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 <p><a id='Callback-in-training-1'></a></p>
 <h1 id="callback-in-training">Callback in training</h1>
 <p><a id='MXNet.mx.AbstractBatchCallback' href='#MXNet.mx.AbstractBatchCallback'>#</a>
 <strong><code>MXNet.mx.AbstractBatchCallback</code></strong> &mdash; <em>Type</em>.</p>
 <pre><code class="julia">AbstractBatchCallback
 </code></pre>

 <p>Abstract type of callbacks to be called every mini-batch.</p>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L25-L29' class='documenter-source'>source</a><br></p>
 <p><a id='MXNet.mx.AbstractCallback' href='#MXNet.mx.AbstractCallback'>#</a>
 <strong><code>MXNet.mx.AbstractCallback</code></strong> &mdash; <em>Type</em>.</p>
 <pre><code class="julia">AbstractCallback
 </code></pre>

 <p>Abstract type of callback functions used in training.</p>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L18-L22' class='documenter-source'>source</a><br></p>
 <p><a id='MXNet.mx.AbstractEpochCallback' href='#MXNet.mx.AbstractEpochCallback'>#</a>
 <strong><code>MXNet.mx.AbstractEpochCallback</code></strong> &mdash; <em>Type</em>.</p>
 <pre><code class="julia">AbstractEpochCallback
 </code></pre>

 <p>Abstract type of callbacks to be called every epoch.</p>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L32-L36' class='documenter-source'>source</a><br></p>
 <p><a id='MXNet.mx.do_checkpoint-Tuple{AbstractString}' href='#MXNet.mx.do_checkpoint-Tuple{AbstractString}'>#</a>
 <strong><code>MXNet.mx.do_checkpoint</code></strong> &mdash; <em>Method</em>.</p>
 <pre><code class="julia">do_checkpoint(prefix; frequency=1, save_epoch_0=false)
 </code></pre>

 <p>Create an <code>AbstractEpochCallback</code> that save checkpoints of the model to disk. The checkpoints can be loaded back later on.</p>
 <p><strong>Arguments</strong></p>
 <ul>
 <li><code>prefix::AbstractString</code>: the prefix of the filenames to save the model. The model architecture will be saved to prefix-symbol.json, while the weights will be saved to prefix-0012.params, for example, for the 12-th epoch.</li>
 <li><code>frequency::Int</code>: keyword argument, default is 1. The frequency (measured in epochs) to save checkpoints.</li>
 <li><code>save_epoch_0::Bool</code>: keyword argument, default false. Whether we should save a checkpoint for epoch 0 (model initialized but not seen any data yet).</li>
 </ul>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L138-L153' class='documenter-source'>source</a><br></p>
 <p><a id='MXNet.mx.every_n_batch-Tuple{Function,Int64}' href='#MXNet.mx.every_n_batch-Tuple{Function,Int64}'>#</a>
 <strong><code>MXNet.mx.every_n_batch</code></strong> &mdash; <em>Method</em>.</p>
 <pre><code class="julia">every_n_batch(callback :: Function, n :: Int; call_on_0 = false)
 </code></pre>

 <p>A convenient function to construct a callback that runs every <code>n</code> mini-batches.</p>
 <p><strong>Arguments</strong></p>
 <ul>
 <li><code>call_on_0::Bool</code>: keyword argument, default false. Unless set, the callback         will <em>not</em> be run on batch 0.</li>
 </ul>
 <p>For example, the <a href="./#MXNet.mx.speedometer-Tuple{}"><code>speedometer</code></a> callback is defined as</p>
 <pre><code class="julia">every_n_batch(frequency, call_on_0=true) do state :: OptimizationState
   if state.curr_batch == 0
     # reset timer
   else
     # compute and print speed
   end
 end
 </code></pre>

 <p>See also <a href="./#MXNet.mx.every_n_epoch-Tuple{Function,Int64}"><code>every_n_epoch</code></a> and <a href="./#MXNet.mx.speedometer-Tuple{}"><code>speedometer</code></a>.</p>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L45-L67' class='documenter-source'>source</a><br></p>
 <p><a id='MXNet.mx.every_n_epoch-Tuple{Function,Int64}' href='#MXNet.mx.every_n_epoch-Tuple{Function,Int64}'>#</a>
 <strong><code>MXNet.mx.every_n_epoch</code></strong> &mdash; <em>Method</em>.</p>
 <pre><code class="julia">every_n_epoch(callback :: Function, n :: Int; call_on_0 = false)
 </code></pre>

 <p>A convenient function to construct a callback that runs every <code>n</code> full data-passes.</p>
 <ul>
 <li><code>call_on_0::Bool</code>: keyword argument, default false. Unless set, the callback         will <em>not</em> be run on epoch 0. Epoch 0 means no training has been performed         yet. This is useful if you want to inspect the randomly initialized model         that has not seen any data yet.</li>
 </ul>
 <p>See also <a href="./#MXNet.mx.every_n_batch-Tuple{Function,Int64}"><code>every_n_batch</code></a>.</p>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L112-L123' class='documenter-source'>source</a><br></p>
 <p><a id='MXNet.mx.speedometer-Tuple{}' href='#MXNet.mx.speedometer-Tuple{}'>#</a>
 <strong><code>MXNet.mx.speedometer</code></strong> &mdash; <em>Method</em>.</p>
 <pre><code class="julia">speedometer(;frequency=50)
 </code></pre>

 <p>Create an <code>AbstractBatchCallback</code> that measure the training speed    (number of samples processed per second) every k mini-batches.</p>
 <p><strong>Arguments</strong></p>
 <ul>
 <li><code>frequency::Int</code>: keyword argument, default 50. The frequency (number of         min-batches) to measure and report the speed.</li>
 </ul>
 <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L81-L90' class='documenter-source'>source</a><br></p>


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	<a href="https://github.com/apache/mxnet/tree/master/edit/master/docs/api/callback.md" title="Edit this page" class="md-icon md-content__icon"></a>


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	<p><a id='Callback-in-training-1'></a></p>
	<h1 id="callback-in-training">Callback in training</h1>
	<p><a id='MXNet.mx.AbstractBatchCallback' href='#MXNet.mx.AbstractBatchCallback'>#</a>
	<strong><code>MXNet.mx.AbstractBatchCallback</code></strong> — <em>Type</em>.</p>
	<pre><code class="julia">AbstractBatchCallback
	</code></pre>

	<p>Abstract type of callbacks to be called every mini-batch.</p>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L25-L29' class='documenter-source'>source</a><br></p>
	<p><a id='MXNet.mx.AbstractCallback' href='#MXNet.mx.AbstractCallback'>#</a>
	<strong><code>MXNet.mx.AbstractCallback</code></strong> — <em>Type</em>.</p>
	<pre><code class="julia">AbstractCallback
	</code></pre>

	<p>Abstract type of callback functions used in training.</p>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L18-L22' class='documenter-source'>source</a><br></p>
	<p><a id='MXNet.mx.AbstractEpochCallback' href='#MXNet.mx.AbstractEpochCallback'>#</a>
	<strong><code>MXNet.mx.AbstractEpochCallback</code></strong> — <em>Type</em>.</p>
	<pre><code class="julia">AbstractEpochCallback
	</code></pre>

	<p>Abstract type of callbacks to be called every epoch.</p>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L32-L36' class='documenter-source'>source</a><br></p>
	<p><a id='MXNet.mx.do_checkpoint-Tuple{AbstractString}' href='#MXNet.mx.do_checkpoint-Tuple{AbstractString}'>#</a>
	<strong><code>MXNet.mx.do_checkpoint</code></strong> — <em>Method</em>.</p>
	<pre><code class="julia">do_checkpoint(prefix; frequency=1, save_epoch_0=false)
	</code></pre>

	<p>Create an <code>AbstractEpochCallback</code> that save checkpoints of the model to disk. The checkpoints can be loaded back later on.</p>
	<p><strong>Arguments</strong></p>
	<ul>
	<li><code>prefix::AbstractString</code>: the prefix of the filenames to save the model. The model architecture will be saved to prefix-symbol.json, while the weights will be saved to prefix-0012.params, for example, for the 12-th epoch.</li>
	<li><code>frequency::Int</code>: keyword argument, default is 1. The frequency (measured in epochs) to save checkpoints.</li>
	<li><code>save_epoch_0::Bool</code>: keyword argument, default false. Whether we should save a checkpoint for epoch 0 (model initialized but not seen any data yet).</li>
	</ul>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L138-L153' class='documenter-source'>source</a><br></p>
	<p><a id='MXNet.mx.every_n_batch-Tuple{Function,Int64}' href='#MXNet.mx.every_n_batch-Tuple{Function,Int64}'>#</a>
	<strong><code>MXNet.mx.every_n_batch</code></strong> — <em>Method</em>.</p>
	<pre><code class="julia">every_n_batch(callback :: Function, n :: Int; call_on_0 = false)
	</code></pre>

	<p>A convenient function to construct a callback that runs every <code>n</code> mini-batches.</p>
	<p><strong>Arguments</strong></p>
	<ul>
	<li><code>call_on_0::Bool</code>: keyword argument, default false. Unless set, the callback will <em>not</em> be run on batch 0.</li>
	</ul>
	<p>For example, the <a href="./#MXNet.mx.speedometer-Tuple{}"><code>speedometer</code></a> callback is defined as</p>
	<pre><code class="julia">every_n_batch(frequency, call_on_0=true) do state :: OptimizationState
	if state.curr_batch == 0
	# reset timer
	else
	# compute and print speed
	end
	end
	</code></pre>

	<p>See also <a href="./#MXNet.mx.every_n_epoch-Tuple{Function,Int64}"><code>every_n_epoch</code></a> and <a href="./#MXNet.mx.speedometer-Tuple{}"><code>speedometer</code></a>.</p>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L45-L67' class='documenter-source'>source</a><br></p>
	<p><a id='MXNet.mx.every_n_epoch-Tuple{Function,Int64}' href='#MXNet.mx.every_n_epoch-Tuple{Function,Int64}'>#</a>
	<strong><code>MXNet.mx.every_n_epoch</code></strong> — <em>Method</em>.</p>
	<pre><code class="julia">every_n_epoch(callback :: Function, n :: Int; call_on_0 = false)
	</code></pre>

	<p>A convenient function to construct a callback that runs every <code>n</code> full data-passes.</p>
	<ul>
	<li><code>call_on_0::Bool</code>: keyword argument, default false. Unless set, the callback will <em>not</em> be run on epoch 0. Epoch 0 means no training has been performed yet. This is useful if you want to inspect the randomly initialized model that has not seen any data yet.</li>
	</ul>
	<p>See also <a href="./#MXNet.mx.every_n_batch-Tuple{Function,Int64}"><code>every_n_batch</code></a>.</p>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L112-L123' class='documenter-source'>source</a><br></p>
	<p><a id='MXNet.mx.speedometer-Tuple{}' href='#MXNet.mx.speedometer-Tuple{}'>#</a>
	<strong><code>MXNet.mx.speedometer</code></strong> — <em>Method</em>.</p>
	<pre><code class="julia">speedometer(;frequency=50)
	</code></pre>

	<p>Create an <code>AbstractBatchCallback</code> that measure the training speed (number of samples processed per second) every k mini-batches.</p>
	<p><strong>Arguments</strong></p>
	<ul>
	<li><code>frequency::Int</code>: keyword argument, default 50. The frequency (number of min-batches) to measure and report the speed.</li>
	</ul>
	<p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/callback.jl#L81-L90' class='documenter-source'>source</a><br></p>










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