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| <h3></h3></header> |
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| |
| <h1 id="module-api">Module API</h1> |
| <p>The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. Module wraps a Symbol and one or more Executors. It has both a high level and intermediate level API.</p> |
| |
| <p>Topics:</p> |
| |
| <ul> |
| <li><a href="#prepare-the-data">Prepare the Data</a></li> |
| <li><a href="#list-key-value-pairs">List Key-Value Pairs</a></li> |
| <li><a href="#preparing-a-module-for-computation">Preparing a Module for Computation</a></li> |
| <li><a href="#training-and-predicting">Training and Predicting</a></li> |
| <li><a href="#saving-and-loading">Saving and Loading</a></li> |
| <li><a href="/versions/master/api/clojure/docs/api">Clojure API Reference</a></li> |
| </ul> |
| |
| <p>To follow along with this documentation, you can use this namespace to with the needed requires:</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="nf">ns</span><span class="w"> </span><span class="n">docs.module</span><span class="w"> |
| </span><span class="p">(</span><span class="no">:require</span><span class="w"> </span><span class="p">[</span><span class="n">clojure.java.io</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">io</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">clojure.java.shell</span><span class="w"> </span><span class="no">:refer</span><span class="w"> </span><span class="p">[</span><span class="n">sh</span><span class="p">]]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.eval-metric</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">eval-metric</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.io</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">mx-io</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.module</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">m</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.symbol</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">sym</span><span class="p">]</span><span class="w"> |
| </span><span class="p">[</span><span class="n">org.apache.clojure-mxnet.ndarray</span><span class="w"> </span><span class="no">:as</span><span class="w"> </span><span class="n">ndarray</span><span class="p">]))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <h2 id="prepare-the-data">Prepare the Data</h2> |
| |
| <p>In this example, we are going to use the MNIST data set. If you have cloned the MXNet repo and <code class="highlighter-rouge">cd contrib/clojure-package</code>, we can run some helper scripts to download the data for us.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">data-dir</span><span class="w"> </span><span class="s">"data/"</span><span class="p">)</span><span class="w"> |
| |
| </span><span class="p">(</span><span class="nb">when-not</span><span class="w"> </span><span class="p">(</span><span class="nf">.exists</span><span class="w"> </span><span class="p">(</span><span class="nf">io/file</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="n">data-dir</span><span class="w"> </span><span class="s">"train-images-idx3-ubyte"</span><span class="p">)))</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sh</span><span class="w"> </span><span class="s">"../../scripts/get_mnist_data.sh"</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>MXNet provides function in the <code class="highlighter-rouge">io</code> namespace to load the MNIST datasets into training and test data iterators that we can use with our module.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">train-data</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/mnist-iter</span><span class="w"> </span><span class="p">{</span><span class="no">:image</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="n">data-dir</span><span class="w"> </span><span class="s">"train-images-idx3-ubyte"</span><span class="p">)</span><span class="w"> |
| </span><span class="no">:label</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="n">data-dir</span><span class="w"> </span><span class="s">"train-labels-idx1-ubyte"</span><span class="p">)</span><span class="w"> |
| </span><span class="no">:label-name</span><span class="w"> </span><span class="s">"softmax_label"</span><span class="w"> |
| </span><span class="no">:input-shape</span><span class="w"> </span><span class="p">[</span><span class="mi">784</span><span class="p">]</span><span class="w"> |
| </span><span class="no">:batch-size</span><span class="w"> </span><span class="mi">10</span><span class="w"> |
| </span><span class="no">:shuffle</span><span class="w"> </span><span class="n">true</span><span class="w"> |
| </span><span class="no">:flat</span><span class="w"> </span><span class="n">true</span><span class="w"> |
| </span><span class="no">:silent</span><span class="w"> </span><span class="n">false</span><span class="w"> |
| </span><span class="no">:seed</span><span class="w"> </span><span class="mi">10</span><span class="p">}))</span><span class="w"> |
| |
| </span><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">test-data</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/mnist-iter</span><span class="w"> </span><span class="p">{</span><span class="no">:image</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="n">data-dir</span><span class="w"> </span><span class="s">"t10k-images-idx3-ubyte"</span><span class="p">)</span><span class="w"> |
| </span><span class="no">:label</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="n">data-dir</span><span class="w"> </span><span class="s">"t10k-labels-idx1-ubyte"</span><span class="p">)</span><span class="w"> |
| </span><span class="no">:input-shape</span><span class="w"> </span><span class="p">[</span><span class="mi">784</span><span class="p">]</span><span class="w"> |
| </span><span class="no">:batch-size</span><span class="w"> </span><span class="mi">10</span><span class="w"> |
| </span><span class="no">:flat</span><span class="w"> </span><span class="n">true</span><span class="w"> |
| </span><span class="no">:silent</span><span class="w"> </span><span class="n">false</span><span class="p">}))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <h2 id="preparing-a-module-for-computation">Preparing a Module for Computation</h2> |
| |
| <p>To construct a module, we need to have a symbol as input. This symbol takes input data in the first layer and then has subsequent layers of fully connected and relu activation layers, ending up in a softmax layer for output.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">let</span><span class="w"> </span><span class="p">[</span><span class="n">data</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data"</span><span class="p">)</span><span class="w"> |
| </span><span class="n">fc1</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">128</span><span class="p">})</span><span class="w"> |
| </span><span class="n">act1</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="s">"relu1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">fc1</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">})</span><span class="w"> |
| </span><span class="n">fc2</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc2"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">act1</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">64</span><span class="p">})</span><span class="w"> |
| </span><span class="n">act2</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="s">"relu2"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">fc2</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">})</span><span class="w"> |
| </span><span class="n">fc3</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc3"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">act2</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">10</span><span class="p">})</span><span class="w"> |
| </span><span class="n">out</span><span class="w"> </span><span class="p">(</span><span class="nf">sym/softmax-output</span><span class="w"> </span><span class="s">"softmax"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">fc3</span><span class="p">})]</span><span class="w"> |
| </span><span class="n">out</span><span class="p">)</span><span class="w"> |
| </span><span class="c1">;=>#object[org.apache.mxnet.Symbol 0x1f43a406 "org.apache.mxnet.Symbol@1f43a406"]</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>You can also write this with the <code class="highlighter-rouge">as-></code> threading macro.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="p">(</span><span class="nf">as-></span><span class="w"> </span><span class="p">(</span><span class="nf">sym/variable</span><span class="w"> </span><span class="s">"data"</span><span class="p">)</span><span class="w"> </span><span class="n">data</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">128</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="s">"relu1"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc2"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">64</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/activation</span><span class="w"> </span><span class="s">"relu2"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:act-type</span><span class="w"> </span><span class="s">"relu"</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/fully-connected</span><span class="w"> </span><span class="s">"fc3"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="no">:num-hidden</span><span class="w"> </span><span class="mi">10</span><span class="p">})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">sym/softmax-output</span><span class="w"> </span><span class="s">"softmax"</span><span class="w"> </span><span class="p">{</span><span class="no">:data</span><span class="w"> </span><span class="n">data</span><span class="p">})))</span><span class="w"> |
| </span><span class="c1">;=> #'tutorial.module/out</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>By default, <code class="highlighter-rouge">context</code> is the CPU. If you need data parallelization, you can specify a GPU context or an array of GPU contexts like this <code class="highlighter-rouge">(m/module out {:contexts [(context/gpu)]})</code></p> |
| |
| <p>Before you can compute with a module, you need to call <code class="highlighter-rouge">bind</code> to allocate the device memory and <code class="highlighter-rouge">init-params</code> or <code class="highlighter-rouge">set-params</code> to initialize the parameters. If you simply want to fit a module, you don’t need to call <code class="highlighter-rouge">bind</code> and <code class="highlighter-rouge">init-params</code> explicitly, because the <code class="highlighter-rouge">fit</code> function automatically calls them if they are needed.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">let</span><span class="w"> </span><span class="p">[</span><span class="n">mod</span><span class="w"> </span><span class="p">(</span><span class="nf">m/module</span><span class="w"> </span><span class="n">out</span><span class="p">)]</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">-></span><span class="w"> </span><span class="n">mod</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">m/bind</span><span class="w"> </span><span class="p">{</span><span class="no">:data-shapes</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/provide-data</span><span class="w"> </span><span class="n">train-data</span><span class="p">)</span><span class="w"> |
| </span><span class="no">:label-shapes</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/provide-label</span><span class="w"> </span><span class="n">train-data</span><span class="p">)})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">m/init-params</span><span class="p">)))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>Now you can compute with the module using functions like <code class="highlighter-rouge">forward</code>, <code class="highlighter-rouge">backward</code>, etc.</p> |
| |
| <h2 id="training-and-predicting">Training and Predicting</h2> |
| |
| <p>Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the <code class="highlighter-rouge">fit</code> function with some data iterators:</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">mod</span><span class="w"> </span><span class="p">(</span><span class="nf">m/fit</span><span class="w"> </span><span class="p">(</span><span class="nf">m/module</span><span class="w"> </span><span class="n">out</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="no">:train-data</span><span class="w"> </span><span class="n">train-data</span><span class="w"> </span><span class="no">:eval-data</span><span class="w"> </span><span class="n">test-data</span><span class="w"> </span><span class="no">:num-epoch</span><span class="w"> </span><span class="mi">1</span><span class="p">}))</span><span class="w"> |
| </span><span class="c1">;; Epoch 0 Train- [accuracy 0.12521666]</span><span class="w"> |
| </span><span class="c1">;; Epoch 0 Time cost- 8392</span><span class="w"> |
| </span><span class="c1">;; Epoch 0 Validation- [accuracy 0.2227]</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>You can pass in batch-end callbacks using batch-end-callback and epoch-end callbacks using epoch-end-callback in the <code class="highlighter-rouge">fit-params</code>. You can also set parameters using functions like in the fit-params like optimizer and eval-metric. To learn more about the fit-params, see the fit-param function options. To predict with a module, call <code class="highlighter-rouge">predict</code> with a DataIter:</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">results</span><span class="w"> </span><span class="p">(</span><span class="nf">m/predict</span><span class="w"> </span><span class="n">mod</span><span class="w"> </span><span class="p">{</span><span class="no">:eval-data</span><span class="w"> </span><span class="n">test-data</span><span class="p">}))</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">first</span><span class="w"> </span><span class="n">results</span><span class="p">)</span><span class="w"> </span><span class="c1">;=>#object[org.apache.mxnet.NDArray 0x3540b6d3 "org.apache.mxnet.NDArray@a48686ec"]</span><span class="w"> |
| |
| </span><span class="p">(</span><span class="nb">first</span><span class="w"> </span><span class="p">(</span><span class="nf">ndarray/->vec</span><span class="w"> </span><span class="p">(</span><span class="nb">first</span><span class="w"> </span><span class="n">results</span><span class="p">)))</span><span class="w"> </span><span class="c1">;=>0.08261358</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the <a href="/versions/master/api/clojure/docs/api/org.apache.clojure-mxnet.module.html#var-predict"><code class="highlighter-rouge">predict</code></a> function.</p> |
| |
| <p>When prediction results might be too large to fit in memory, use the <a href="/versions/master/api/clojure/docs/api/org.apache.clojure-mxnet.module.html#var-predict-every-batch"><code class="highlighter-rouge">predict-every-batch</code></a> API.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">let</span><span class="w"> </span><span class="p">[</span><span class="n">preds</span><span class="w"> </span><span class="p">(</span><span class="nf">m/predict-every-batch</span><span class="w"> </span><span class="n">mod</span><span class="w"> </span><span class="p">{</span><span class="no">:eval-data</span><span class="w"> </span><span class="n">test-data</span><span class="p">})]</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">mx-io/reduce-batches</span><span class="w"> </span><span class="n">test-data</span><span class="w"> |
| </span><span class="p">(</span><span class="k">fn</span><span class="w"> </span><span class="p">[</span><span class="n">i</span><span class="w"> </span><span class="n">batch</span><span class="p">]</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">println</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="s">"pred is "</span><span class="w"> </span><span class="p">(</span><span class="nb">first</span><span class="w"> </span><span class="p">(</span><span class="nb">get</span><span class="w"> </span><span class="n">preds</span><span class="w"> </span><span class="n">i</span><span class="p">))))</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">println</span><span class="w"> </span><span class="p">(</span><span class="nb">str</span><span class="w"> </span><span class="s">"label is "</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/batch-label</span><span class="w"> </span><span class="n">batch</span><span class="p">)))</span><span class="w"> |
| </span><span class="c1">;;; do something</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">inc</span><span class="w"> </span><span class="n">i</span><span class="p">))))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>If you need to evaluate on a test set and don’t need the prediction output, call the <code class="highlighter-rouge">score</code> function with a data iterator and an eval metric:</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="nf">m/score</span><span class="w"> </span><span class="n">mod</span><span class="w"> </span><span class="p">{</span><span class="no">:eval-data</span><span class="w"> </span><span class="n">test-data</span><span class="w"> </span><span class="no">:eval-metric</span><span class="w"> </span><span class="p">(</span><span class="nf">eval-metric/accuracy</span><span class="p">)})</span><span class="w"> </span><span class="c1">;=>["accuracy" 0.2227]</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>This runs predictions on each batch in the provided data iterator and computes the evaluation score using the provided eval metric. The evaluation results are stored in <code class="highlighter-rouge">eval-metric</code> object itself so that you can query later.</p> |
| |
| <h2 id="saving-and-loading">Saving and Loading</h2> |
| |
| <p>To save the module parameters in each training epoch, use the <code class="highlighter-rouge">save-checkpoint</code> function:</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">let</span><span class="w"> </span><span class="p">[</span><span class="n">save-prefix</span><span class="w"> </span><span class="s">"my-model"</span><span class="p">]</span><span class="w"> |
| </span><span class="p">(</span><span class="nb">doseq</span><span class="w"> </span><span class="p">[</span><span class="n">epoch-num</span><span class="w"> </span><span class="p">(</span><span class="nb">range</span><span class="w"> </span><span class="mi">3</span><span class="p">)]</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">mx-io/do-batches</span><span class="w"> </span><span class="n">train-data</span><span class="w"> </span><span class="p">(</span><span class="k">fn</span><span class="w"> </span><span class="p">[</span><span class="n">batch</span><span class="w"> |
| </span><span class="c1">;; do something</span><span class="w"> |
| </span><span class="p">]))</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">m/save-checkpoint</span><span class="w"> </span><span class="n">mod</span><span class="w"> </span><span class="p">{</span><span class="no">:prefix</span><span class="w"> </span><span class="n">save-prefix</span><span class="w"> </span><span class="no">:epoch</span><span class="w"> </span><span class="n">epoch-num</span><span class="w"> </span><span class="no">:save-opt-states</span><span class="w"> </span><span class="n">true</span><span class="p">})))</span><span class="w"> |
| |
| </span><span class="c1">;; INFO org.apache.mxnet.module.Module: Saved checkpoint to my-model-0000.params</span><span class="w"> |
| </span><span class="c1">;; INFO org.apache.mxnet.module.Module: Saved optimizer state to my-model-0000.states</span><span class="w"> |
| </span><span class="c1">;; INFO org.apache.mxnet.module.Module: Saved checkpoint to my-model-0001.params</span><span class="w"> |
| </span><span class="c1">;; INFO org.apache.mxnet.module.Module: Saved optimizer state to my-model-0001.states</span><span class="w"> |
| </span><span class="c1">;; INFO org.apache.mxnet.module.Module: Saved checkpoint to my-model-0002.params</span><span class="w"> |
| </span><span class="c1">;; INFO org.apache.mxnet.module.Module: Saved optimizer state to my-model-0002.states</span><span class="w"> |
| |
| </span></code></pre></div></div> |
| |
| <p>To load the saved module parameters, call the <code class="highlighter-rouge">load-checkpoint</code> function:</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="k">def</span><span class="w"> </span><span class="n">new-mod</span><span class="w"> </span><span class="p">(</span><span class="nf">m/load-checkpoint</span><span class="w"> </span><span class="p">{</span><span class="no">:prefix</span><span class="w"> </span><span class="s">"my-model"</span><span class="w"> </span><span class="no">:epoch</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="no">:load-optimizer-states</span><span class="w"> </span><span class="n">true</span><span class="p">}))</span><span class="w"> |
| |
| </span><span class="n">new-mod</span><span class="w"> </span><span class="c1">;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>To initialize parameters, Bind the symbols to construct executors first with <code class="highlighter-rouge">bind</code> function. Then, initialize the parameters and auxiliary states by calling <code class="highlighter-rouge">init-params</code> function.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="nb">-></span><span class="w"> </span><span class="n">new-mod</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">m/bind</span><span class="w"> </span><span class="p">{</span><span class="no">:data-shapes</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/provide-data</span><span class="w"> </span><span class="n">train-data</span><span class="p">)</span><span class="w"> </span><span class="no">:label-shapes</span><span class="w"> </span><span class="p">(</span><span class="nf">mx-io/provide-label</span><span class="w"> </span><span class="n">train-data</span><span class="p">)})</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">m/init-params</span><span class="p">))</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>To get current parameters, use <code class="highlighter-rouge">params</code></p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="w"> |
| </span><span class="p">(</span><span class="k">let</span><span class="w"> </span><span class="p">[[</span><span class="n">arg-params</span><span class="w"> </span><span class="n">aux-params</span><span class="p">]</span><span class="w"> </span><span class="p">(</span><span class="nf">m/params</span><span class="w"> </span><span class="n">new-mod</span><span class="p">)]</span><span class="w"> |
| </span><span class="p">{</span><span class="no">:arg-params</span><span class="w"> </span><span class="n">arg-params</span><span class="w"> |
| </span><span class="no">:aux-params</span><span class="w"> </span><span class="n">aux-params</span><span class="p">})</span><span class="w"> |
| |
| </span><span class="c1">;; {:arg-params</span><span class="w"> |
| </span><span class="c1">;; {"fc3_bias"</span><span class="w"> |
| </span><span class="c1">;; #object[org.apache.mxnet.NDArray 0x39adc3b0 "org.apache.mxnet.NDArray@49caf426"],</span><span class="w"> |
| </span><span class="c1">;; "fc2_weight"</span><span class="w"> |
| </span><span class="c1">;; #object[org.apache.mxnet.NDArray 0x25baf623 "org.apache.mxnet.NDArray@a6c8f9ac"],</span><span class="w"> |
| </span><span class="c1">;; "fc1_bias"</span><span class="w"> |
| </span><span class="c1">;; #object[org.apache.mxnet.NDArray 0x6e089973 "org.apache.mxnet.NDArray@9f91d6eb"],</span><span class="w"> |
| </span><span class="c1">;; "fc3_weight"</span><span class="w"> |
| </span><span class="c1">;; #object[org.apache.mxnet.NDArray 0x756fd109 "org.apache.mxnet.NDArray@2dd0fe3c"],</span><span class="w"> |
| </span><span class="c1">;; "fc2_bias"</span><span class="w"> |
| </span><span class="c1">;; #object[org.apache.mxnet.NDArray 0x1dc69c8b "org.apache.mxnet.NDArray@d128f73d"],</span><span class="w"> |
| </span><span class="c1">;; "fc1_weight"</span><span class="w"> |
| </span><span class="c1">;; #object[org.apache.mxnet.NDArray 0x20abc769 "org.apache.mxnet.NDArray@b8e1c5e8"]},</span><span class="w"> |
| </span><span class="c1">;; :aux-params {}}</span><span class="w"> |
| |
| </span></code></pre></div></div> |
| |
| <p>To assign parameter and aux state values, use the <code class="highlighter-rouge">set-params</code> function.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="p">(</span><span class="nf">m/set-params</span><span class="w"> </span><span class="n">new-mod</span><span class="w"> </span><span class="p">{</span><span class="no">:arg-params</span><span class="w"> </span><span class="p">(</span><span class="nf">m/arg-params</span><span class="w"> </span><span class="n">new-mod</span><span class="p">)</span><span class="w"> </span><span class="no">:aux-params</span><span class="w"> </span><span class="p">(</span><span class="nf">m/aux-params</span><span class="w"> </span><span class="n">new-mod</span><span class="p">)})</span><span class="w"> |
| </span><span class="c1">;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]</span><span class="w"> |
| </span></code></pre></div></div> |
| |
| <p>To resume training from a saved checkpoint, pass the loaded parameters to the <code class="highlighter-rouge">fit</code> function. This will prevent <code class="highlighter-rouge">fit</code> from initialzing randomly.</p> |
| |
| <p>Create fit-params and then use it to set <code class="highlighter-rouge">begin-epoch</code> so that <code class="highlighter-rouge">fit</code> knows to resume from a saved epoch.</p> |
| |
| <div class="language-clojure highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="c1">;; reset the training data before calling fit or you will get an error</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">mx-io/reset</span><span class="w"> </span><span class="n">train-data</span><span class="p">)</span><span class="w"> |
| </span><span class="p">(</span><span class="nf">mx-io/reset</span><span class="w"> </span><span class="n">test-data</span><span class="p">)</span><span class="w"> |
| |
| </span><span class="p">(</span><span class="nf">m/fit</span><span class="w"> </span><span class="n">new-mod</span><span class="w"> </span><span class="p">{</span><span class="no">:train-data</span><span class="w"> </span><span class="n">train-data</span><span class="w"> </span><span class="no">:eval-data</span><span class="w"> </span><span class="n">test-data</span><span class="w"> </span><span class="no">:num-epoch</span><span class="w"> </span><span class="mi">2</span><span class="w"> |
| </span><span class="no">:fit-params</span><span class="w"> </span><span class="p">(</span><span class="nb">-></span><span class="w"> </span><span class="p">(</span><span class="nf">m/fit-params</span><span class="w"> </span><span class="p">{</span><span class="no">:begin-epoch</span><span class="w"> </span><span class="mi">1</span><span class="p">}))})</span><span class="w"> |
| |
| </span></code></pre></div></div> |
| |
| <h2 id="next-steps">Next Steps</h2> |
| <ul> |
| <li>See <a href="symbol">Symbolic API</a> for operations on NDArrays that assemble neural networks from layers.</li> |
| <li>See <a href="ndarray">NDArray API</a> for vector/matrix/tensor operations.</li> |
| <li>See <a href="kvstore">KVStore API</a> for multi-GPU and multi-host distributed training.</li> |
| </ul> |
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