a function that computes the gradients of < code class = "docutils literal notranslate" > < span class = "pre" > fn< / span > < / code > wrt the model's trainable parameters and also its value.< / p > < / td >
< / tr >
< / tbody >
< / table >
< / section >
< section id = "neural-network-layers" >
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< h2 > Neural Network Layers< a class = "headerlink" href = "#neural-network-layers" title = "Permalink to this heading" > #< / a > < / h2 >
< table class = "autosummary longtable table autosummary" >
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< tbody >
< 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, < / p > < / td >
< td > < p > Implements a simple lookup table that maps each input integer to a high-dimensional vector.< / 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 >
< td > < p > Applies the Rectified Linear Unit.< / p > < / td >
< / tr >
< 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 >
< td > < p > Applies the Gaussian Error Linear Units.< / 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 >
< td > < p > Applies the Sigmoid Linear Unit.< / p > < / td >
< / tr >
< 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, < / p > < / td >
< td > < p > Applies an affine transformation to the input.< / p > < / td >
< / tr >
< 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, < / p > < / td >
< td > < p > Applies a 1-dimensional convolution over the multi-channel input sequence.< / p > < / td >
< / tr >
< 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, < / p > < / td >
< td > < p > Applies a 2-dimensional convolution over the multi-channel input image.< / p > < / td >
< / tr >
< 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[, < / p > < / td >
< td > < p > Applies layer normalization [1] on the inputs.< / 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[, < / 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.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, < / p > < / td >
< td > < p > Applies Group Normalization [1] to the inputs.< / 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[, < / p > < / td >
< td > < p > Implements the rotary positional encoding [1].< / p > < / td >
< / tr >
< 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, < / p > < / td >
< td > < p > Implements the scaled dot product attention with multiple heads.< / 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 >
< td > < p > A layer that calls the passed callables in order.< / p > < / td >
< / tr >
< / tbody >
< / table >
< p > Layers without parameters (e.g. activation functions) are also provided as
simple functions.< / p >
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< table class = "autosummary longtable table autosummary" >
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< tbody >
< tr class = "row-odd" > < td > < p > < a class = "reference internal" href = "_autosummary_functions/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 > (x)< / p > < / td >
< td > < p > Applies the Gaussian Error Linear Units function.< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > < a class = "reference internal" href = "_autosummary_functions/mlx.nn.gelu_approx.html#mlx.nn.gelu_approx" title = "mlx.nn.gelu_approx" > < code class = "xref py py-obj docutils literal notranslate" > < span class = "pre" > gelu_approx< / span > < / code > < / a > (x)< / p > < / td >
< td > < p > An approximation to Gaussian Error Linear Unit.< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > < a class = "reference internal" href = "_autosummary_functions/mlx.nn.gelu_fast_approx.html#mlx.nn.gelu_fast_approx" title = "mlx.nn.gelu_fast_approx" > < code class = "xref py py-obj docutils literal notranslate" > < span class = "pre" > gelu_fast_approx< / span > < / code > < / a > (x)< / p > < / td >
< td > < p > A fast approximation to Gaussian Error Linear Unit.< / p > < / td >
< / tr >
< tr class = "row-even" > < td > < p > < a class = "reference internal" href = "_autosummary_functions/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 > (x)< / p > < / td >
< td > < p > Applies the Rectified Linear Unit.< / p > < / td >
< / tr >
< tr class = "row-odd" > < td > < p > < a class = "reference internal" href = "_autosummary_functions/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 > (x)< / p > < / td >
< td > < p > Applies the Sigmoid Linear Unit.< / p > < / td >
< / tr >
< / tbody >
< / table >
< / section >
< / section >
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