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<!--- under the License. --><div class="section" id="module-api">
<span id="module-api"></span><h1>Module API<a class="headerlink" href="#module-api" title="Permalink to this headline"></a></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 class="simple">
<li><a class="reference external" href="#prepare-the-data">Prepare the Data</a></li>
<li><a class="reference external" href="#list-key-value-pairs">List Key-Value Pairs</a></li>
<li><a class="reference external" href="#preparing-a-module-for-computation">Preparing a Module for Computation</a></li>
<li><a class="reference external" href="#training-and-predicting">Training and Predicting</a></li>
<li><a class="reference external" href="#saving-and-loading">Saving and Loading</a></li>
<li><a class="reference external" href="/versions/1.4.1/api/clojure/docs/org.apache.clojure-mxnet.module.html">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="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="kd">ns </span><span class="nv">docs.module</span>
<span class="p">(</span><span class="ss">:require</span> <span class="p">[</span><span class="nv">clojure.java.io</span> <span class="ss">:as</span> <span class="nv">io</span><span class="p">]</span>
<span class="p">[</span><span class="nv">clojure.java.shell</span> <span class="ss">:refer</span> <span class="p">[</span><span class="nv">sh</span><span class="p">]]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.eval-metric</span> <span class="ss">:as</span> <span class="nv">eval-metric</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.io</span> <span class="ss">:as</span> <span class="nv">mx-io</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.module</span> <span class="ss">:as</span> <span class="nv">m</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.symbol</span> <span class="ss">:as</span> <span class="nv">sym</span><span class="p">]</span>
<span class="p">[</span><span class="nv">org.apache.clojure-mxnet.ndarray</span> <span class="ss">:as</span> <span class="nv">ndarray</span><span class="p">]))</span>
</pre></div>
</div>
<div class="section" id="prepare-the-data">
<span id="prepare-the-data"></span><h2>Prepare the Data<a class="headerlink" href="#prepare-the-data" title="Permalink to this headline"></a></h2>
<p>In this example, we are going to use the MNIST data set. If you have cloned the MXNet repo and <code class="docutils literal"><span class="pre">cd</span> <span class="pre">contrib/clojure-package</span></code>, we can run some helper scripts to download the data for us.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">data-dir</span> <span class="s">"data/"</span><span class="p">)</span>
<span class="p">(</span><span class="nb">when-not </span><span class="p">(</span><span class="nf">.exists</span> <span class="p">(</span><span class="nf">io/file</span> <span class="p">(</span><span class="nb">str </span><span class="nv">data-dir</span> <span class="s">"train-images-idx3-ubyte"</span><span class="p">)))</span>
<span class="p">(</span><span class="nf">sh</span> <span class="s">"../../scripts/get_mnist_data.sh"</span><span class="p">))</span>
</pre></div>
</div>
<p>MXNet provides function in the <code class="docutils literal"><span class="pre">io</span></code> namespace to load the MNIST datasets into training and test data iterators that we can use with our module.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">train-data</span> <span class="p">(</span><span class="nf">mx-io/mnist-iter</span> <span class="p">{</span><span class="ss">:image</span> <span class="p">(</span><span class="nb">str </span><span class="nv">data-dir</span> <span class="s">"train-images-idx3-ubyte"</span><span class="p">)</span>
<span class="ss">:label</span> <span class="p">(</span><span class="nb">str </span><span class="nv">data-dir</span> <span class="s">"train-labels-idx1-ubyte"</span><span class="p">)</span>
<span class="ss">:label-name</span> <span class="s">"softmax_label"</span>
<span class="ss">:input-shape</span> <span class="p">[</span><span class="mi">784</span><span class="p">]</span>
<span class="ss">:batch-size</span> <span class="mi">10</span>
<span class="ss">:shuffle</span> <span class="nv">true</span>
<span class="ss">:flat</span> <span class="nv">true</span>
<span class="ss">:silent</span> <span class="nv">false</span>
<span class="ss">:seed</span> <span class="mi">10</span><span class="p">}))</span>
<span class="p">(</span><span class="k">def </span><span class="nv">test-data</span> <span class="p">(</span><span class="nf">mx-io/mnist-iter</span> <span class="p">{</span><span class="ss">:image</span> <span class="p">(</span><span class="nb">str </span><span class="nv">data-dir</span> <span class="s">"t10k-images-idx3-ubyte"</span><span class="p">)</span>
<span class="ss">:label</span> <span class="p">(</span><span class="nb">str </span><span class="nv">data-dir</span> <span class="s">"t10k-labels-idx1-ubyte"</span><span class="p">)</span>
<span class="ss">:input-shape</span> <span class="p">[</span><span class="mi">784</span><span class="p">]</span>
<span class="ss">:batch-size</span> <span class="mi">10</span>
<span class="ss">:flat</span> <span class="nv">true</span>
<span class="ss">:silent</span> <span class="nv">false</span><span class="p">}))</span>
</pre></div>
</div>
</div>
<div class="section" id="preparing-a-module-for-computation">
<span id="preparing-a-module-for-computation"></span><h2>Preparing a Module for Computation<a class="headerlink" href="#preparing-a-module-for-computation" title="Permalink to this headline"></a></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="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">let </span><span class="p">[</span><span class="nv">data</span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data"</span><span class="p">)</span>
<span class="nv">fc1</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">128</span><span class="p">})</span>
<span class="nv">act1</span> <span class="p">(</span><span class="nf">sym/activation</span> <span class="s">"relu1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">fc1</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">})</span>
<span class="nv">fc2</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc2"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">act1</span> <span class="ss">:num-hidden</span> <span class="mi">64</span><span class="p">})</span>
<span class="nv">act2</span> <span class="p">(</span><span class="nf">sym/activation</span> <span class="s">"relu2"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">fc2</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">})</span>
<span class="nv">fc3</span> <span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc3"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">act2</span> <span class="ss">:num-hidden</span> <span class="mi">10</span><span class="p">})</span>
<span class="nv">out</span> <span class="p">(</span><span class="nf">sym/softmax-output</span> <span class="s">"softmax"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">fc3</span><span class="p">})]</span>
<span class="nv">out</span><span class="p">)</span>
<span class="c1">;=>#object[org.apache.mxnet.Symbol 0x1f43a406 "org.apache.mxnet.Symbol@1f43a406"]</span>
</pre></div>
</div>
<p>You can also write this with the <code class="docutils literal"><span class="pre">as-></span></code> threading macro.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">out</span> <span class="p">(</span><span class="nf">as-></span> <span class="p">(</span><span class="nf">sym/variable</span> <span class="s">"data"</span><span class="p">)</span> <span class="nv">data</span>
<span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">128</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/activation</span> <span class="s">"relu1"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc2"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">64</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/activation</span> <span class="s">"relu2"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:act-type</span> <span class="s">"relu"</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/fully-connected</span> <span class="s">"fc3"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span> <span class="ss">:num-hidden</span> <span class="mi">10</span><span class="p">})</span>
<span class="p">(</span><span class="nf">sym/softmax-output</span> <span class="s">"softmax"</span> <span class="p">{</span><span class="ss">:data</span> <span class="nv">data</span><span class="p">})))</span>
<span class="c1">;=> #'tutorial.module/out</span>
</pre></div>
</div>
<p>By default, <code class="docutils literal"><span class="pre">context</span></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="docutils literal"><span class="pre">(m/module</span> <span class="pre">out</span> <span class="pre">{:contexts</span> <span class="pre">[(context/gpu)]})</span></code></p>
<p>Before you can compute with a module, you need to call <code class="docutils literal"><span class="pre">bind</span></code> to allocate the device memory and <code class="docutils literal"><span class="pre">init-params</span></code> or <code class="docutils literal"><span class="pre">set-params</span></code> to initialize the parameters. If you simply want to fit a module, you don’t need to call <code class="docutils literal"><span class="pre">bind</span></code> and <code class="docutils literal"><span class="pre">init-params</span></code> explicitly, because the <code class="docutils literal"><span class="pre">fit</span></code> function automatically calls them if they are needed.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">let </span><span class="p">[</span><span class="nv">mod</span> <span class="p">(</span><span class="nf">m/module</span> <span class="nv">out</span><span class="p">)]</span>
<span class="p">(</span><span class="nb">-> </span><span class="nv">mod</span>
<span class="p">(</span><span class="nf">m/bind</span> <span class="p">{</span><span class="ss">:data-shapes</span> <span class="p">(</span><span class="nf">mx-io/provide-data</span> <span class="nv">train-data</span><span class="p">)</span>
<span class="ss">:label-shapes</span> <span class="p">(</span><span class="nf">mx-io/provide-label</span> <span class="nv">train-data</span><span class="p">)})</span>
<span class="p">(</span><span class="nf">m/init-params</span><span class="p">)))</span>
</pre></div>
</div>
<p>Now you can compute with the module using functions like <code class="docutils literal"><span class="pre">forward</span></code>, <code class="docutils literal"><span class="pre">backward</span></code>, etc.</p>
</div>
<div class="section" id="training-and-predicting">
<span id="training-and-predicting"></span><h2>Training and Predicting<a class="headerlink" href="#training-and-predicting" title="Permalink to this headline"></a></h2>
<p>Modules provide high-level APIs for training, predicting, and evaluating. To fit a module, call the <code class="docutils literal"><span class="pre">fit</span></code> function with some data iterators:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">mod</span> <span class="p">(</span><span class="nf">m/fit</span> <span class="p">(</span><span class="nf">m/module</span> <span class="nv">out</span><span class="p">)</span> <span class="p">{</span><span class="ss">:train-data</span> <span class="nv">train-data</span> <span class="ss">:eval-data</span> <span class="nv">test-data</span> <span class="ss">:num-epoch</span> <span class="mi">1</span><span class="p">}))</span>
<span class="c1">;; Epoch 0 Train- [accuracy 0.12521666]</span>
<span class="c1">;; Epoch 0 Time cost- 8392</span>
<span class="c1">;; Epoch 0 Validation- [accuracy 0.2227]</span>
</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="docutils literal"><span class="pre">fit-params</span></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="docutils literal"><span class="pre">predict</span></code> with a DataIter:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">results</span> <span class="p">(</span><span class="nf">m/predict</span> <span class="nv">mod</span> <span class="p">{</span><span class="ss">:eval-data</span> <span class="nv">test-data</span><span class="p">}))</span>
<span class="p">(</span><span class="nb">first </span><span class="nv">results</span><span class="p">)</span> <span class="c1">;=>#object[org.apache.mxnet.NDArray 0x3540b6d3 "org.apache.mxnet.NDArray@a48686ec"]</span>
<span class="p">(</span><span class="nb">first </span><span class="p">(</span><span class="nf">ndarray/->vec</span> <span class="p">(</span><span class="nb">first </span><span class="nv">results</span><span class="p">)))</span> <span class="c1">;=>0.08261358</span>
</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 class="reference external" href="docs/org.apache.clojure-mxnet.module.html#var-fit-params"><code class="docutils literal"><span class="pre">predict</span></code></a> function.</p>
<p>When prediction results might be too large to fit in memory, use the <a class="reference external" href="docs/org.apache.clojure-mxnet.module.html#predict-every-batch"><code class="docutils literal"><span class="pre">predict-every-batch</span></code></a> API.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">let </span><span class="p">[</span><span class="nv">preds</span> <span class="p">(</span><span class="nf">m/predict-every-batch</span> <span class="nv">mod</span> <span class="p">{</span><span class="ss">:eval-data</span> <span class="nv">test-data</span><span class="p">})]</span>
<span class="p">(</span><span class="nf">mx-io/reduce-batches</span> <span class="nv">test-data</span>
<span class="p">(</span><span class="k">fn </span><span class="p">[</span><span class="nv">i</span> <span class="nv">batch</span><span class="p">]</span>
<span class="p">(</span><span class="nb">println </span><span class="p">(</span><span class="nb">str </span><span class="s">"pred is "</span> <span class="p">(</span><span class="nb">first </span><span class="p">(</span><span class="nb">get </span><span class="nv">preds</span> <span class="nv">i</span><span class="p">))))</span>
<span class="p">(</span><span class="nb">println </span><span class="p">(</span><span class="nb">str </span><span class="s">"label is "</span> <span class="p">(</span><span class="nf">mx-io/batch-label</span> <span class="nv">batch</span><span class="p">)))</span>
<span class="c1">;;; do something</span>
<span class="p">(</span><span class="nb">inc </span><span class="nv">i</span><span class="p">))))</span>
</pre></div>
</div>
<p>If you need to evaluate on a test set and don’t need the prediction output, call the <code class="docutils literal"><span class="pre">score</span></code> function with a data iterator and an eval metric:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="nf">m/score</span> <span class="nv">mod</span> <span class="p">{</span><span class="ss">:eval-data</span> <span class="nv">test-data</span> <span class="ss">:eval-metric</span> <span class="p">(</span><span class="nf">eval-metric/accuracy</span><span class="p">)})</span> <span class="c1">;=>["accuracy" 0.2227]</span>
</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="docutils literal"><span class="pre">eval-metric</span></code> object itself so that you can query later.</p>
</div>
<div class="section" id="saving-and-loading">
<span id="saving-and-loading"></span><h2>Saving and Loading<a class="headerlink" href="#saving-and-loading" title="Permalink to this headline"></a></h2>
<p>To save the module parameters in each training epoch, use the <code class="docutils literal"><span class="pre">save-checkpoint</span></code> function:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">let </span><span class="p">[</span><span class="nv">save-prefix</span> <span class="s">"my-model"</span><span class="p">]</span>
<span class="p">(</span><span class="nb">doseq </span><span class="p">[</span><span class="nv">epoch-num</span> <span class="p">(</span><span class="nb">range </span><span class="mi">3</span><span class="p">)]</span>
<span class="p">(</span><span class="nf">mx-io/do-batches</span> <span class="nv">train-data</span> <span class="p">(</span><span class="k">fn </span><span class="p">[</span><span class="nv">batch</span>
<span class="c1">;; do something</span>
<span class="p">]))</span>
<span class="p">(</span><span class="nf">m/save-checkpoint</span> <span class="nv">mod</span> <span class="p">{</span><span class="ss">:prefix</span> <span class="nv">save-prefix</span> <span class="ss">:epoch</span> <span class="nv">epoch-num</span> <span class="ss">:save-opt-states</span> <span class="nv">true</span><span class="p">})))</span>
<span class="c1">;; INFO org.apache.mxnet.module.Module: Saved checkpoint to my-model-0000.params</span>
<span class="c1">;; INFO org.apache.mxnet.module.Module: Saved optimizer state to my-model-0000.states</span>
<span class="c1">;; INFO org.apache.mxnet.module.Module: Saved checkpoint to my-model-0001.params</span>
<span class="c1">;; INFO org.apache.mxnet.module.Module: Saved optimizer state to my-model-0001.states</span>
<span class="c1">;; INFO org.apache.mxnet.module.Module: Saved checkpoint to my-model-0002.params</span>
<span class="c1">;; INFO org.apache.mxnet.module.Module: Saved optimizer state to my-model-0002.states</span>
</pre></div>
</div>
<p>To load the saved module parameters, call the <code class="docutils literal"><span class="pre">load-checkpoint</span></code> function:</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">def </span><span class="nv">new-mod</span> <span class="p">(</span><span class="nf">m/load-checkpoint</span> <span class="p">{</span><span class="ss">:prefix</span> <span class="s">"my-model"</span> <span class="ss">:epoch</span> <span class="mi">1</span> <span class="ss">:load-optimizer-states</span> <span class="nv">true</span><span class="p">}))</span>
<span class="nv">new-mod</span> <span class="c1">;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]</span>
</pre></div>
</div>
<p>To initialize parameters, Bind the symbols to construct executors first with <code class="docutils literal"><span class="pre">bind</span></code> function. Then, initialize the parameters and auxiliary states by calling <code class="docutils literal"><span class="pre">init-params</span></code> function.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="nb">-> </span><span class="nv">new-mod</span>
<span class="p">(</span><span class="nf">m/bind</span> <span class="p">{</span><span class="ss">:data-shapes</span> <span class="p">(</span><span class="nf">mx-io/provide-data</span> <span class="nv">train-data</span><span class="p">)</span> <span class="ss">:label-shapes</span> <span class="p">(</span><span class="nf">mx-io/provide-label</span> <span class="nv">train-data</span><span class="p">)})</span>
<span class="p">(</span><span class="nf">m/init-params</span><span class="p">))</span>
</pre></div>
</div>
<p>To get current parameters, use <code class="docutils literal"><span class="pre">params</span></code></p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="k">let </span><span class="p">[[</span><span class="nv">arg-params</span> <span class="nv">aux-params</span><span class="p">]</span> <span class="p">(</span><span class="nf">m/params</span> <span class="nv">new-mod</span><span class="p">)]</span>
<span class="p">{</span><span class="ss">:arg-params</span> <span class="nv">arg-params</span>
<span class="ss">:aux-params</span> <span class="nv">aux-params</span><span class="p">})</span>
<span class="c1">;; {:arg-params</span>
<span class="c1">;; {"fc3_bias"</span>
<span class="c1">;; #object[org.apache.mxnet.NDArray 0x39adc3b0 "org.apache.mxnet.NDArray@49caf426"],</span>
<span class="c1">;; "fc2_weight"</span>
<span class="c1">;; #object[org.apache.mxnet.NDArray 0x25baf623 "org.apache.mxnet.NDArray@a6c8f9ac"],</span>
<span class="c1">;; "fc1_bias"</span>
<span class="c1">;; #object[org.apache.mxnet.NDArray 0x6e089973 "org.apache.mxnet.NDArray@9f91d6eb"],</span>
<span class="c1">;; "fc3_weight"</span>
<span class="c1">;; #object[org.apache.mxnet.NDArray 0x756fd109 "org.apache.mxnet.NDArray@2dd0fe3c"],</span>
<span class="c1">;; "fc2_bias"</span>
<span class="c1">;; #object[org.apache.mxnet.NDArray 0x1dc69c8b "org.apache.mxnet.NDArray@d128f73d"],</span>
<span class="c1">;; "fc1_weight"</span>
<span class="c1">;; #object[org.apache.mxnet.NDArray 0x20abc769 "org.apache.mxnet.NDArray@b8e1c5e8"]},</span>
<span class="c1">;; :aux-params {}}</span>
</pre></div>
</div>
<p>To assign parameter and aux state values, use the <code class="docutils literal"><span class="pre">set-params</span></code> function.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="p">(</span><span class="nf">m/set-params</span> <span class="nv">new-mod</span> <span class="p">{</span><span class="ss">:arg-params</span> <span class="p">(</span><span class="nf">m/arg-params</span> <span class="nv">new-mod</span><span class="p">)</span> <span class="ss">:aux-params</span> <span class="p">(</span><span class="nf">m/aux-params</span> <span class="nv">new-mod</span><span class="p">)})</span>
<span class="c1">;=> #object[org.apache.mxnet.module.Module 0x5304d0f4 "org.apache.mxnet.module.Module@5304d0f4"]</span>
</pre></div>
</div>
<p>To resume training from a saved checkpoint, pass the loaded parameters to the <code class="docutils literal"><span class="pre">fit</span></code> function. This will prevent <code class="docutils literal"><span class="pre">fit</span></code> from initialzing randomly.</p>
<p>Create fit-params and then use it to set <code class="docutils literal"><span class="pre">begin-epoch</span></code> so that <code class="docutils literal"><span class="pre">fit</span></code> knows to resume from a saved epoch.</p>
<div class="highlight-clojure"><div class="highlight"><pre><span></span><span class="c1">;; reset the training data before calling fit or you will get an error</span>
<span class="p">(</span><span class="nf">mx-io/reset</span> <span class="nv">train-data</span><span class="p">)</span>
<span class="p">(</span><span class="nf">mx-io/reset</span> <span class="nv">test-data</span><span class="p">)</span>
<span class="p">(</span><span class="nf">m/fit</span> <span class="nv">new-mod</span> <span class="p">{</span><span class="ss">:train-data</span> <span class="nv">train-data</span> <span class="ss">:eval-data</span> <span class="nv">test-data</span> <span class="ss">:num-epoch</span> <span class="mi">2</span>
<span class="ss">:fit-params</span> <span class="p">(</span><span class="nb">-> </span><span class="p">(</span><span class="nf">m/fit-params</span> <span class="p">{</span><span class="ss">:begin-epoch</span> <span class="mi">1</span><span class="p">}))})</span>
</pre></div>
</div>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline"></a></h2>
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<li>See <a class="reference internal" href="symbol.html"><span class="doc">Symbolic API</span></a> for operations on NDArrays that assemble neural networks from layers.</li>
<li>See <a class="reference internal" href="ndarray.html"><span class="doc">NDArray API</span></a> for vector/matrix/tensor operations.</li>
<li>See <a class="reference internal" href="kvstore.html"><span class="doc">KVStore API</span></a> for multi-GPU and multi-host distributed training.</li>
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<h3><a href="../../index.html">Table Of Contents</a></h3>
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<li><a class="reference internal" href="#">Module API</a><ul>
<li><a class="reference internal" href="#prepare-the-data">Prepare the Data</a></li>
<li><a class="reference internal" href="#preparing-a-module-for-computation">Preparing a Module for Computation</a></li>
<li><a class="reference internal" href="#training-and-predicting">Training and Predicting</a></li>
<li><a class="reference internal" href="#saving-and-loading">Saving and Loading</a></li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
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