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| <a href="https://github.com/apache/mxnet/tree/master/edit/master/docs/api/model.md" title="Edit this page" class="md-icon md-content__icon"></a> |
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| |
| <p><a id='Model-1'></a></p> |
| <h1 id="model">Model</h1> |
| <p>The model API provides convenient high-level interface to do training and predicting on a network described using the symbolic API.</p> |
| <p><a id='MXNet.mx.AbstractModel' href='#MXNet.mx.AbstractModel'>#</a> |
| <strong><code>MXNet.mx.AbstractModel</code></strong> — <em>Type</em>.</p> |
| <pre><code class="julia">AbstractModel |
| </code></pre> |
| |
| <p>The abstract super type of all models in MXNet.jl.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L18-L22' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.FeedForward' href='#MXNet.mx.FeedForward'>#</a> |
| <strong><code>MXNet.mx.FeedForward</code></strong> — <em>Type</em>.</p> |
| <pre><code class="julia">FeedForward |
| </code></pre> |
| |
| <p>The feedforward model provides convenient interface to train and predict on feedforward architectures like multi-layer MLP, ConvNets, etc. There is no explicitly handling of <em>time index</em>, but it is relatively easy to implement unrolled RNN / LSTM under this framework (<em>TODO</em>: add example). For models that handles sequential data explicitly, please use <em>TODO</em>...</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L25-L33' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.FeedForward-Tuple{SymbolicNode}' href='#MXNet.mx.FeedForward-Tuple{SymbolicNode}'>#</a> |
| <strong><code>MXNet.mx.FeedForward</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">FeedForward(arch :: SymbolicNode, ctx) |
| </code></pre> |
| |
| <p><strong>Arguments:</strong></p> |
| <ul> |
| <li><code>arch</code>: the architecture of the network constructed using the symbolic API.</li> |
| <li><code>ctx</code>: the devices on which this model should do computation. It could be a single <code>Context</code> or a list of <code>Context</code> objects. In the latter case, data parallelization will be used for training. If no context is provided, the default context <code>cpu()</code> will be used.</li> |
| </ul> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L65-L73' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.predict-Tuple{Function,FeedForward,AbstractDataProvider}' href='#MXNet.mx.predict-Tuple{Function,FeedForward,AbstractDataProvider}'>#</a> |
| <strong><code>MXNet.mx.predict</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">predict(self, data; overwrite=false, callback=nothing) |
| </code></pre> |
| |
| <p>Predict using an existing model. The model should be already initialized, or trained or loaded from a checkpoint. There is an overloaded function that allows to pass the callback as the first argument, so it is possible to do</p> |
| <pre><code class="julia">predict(model, data) do batch_output |
| # consume or write batch_output to file |
| end |
| </code></pre> |
| |
| <p><strong>Arguments:</strong></p> |
| <ul> |
| <li><code>self::FeedForward</code>: the model.</li> |
| <li><code>data::AbstractDataProvider</code>: the data to perform prediction on.</li> |
| <li><code>overwrite::Bool</code>: an <code>Executor</code> is initialized the first time predict is called. The memory allocation of the <code>Executor</code> depends on the mini-batch size of the test data provider. If you call predict twice with data provider of the same batch-size, then the executor can be potentially be re-used. So, if <code>overwrite</code> is false, we will try to re-use, and raise an error if batch-size changed. If <code>overwrite</code> is true (the default), a new <code>Executor</code> will be created to replace the old one.</li> |
| <li><code>verbosity::Integer</code>: Determines the verbosity of the print messages. Higher numbers leads to more verbose printing. Acceptable values are - <code>0</code>: Do not print anything during prediction - <code>1</code>: Print allocation information during prediction</li> |
| </ul> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Prediction is computationally much less costly than training, so the bottleneck sometimes becomes the IO for copying mini-batches of data. Since there is no concern about convergence in prediction, it is better to set the mini-batch size as large as possible (limited by your device memory) if prediction speed is a concern.</p> |
| <p>For the same reason, currently prediction will only use the first device even if multiple devices are provided to construct the model.</p> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>If you perform further after prediction. The weights are not automatically synchronized if <code>overwrite</code> is set to false and the old predictor is re-used. In this case setting <code>overwrite</code> to true (the default) will re-initialize the predictor the next time you call predict and synchronize the weights again.</p> |
| </div> |
| <p>See also <a href="./#MXNet.mx.train-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}"><code>train</code></a>, <a href="./#MXNet.mx.fit-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}"><code>fit</code></a>, <a href="./#MXNet.mx.init_model-Tuple{FeedForward,AbstractInitializer}"><code>init_model</code></a>, and <a href="./#MXNet.mx.load_checkpoint-Tuple{AbstractString,Int64,Type{FeedForward}}"><code>load_checkpoint</code></a></p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L178-L221' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx._split_inputs-Tuple{Int64,Int64}' href='#MXNet.mx._split_inputs-Tuple{Int64,Int64}'>#</a> |
| <strong><code>MXNet.mx._split_inputs</code></strong> — <em>Method</em>.</p> |
| <p>Get a split of <code>batch_size</code> into <code>n_split</code> pieces for data parallelization. Returns a vector of length <code>n_split</code>, with each entry a <code>UnitRange{Int}</code> indicating the slice index for that piece.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L48-L52' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.fit-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}' href='#MXNet.mx.fit-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}'>#</a> |
| <strong><code>MXNet.mx.fit</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">fit(model::FeedForward, optimizer, data; kwargs...) |
| </code></pre> |
| |
| <p>Train the <code>model</code> on <code>data</code> with the <code>optimizer</code>.</p> |
| <ul> |
| <li><code>model::FeedForward</code>: the model to be trained.</li> |
| <li><code>optimizer::AbstractOptimizer</code>: the optimization algorithm to use.</li> |
| <li><code>data::AbstractDataProvider</code>: the training data provider.</li> |
| <li><code>n_epoch::Int</code>: default 10, the number of full data-passes to run.</li> |
| <li><code>eval_data::AbstractDataProvider</code>: keyword argument, default <code>nothing</code>. The data provider for the validation set.</li> |
| <li><code>eval_metric::AbstractEvalMetric</code>: keyword argument, default <a href="../metric/#MXNet.mx.Accuracy"><code>Accuracy()</code></a>. The metric used to evaluate the training performance. If <code>eval_data</code> is provided, the same metric is also calculated on the validation set.</li> |
| <li><code>kvstore</code>: keyword argument, default <code>:local</code>. The key-value store used to synchronize gradients and parameters when multiple devices are used for training. :type kvstore: <code>KVStore</code> or <code>Symbol</code></li> |
| <li><code>initializer::AbstractInitializer</code>: keyword argument, default <code>UniformInitializer(0.01)</code>.</li> |
| <li><code>force_init::Bool</code>: keyword argument, default false. By default, the random initialization using the provided <code>initializer</code> will be skipped if the model weights already exists, maybe from a previous call to <a href="./#MXNet.mx.train-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}"><code>train</code></a> or an explicit call to <a href="./#MXNet.mx.init_model-Tuple{FeedForward,AbstractInitializer}"><code>init_model</code></a> or <a href="./#MXNet.mx.load_checkpoint-Tuple{AbstractString,Int64,Type{FeedForward}}"><code>load_checkpoint</code></a>. When this option is set, it will always do random initialization at the begining of training.</li> |
| <li><code>callbacks::Vector{AbstractCallback}</code>: keyword argument, default <code>[]</code>. Callbacks to be invoked at each epoch or mini-batch, see <code>AbstractCallback</code>.</li> |
| <li><code>verbosity::Int</code>: Determines the verbosity of the print messages. Higher numbers leads to more verbose printing. Acceptable values are - <code>0</code>: Do not print anything during training - <code>1</code>: Print starting and final messages - <code>2</code>: Print one time messages and a message at the start of each epoch - <code>3</code>: Print a summary of the training and validation accuracy for each epoch</li> |
| <li><code>η_decay::Symbol</code>: <code>:epoch</code> or <code>:batch</code>, decay learning rate on epoch or batch.</li> |
| </ul> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L331-L362' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.init_model-Tuple{FeedForward,AbstractInitializer}' href='#MXNet.mx.init_model-Tuple{FeedForward,AbstractInitializer}'>#</a> |
| <strong><code>MXNet.mx.init_model</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">init_model(self, initializer; overwrite=false, input_shapes...) |
| </code></pre> |
| |
| <p>Initialize the weights in the model.</p> |
| <p>This method will be called automatically when training a model. So there is usually no need to call this method unless one needs to inspect a model with only randomly initialized weights.</p> |
| <p><strong>Arguments:</strong></p> |
| <ul> |
| <li><code>self::FeedForward</code>: the model to be initialized.</li> |
| <li><code>initializer::AbstractInitializer</code>: an initializer describing how the weights should be initialized.</li> |
| <li><code>overwrite::Bool</code>: keyword argument, force initialization even when weights already exists.</li> |
| <li><code>input_shapes</code>: the shape of all data and label inputs to this model, given as keyword arguments. For example, <code>data=(28,28,1,100), label=(100,)</code>.</li> |
| </ul> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L77-L92' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.load_checkpoint-Tuple{AbstractString,Int64,Type{FeedForward}}' href='#MXNet.mx.load_checkpoint-Tuple{AbstractString,Int64,Type{FeedForward}}'>#</a> |
| <strong><code>MXNet.mx.load_checkpoint</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">load_checkpoint(prefix, epoch, ::mx.FeedForward; context) |
| </code></pre> |
| |
| <p>Load a mx.FeedForward model from the checkpoint <em>prefix</em>, <em>epoch</em> and optionally provide a context.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L645-L649' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.train-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}' href='#MXNet.mx.train-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}'>#</a> |
| <strong><code>MXNet.mx.train</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">train(model :: FeedForward, ...) |
| </code></pre> |
| |
| <p>Alias to <a href="./#MXNet.mx.fit-Tuple{FeedForward,AbstractOptimizer,AbstractDataProvider}"><code>fit</code></a>.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/model.jl#L323-L327' class='documenter-source'>source</a><br></p> |
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