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---
title: Inference Learner
keywords: fastai
sidebar: home_sidebar
summary: "Intermediate tutorial, explains how to create a Learner for inference"
---
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<h2 id="Create-a-Learner-for-inference">Create a Learner for inference<a class="anchor-link" href="#Create-a-Learner-for-inference">¶</a></h2>
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<p>In this tutorial, we'll see how the same API allows you to create an empty <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> for a <a href="/basic_train.html#Learner"><code>Learner</code></a> at inference time (once you have trained your model) and how to call the <code>predict</code> method to get the predictions on a single item.</p>
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<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>As usual, this page is generated from a notebook that you can find in the <code>docs_src</code> folder of the
<a href="https://github.com/fastai/fastai">fastai repo</a>. We use the saved models from <a href="/tutorial.data.html">this tutorial</a> to
have this notebook run quickly.</div>
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<h2 id="Vision">Vision<a class="anchor-link" href="#Vision">¶</a></h2>
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<p>To quickly get acces to all the vision functionality inside fastai, we use the usual import statements.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai.vision</span> <span class="k">import</span> <span class="o">*</span>
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<h3 id="A-classification-problem">A classification problem<a class="anchor-link" href="#A-classification-problem">¶</a></h3>
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<p>Let's begin with our sample of the MNIST dataset.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mnist</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">MNIST_TINY</span><span class="p">)</span>
<span class="n">tfms</span> <span class="o">=</span> <span class="n">get_transforms</span><span class="p">(</span><span class="n">do_flip</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<p>It's set up with an imagenet structure so we use it to split our training and validation set, then labelling.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">ImageList</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">mnist</span><span class="p">)</span>
<span class="o">.</span><span class="n">split_by_folder</span><span class="p">()</span>
<span class="o">.</span><span class="n">label_from_folder</span><span class="p">()</span>
<span class="o">.</span><span class="n">add_test_folder</span><span class="p">(</span><span class="s1">'test'</span><span class="p">)</span>
<span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">tfms</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
<span class="o">.</span><span class="n">databunch</span><span class="p">()</span>
<span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">imagenet_stats</span><span class="p">))</span>
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<p>Now that our data has been properly set up, we can train a model. We already did in the <a href="/tutorial.data.html">look at your data tutorial</a> so we'll just load our saved results here.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mini_train'</span><span class="p">)</span>
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<p>Once everything is ready for inference, we just have to call <code>learn.export</code> to save all the information of our <a href="/basic_train.html#Learner"><code>Learner</code></a> object for inference: the stuff we need in the <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> (transforms, classes, normalization...), the model with its weights and all the callbacks our <a href="/basic_train.html#Learner"><code>Learner</code></a> was using. Everything will be in a file named <code>export.pkl</code> in the folder <code>learn.path</code>. If you deploy your model on a different machine, this is the file you'll need to copy.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">export</span><span class="p">()</span>
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<p>To create the <a href="/basic_train.html#Learner"><code>Learner</code></a> for inference, you'll need to use the <a href="/basic_train.html#load_learner"><code>load_learner</code></a> function. Note that you don't have to specify anything: it remembers the classes, the transforms you used or the normalization in the data, the model, its weigths... The only argument needed is the folder where the 'export.pkl' file is.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">load_learner</span><span class="p">(</span><span class="n">mnist</span><span class="p">)</span>
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<p>You can now get the predictions on any image via <code>learn.predict</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_ds</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
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<pre>(Category 3, tensor(0), tensor([0.5275, 0.4725]))</pre>
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<p>It returns a tuple of three things: the object predicted (with the class in this instance), the underlying data (here the corresponding index) and the raw probabilities. You can also do inference on a larger set of data by adding a <em>test set</em>. This is done by passing an <a href="/data_block.html#ItemList"><code>ItemList</code></a> to <a href="/basic_train.html#load_learner"><code>load_learner</code></a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">load_learner</span><span class="p">(</span><span class="n">mnist</span><span class="p">,</span> <span class="n">test</span><span class="o">=</span><span class="n">ImageList</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">mnist</span><span class="o">/</span><span class="s1">'test'</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span><span class="p">,</span><span class="n">y</span> <span class="o">=</span> <span class="n">learn</span><span class="o">.</span><span class="n">get_preds</span><span class="p">(</span><span class="n">ds_type</span><span class="o">=</span><span class="n">DatasetType</span><span class="o">.</span><span class="n">Test</span><span class="p">)</span>
<span class="n">preds</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<pre>tensor([[0.9866, 0.0134],
[0.0019, 0.9981],
[0.9721, 0.0279],
[0.0067, 0.9933],
[0.9966, 0.0034]])</pre>
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<h3 id="A-multi-label-problem">A multi-label problem<a class="anchor-link" href="#A-multi-label-problem">¶</a></h3>
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<p>Now let's try these on the planet dataset, which is a little bit different in the sense that each image can have multiple tags (and not just one label).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">planet</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">PLANET_TINY</span><span class="p">)</span>
<span class="n">planet_tfms</span> <span class="o">=</span> <span class="n">get_transforms</span><span class="p">(</span><span class="n">flip_vert</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">max_lighting</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">max_zoom</span><span class="o">=</span><span class="mf">1.05</span><span class="p">,</span> <span class="n">max_warp</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
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<p>Here each images is labelled in a file named <code>labels.csv</code>. We have to add <a href="/train.html#train"><code>train</code></a> as a prefix to the filenames, <code>.jpg</code> as a suffix and indicate that the labels are separated by spaces.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">ImageList</span><span class="o">.</span><span class="n">from_csv</span><span class="p">(</span><span class="n">planet</span><span class="p">,</span> <span class="s1">'labels.csv'</span><span class="p">,</span> <span class="n">folder</span><span class="o">=</span><span class="s1">'train'</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s1">'.jpg'</span><span class="p">)</span>
<span class="o">.</span><span class="n">split_by_rand_pct</span><span class="p">()</span>
<span class="o">.</span><span class="n">label_from_df</span><span class="p">(</span><span class="n">label_delim</span><span class="o">=</span><span class="s1">' '</span><span class="p">)</span>
<span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">planet_tfms</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
<span class="o">.</span><span class="n">databunch</span><span class="p">()</span>
<span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">imagenet_stats</span><span class="p">))</span>
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<p>Again, we load the model we saved in <a href="/tutorial.data.html">look at your data tutorial</a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mini_train'</span><span class="p">)</span>
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<p>Then we can export it before loading it for inference.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">export</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">load_learner</span><span class="p">(</span><span class="n">planet</span><span class="p">)</span>
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<p>And we get the predictions on any image via <code>learn.predict</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_ds</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
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<pre>(MultiCategory blooming;clear;habitation;haze;primary,
tensor([0., 0., 0., 1., 1., 0., 0., 1., 1., 0., 1., 0., 0., 0.]),
tensor([0.1906, 0.3646, 0.3146, 0.6931, 0.6352, 0.3982, 0.3114, 0.5396, 0.5245,
0.3933, 0.6069, 0.1630, 0.4260, 0.2641]))</pre>
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<p>Here we can specify a particular threshold to consider the predictions to be correct or not. The default is <code>0.5</code>, but we can change it.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">thresh</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
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<pre>(MultiCategory artisinal_mine;bare_ground;blooming;clear;cloudy;cultivation;habitation;haze;partly_cloudy;primary;selective_logging,
tensor([0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0.]),
tensor([0.1906, 0.3646, 0.3146, 0.6931, 0.6352, 0.3982, 0.3114, 0.5396, 0.5245,
0.3933, 0.6069, 0.1630, 0.4260, 0.2641]))</pre>
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<h3 id="A-regression-example">A regression example<a class="anchor-link" href="#A-regression-example">¶</a></h3>
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<p>For the next example, we are going to use the <a href="https://data.vision.ee.ethz.ch/cvl/gfanelli/head_pose/head_forest.html#db">BIWI head pose</a> dataset. On pictures of persons, we have to find the center of their face. For the fastai docs, we have built a small subsample of the dataset (200 images) and prepared a dictionary for the correspondance filename to center.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">biwi</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">BIWI_SAMPLE</span><span class="p">)</span>
<span class="n">fn2ctr</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">biwi</span><span class="o">/</span><span class="s1">'centers.pkl'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">))</span>
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<p>To grab our data, we use this dictionary to label our items. We also use the <a href="/vision.data.html#PointsItemList"><code>PointsItemList</code></a> class to have the targets be of type <a href="/vision.image.html#ImagePoints"><code>ImagePoints</code></a> (which will make sure the data augmentation is properly applied to them). When calling <a href="/tabular.transform.html#tabular.transform"><code>transform</code></a> we make sure to set <code>tfm_y=True</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">PointsItemList</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">biwi</span><span class="p">)</span>
<span class="o">.</span><span class="n">split_by_rand_pct</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="o">.</span><span class="n">label_from_func</span><span class="p">(</span><span class="k">lambda</span> <span class="n">o</span><span class="p">:</span><span class="n">fn2ctr</span><span class="p">[</span><span class="n">o</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
<span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">get_transforms</span><span class="p">(),</span> <span class="n">tfm_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span><span class="mi">160</span><span class="p">))</span>
<span class="o">.</span><span class="n">databunch</span><span class="p">()</span>
<span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">imagenet_stats</span><span class="p">))</span>
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<p>As before, the road to inference is pretty straightforward: load the model we trained before, export the <a href="/basic_train.html#Learner"><code>Learner</code></a> then load it for production.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">cnn_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">,</span> <span class="n">lin_ftrs</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">],</span> <span class="n">ps</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mini_train'</span><span class="p">);</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">export</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">load_learner</span><span class="p">(</span><span class="n">biwi</span><span class="p">)</span>
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<p>And now we can a prediction on an image.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">valid_ds</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
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<pre>(ImagePoints (120, 160),
tensor([[ 2.2184, -0.3693]]),
tensor([ 2.2184, -0.3693]))</pre>
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<p>To visualize the predictions, we can use the <a href="/vision.image.html#Image.show"><code>Image.show</code></a> method.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
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u998Ky6+neMDVhHXfm2O+xHRaxr032ZIf1lxIIXcdT2vn3+vKF507D/sjJnVOjOK9PfJV0IoDrncN6EaCMXItSd061K9iv7F3GktSCqp0zHAJalr8O1zkE0gYFc4ZabgNp+X7tMbY76k5BvlQkpGoejl6KOVa3vE629DDK8rzunEAJXu3IhdFrhw162zql6D8vrIQZBhK1+vq9Nge2gl2gAjNd/XnQhgOqlwWFDkIXzSCTahWkhxgZDu/W1TOocpgbOSuaU1pDfNk/hHJLaB249y1C3tmJll/ezBFnlwhQSW8OktMtpzs67pmyx8Yy1WBvmQznvsN6CD9OrvfTMyzlIMNYgvaQoi5CMwYREEzEYxNdgEzqhNAZqudAgQSdUCJwIm3UeqTXeOaSSLV9/DWzfBpDC1Qh1gnDsBE62pjjXo5fFhWAgrSirCploClORpXmYmSAFVd0miPq9colWL4NTJogb4VxlDFJrjHPYOqZ3o+LQoQsB1GWShqVNL4oC7Ywa6/ZdharNZduG+eDlWt/l12ny8XxXFIi0iauex13b/33V6DzxoTt6hHvPHv80dCGAGkEQh9d4Lv5eKlfnm2hgVdvtKjvn4WMd0Lq203aDvMhUtU6xisft/atAXUXqXMfJmu/7WXzrhQBqt3E3cbWuJh0Bs5wntOqB2gSWLq3TzOP/24CMw2q7A3U5ePs71rla1333RaZu2SNQnWu3x+b//qzoQgC1qqrGXw00cZ/e+yYYpD3Mtw3w62TUri0zxo9uGvojdTlkOwYhukLbHLsrlsRZCPF3VwlrP/tV4qxdoEa79LrrS47avvbTl+FCAHWdvTNyr+7QHymCZZ2Mumno3zTtYZN8+TKa/bprXcN7955XkTbJqOdx1J8lXQigCiEarhU5VwzxO88c1OZa8TldzhcTgIX/rQamdBWwdUAqioKqqlaytcSAlE1AbYcptsWEmMvJ+5CgbVnWs6lvgCYIpSjmzaiQ5yHVURxx4gjTnnWwrn7DXp7p8O3r3XNSSqxzK+9K07TObbDevnseSSkpi7IJFGoHuryILgRQu/70eG6T7Hjex3Wj79uiwdJBs1rJ5yk66xSl8663z6+j8+7ZxHVf5lk/S3rRd/wsZdRXCqhRRo29VgjRcK32+XY4GrzY390915ZRu27DdSBtK1ORywMNl17Xgdodrm1ia+eq6lo52hy1eZ7YPIp4vzTifxbRYlNH3HRP9/5NGv/LyqjnKZYvogsB1E1cq536u6vIrANoG8zr37P53e3fm8rVBlfbNbnunu79Z8G5tCwIsY4zny3TeeLGyw7B6771RSNK91z4vqVI9rIc9WVHkHV0YYAazUxde2rXEdCm1WF9fW9vXwshg6vn4/vj/mWBum7eVPtZ60xjm+8/H4jr6ku0jl90P2vu7Z5bd++ma5sM/vWPeFfr+atOmleWo7aHxhX3J6s21kjrtPxN3qmfRkbtgrS9P29r/zd+y8sANZL3Hs/6Mq2jn5Sjrjt3Htc7z9j/Ms/8rOBs08UAauWwVZ20gVrG0x6kx1b1tBDvQDVxfiDXTz9e57GKm2W91t+VfeNxyLtfUlUFUkJV+Vprd01Wv+5/KmuWycfqLCVehIwlrk52YethU8kQq5AIhbcOW6fZAI+zFq0UqdRIrTg9OiI1nvmz55ycnDArCvqDMYV1CCnpj7bY2tnB4WsLhcNUc9JM4yrDbHpKmiaN31/W5VoJPLGrTgwpJdLLtR2uO/I1ItE57dwWiV4m/LJNFwKomzhN1w4KbW64+VmRzogFclUxaDdA97/t97fly00yavf/L/N9kZwzdd4oWQeShCx8xhhOj48QriJRgv/wH36fez+6S1EUwUGS5JTWItOM3Ss3+OYv/wpf+uI7LSXOs1gYUiUZbV1iUUwb5r0OHj8Nx4v/b7fLi2TUz0IXAqhtIHTBs9ngvwaIsFZEWF4/GyAMm4f+FwE10nmyaPv8+vstzgVgeSzWLYfyREMyyjjcP+IHf/W3HB0+48tfeoOrl3ex1YJPHj3iweNPefjpxzy8/xHPD59xtP+Md955hxs3bzObV/T7fRKVUhSrK6K8jA25e64rMm2iKJd2j1/0/PPowgG16x7daPB364f+dRy1SfndqehNppbmHefIpO171/1vHVA3ba7O92cB52M8gcSaiqP9PR7c/zHYGf/6v/tvUN6zt/eQ8TDhzsNL3H+0ww8/yPng7ifc+/Bv2H/6hIOnv8Qv/fKvc+PGbazxLCYFxlaIbLN1Y10dtMvertPuN0WrRbceus9bNwq9LF0IoFZVQVUVOKea9D1VFfz9cakb5wTL/J8ea90ZjrnOhroSsNLhJi+SUdvcPHactp97XaW3s5Z0JwWu8/c7PIgA0FBOj/AeV814/MmPuLK9xa//o19EY3n04McM8ow7VwccHzzk9o5gd3SD166Pefv1a/xv/+b/4eDxPf79v33Mn3z7O/zX/+Jf8Ytf/yaDwZiT6RHelys2uk2dadPvdUpruL5OMWzbp8/OEv6sgL0QQF0npMeG3mRHbSLb11RgPN8GqRCiUcbiuQbAG4C3buhfJ6as2y+5zapDoXvdewFKYY1H+pCzH2expSORgkQrivmMyWLC7evXcFXJ/t4zpDUkWYooS/pZztXLl9ne3uZ09oxs0GMyn/B//f7vYYXkna98FYRFdXLmbgLLeSMHnB25XiQ2nMehXymgbhouz1OmeEEQdHzGSp7/VkrvdSD6LEBdriDCmf92O0z321bKK0CR1HzIoOoM2pUF5RUHT55R5Bm74yEnB4cUxRwlBd5pEt1HComUFXnWJ01T+v0+z09OKJ3g4eOH/B//7t/wm7MT/uk/+22mJ/OXaouflKIY8POgCwJUx2Ixr9cc8uR5zmIxJ03TpaLhHdbGlN2idbya5SNmuQNWUljOZjNu3rnF8fExzjnyPEdKyWKx2LjWUReI7QCX8ywF8XoEdKTZbNYsJmatZTAYYEpH5SrG2ztMjw8oKoPwYdnKd7/7LjvjHq/dukVfa+59+CFHR0cIb9nZ2eHLX7mK1inTwwOKSrIoLYuyQmcpzsK0mlA8q/jue9/lH3zjHYbao+Vy5ZcYbNPuhG2XtbUWIcN5rTVFUZDn+ZnwxlgHSimcjR223Ulp3tfOshKDaV6GLgRQw8Q+E+YLEfKAAhjbyvdZFzUuAmH9ag77SGmaNil0Dg4O2N/f56OPPuLHP/4x/+p/+NeMRiPyPAfCuk3t1eS6tInTtxupfe+6/7T/O51OefDgAc+ePWNra4uvfe1raCFxKuNg7zm+mFLMJihvePrwPtvDAcd7e7y//5z9nW1mp4dordnZucFi4fnuX3zI1evX6A12kdpiKiiLiklVgdK4HKanE/7yr/4c/b94/vt/+S9DfbdGBGMMw+GQ+/fv89EHH3LlyhWuX79Ov9/HidqmW49qcY5a2+vWtabQTMRclVF/WroYQO0M8V2lI94DNGnCY0qfrqloOp02yc0Gg0GdXj3j+vXrpGlKUYQFciMniavQraOuQvUiUaFb5i5Yq6ri4OCAx48fA4Hj4yVZliISzd/+4C6LyTGv37rG5OQ589MTDp8fIIzBVQv2nz1BSsnJZMZ46yp/84MfcuPWTW7evs3O5V2+/NYX2NvbY1ZUOAwKgZIKO1/w+OGjlTK2TW2z2Yz9/X3ef/99Dg8P8d5z8+ZNEr36TW1OvA6o59VJ8xzOF4U20YUAaluRWtfQ6+6NQ8u6Z0UuPBgMyLKsSR8ZNfZ2JNR5NsFuuWDzkN/dr9uuXr1Kvx9kyV6vR1mWZFkGWKS0LGZH7D19RC8pqcopVy6P+ZV/9HVGwz73P7nHw3GfS5cukaU9Dg9PsWbOpw8/5tnTh1y+eoVrO2O2BzmLxYLKeWxpSZHMvWJ2Mm3K1rYJW2uZTqd1Yrpr7OzsNAvKhe9ZdaGG1fsqVlPrrk8Z9DJAfKWUqU0ctavEQHttpGXltcGWZRllWVJVFUIIjo+P+eCDD3jvvff4ha9/jTzP0Vo38mtcTGIdnTeMt+9ZuZ+zYI2/o5hRFAW9Xq/pVIY53lbcvHGZcnrAydEewzzl9dducO3aNomSjMdD9vcTlAKpPMIX/MNvfpXpdMqPP77Hg48/wicZoyzlWAsKIzmZF8gkRxpBOS3qOlyCKgZbxxSWly5dYjwO632FTl01K8XE/xhjWCwW5Hm/Ve/LTrysjrNBKT8NXSigdjXsuLY9nDUaG+uRUp0BalEUpEmOkgnHR6dIqfn6L36TL33xHYSUHB0eI7VkWK+POp3NGA4GLesfTVKFjc6G82TUCFR3Nug7ys+np6f0+/3G+2TslGEv4c03rvLWnW3M4hTlDH/9l/+J2eFjtre3GW0N+Me/9q3wvR4wr7H/6VOkVHzj618kTXN+7/f/gEeffIwyjmGSUSwqlIZEJMjCNkCKMmqcrQCwv7/PgwcPGI1GjEYD0jRnNl2gWtN3vPcURcHx8TFap2f0g9ARonLbtr6ExB/rhv5XiqMCjcYZlo8MHxmnbXRlRGMM/cGIyWTaJLU1xvD+Dz/EGMOTJ884Pj7m7be/FFaqTnNu3rnFp8+e1asAambzBYPBAC8llTF4KVBaID1YwqIR4d2W1SXUY0OsM1zXSz1KEdau8hLhZVxwitlkyvb4Er/5T/5zwoK+lvnCYH2B1mAmxyTWou2Cj+9+xL0fvs9dW7G9u8ObX3yb62++gdIJvd6Ak+MZTveZ19NU5vMFg/GY0hQ4B/NZwZtv3uJ0MuXw+JSynKKEIongtB5nLMJDv5/TzzPe+fIXuTQesZhPydJ0xarS1gWivG2MYW9vj/F4m/F4DECvlzKZTIJVoz8KYkhVoZQmSRRFvSq28J5Era61eh5dGKDCelNPV5mBwFVnsxm9Xg9jDHfv3uXDDz/kj77zJxweHtPv9+n3h3z/vb9hPp9z69Ydvv6ffZ13vvYljk9PGfohWZ5wPDllOBxirQlrlPp2HhVPO3SjrUysUxhWP2SzJyZSlJXxHq8ElTU440nxJCrl+uUrpF//JmVZgBSkOgOvmBeWo5MDXGGoFgWLxQJRc6miKrl87SpePcc4+ObXv8qHP77H8fERSizFpi6VZcl4PGaRpkgJW1tb2LI4U/4o50sp6fV6fO973+Pg4IA7d15HCMF8Pufo6AhjDFmW8eYbbwHUiYc11r5cpNQ6ulBAhY57cQNQAbTUVFXF/fv3+cM//EPef/99nnz6jCTJ6ko5pViUlGUZQIHhq9/4SjOhTqmc2WxWKzO1FuzEima/adhfN+Qvr3NGTl0lV3Po+lnOgZRUpSCRKaaYMatK8AnXr90mSRRCSUpnUTKjMiW2NJRFga0qqqrC2aWItLu7y+7lq1gv2L1yhezRY7yH3iCssO07CisELX48HnNyfIwxQcE7mkyb9Wdj/cPSLb1YLNjf3+fo6IjBYNSIBbEccTJkrIc4IXBT3b2ILgRQ2wpUN71PG7Bt4/7x8THD4Yi/+Iu/4Dvf+Q4Ao9EWQqhgSDeGubcoLTk5Peb0gwmPHjzgjTfe4PjwkKqYB4XGLKOXhF9NV962ELTnP8Xr0LUa1A0ruqmATL0JhFiuUqgTiRS6XlvQo4Xn4aMHvP/eezz/9AGqKhltDbhz5w533ngde1xghSTPU4Qrm5wDRbmgLMOQev36dZxUHB5NmC0KTqdzCgvb40sr5W7XaVQu3333Xd5++ws8f/6cXpZR2rL5T/t7pZSUZclv/dZvMZ1OAclkMuHatWtNTUSba7v9lFpNqd6V88+jCwFUOMtJz9O4I+c6Ojpib28PpRRpmnJ6ekqe9xvvCoT5SEmSoDPN9773PZRS3Llzh/F4xOHhYbOIRLAsnJ2zfl4ZuiSEo52yvd0YsTxt47hUCikUVVlBHd6X6IzxeExiK6rJMYtixtNn+wzHl9i5eROcxbml/B6f5b3HIhiOtpA6ZefyDe4/OeDex5+AhK/94jcCMFtmugCe5YyKaHc2xpAMhg1Qu4EozjnG4zGTyYSqqhiNxsFB4Bzz+dJNGz15XaWrXb8vSxcCqOvkuXZPbIsA8YOzLOO73/0uH330UePiGwwGaK2pqiC7RaoqQ+UKfvjDHyKE4PT0lNu3bzIajZjP5/T79bDYyZt4HkC7HGEJyPb3uPWNIcJhYaM1AAAgAElEQVTMU+/r5dG9AwHWVSSp4tL2Fj0Nyc6QqipQSUqShMV7tVQ4U2HxQQxwpuH8RVGg84phr4/OM/7mb3/AdG5456tf4pd+9VcCMKVaAWpwaQbAXr16lclkQlEUlOUqSNujmXNhqc1PPvmE4+Njbt9+jcuXLzObzRrXtBBhecp2/WmdrNTrK8lR4SwwukBtlAEvQXru3r3LkydP6PV69dAcNNpoeinLsrGXDvMhR0dH/Nmf/Rl//dd/zVe+8mV+93d/d8XL0q20TZXYHQpXKZhoznJUC+gapEtftxCC/iAHa5idLlgspkxmp5w8f8rVrQE7l3cYDAZY7zg63kfpFKTAYpnNZo32PZ3PmUynVB5OJgueHhzw3vc/4vbr1/jlX/k1bt28w9yasD4qqyu/OBfEkeFwyEcffcDNmzcZ5D1Uqppv7A7j7aU2hRCN7TXWt5QyyN+sinY/KV0IoK5TWKK5qh3yF+JUK7SCyWzGd779xyQ6I0kSTk5OGI1GKK0wtgz2OixCOoQUzOYT8qwXluGZzvjj7/wRJ0fHfPGLX+SXf+WXGI1GWEOj0WqtqcSC2WzWDI/tbCTtCKxI4Vy0G0ZyxNWuQ4LiCPBlhzydPCdPJV4VyNxx5daYN9+8SkZZz9kqKYqCybykNBXGOBalaerFGENpKpK8h3Xw6PEj/vK9v+Kf/4v/il/+1X/C9u4uR8fHZP3eili1dHwEL97Vq1e5cmU3zAqQCi+Wz4cgbmRZ1ugAb731Fm+//TYgm7Zqr77dFnMi41BKr4Ruqm7s4Qa6kEBtV2asAKUURVGbTFQwUJdlwXA4Amjsqc5FZScmdghyoxBh9WeFIE9SKq358P33efrpp1y7cpm3336b7e1xaBS7XCNg3ZDfLft6N6xjJbO1iGLAWResThVCh8mHc7NgOj3h9MRyqReGeWMcpak4mc6C18164qrO4U2BI2Z5yrwoWRQleW/At/7xL5H3e8g0Qej1EWJAIwpkWUaWJfR6PapFwbycI9VqRwxWEqgqu+G7X44+i3wKFwSoXVdpG7jRCRBBG6L+Hffv30eIpXclTdMzwczRDEQdxGKtZT6fN/dEL8u3v/1tZrMZv/3b/0XjcIgLmHWVq/ZxW8Foy6jdeyPou88Je49A4rxH6QSdJhRlxfH+M55LhzdV8/yico3C5xwIas+cCvWgdY71sFgseOutt9jZ2eF0uqA6PmU4GlEW6+NR20NzXC1w+V1yxf4aV7Q2Ztb6/s/U3Gfq8WXoQgA1ctG2OSoOTW2zUJSVZrMZH330UfAsec9isSDLstoduKrIBM9HUJQWi8XSIiCWCS/effdd0jTlN37j11rPoQkK2sRF14E1vHd536oy1lau6oAPAWVlEdaSp312r9wgEYo00ew9+IRiHj07CqkT8PXKhdYh6m+Q9YijspTUS5K8xze/9Q+xDrYv73Lw/ITFotwUa76yFFFY5rNqLAIRuG2RJ5qeIr2sUrROxHtZuhBA7U45iT08+vrbXPP4+Jjv/sX3+P73v49ANdq+956yLOog6NpKgF0CRAiEUA1HiNeklPzO7/wOb775JmVZNsG8IZxws196s9bfuk8EbX69+FDfi0AneS1nepJsi6u3Bly/fpPqrXd48MnHPHjwgMVigfXUi2YohCpQYrnatUPgjePp3jNmizmXr17j4PkhY6HIB/0wEpXrOWoMf2wrTUAjP3Z/t/Nohesv39Y/KVgvxDLobX96u/DdeVLRI/LkyZMwPFcLdCIZjUZIGQV513DM8Ozgg4dg/nHe4AmdIUzd6AVvzu5uE3WVpunGqP/lM188bL1sQ9gq2hs11oAxDucFw60BSZaA8FgXExpHIHVEEmextuDKlV1+dPdjfu/f/Z+Mhj0mp8e4qsRVJeBBtLaa2p1MqaQe2dZ4pZArI8lPI6N+VroQQBXC45whKiAxO0lRzOn3c7z3zOdzTk9PKRYVd+/eJctSBoM+QkBZFiglcK5lYA5hw82GlyRKkWpNqjX9PMeYisPDA+7e/ZDt7TFOQD7oczqbQr2StPBhVijONccSQraWNZsSApzD2woVjKNgHdKDQuCNRTiPFgotJRKoijmpFNjKkCc5g3yA8AJrSxAlUlUobfGuwtmQfMI7gVBqmeXEW7SCqjrl2pWcH//4b/FmyrAncdWEXiqoTIH3liRRKBW8ZPP5tOm0UWNfLEqyrIfwoZyJ1AgHiUzQQiO9RDgRAiNsmHUhnKjP++XmV7dYB9Kzct/L0IUY+hGuliUVXe8NBKUnaPRBiQqKjiVkkHbNfzbJSo290BsEIgBXhJVjorxVVRVJbYrxPkxj7vbizRr+WVFgHRljmtkHxhgQkGqNGgxQSjEtSzQCLy3eOU5nxxTlAnAoBV5LvNRIqdFCopVE6dDRhfQI5RnkGd/4+i9wMlvw/PkTbty8g05Tjo/2kWmGlGBtFeak6YzhcMjJyQnz+Zz9vT1ef+3NZvYD/kLwMeCCALU9xCzD+QRJkjCZTBAiaP2PHz/mD/7gD5jNZuhUNR6QtnG9HQ4IZzOCxOO2LJqmKUdHR2T9XuPRClxrqXC1nxWfsU5GjfbR9nfFLcv6NUgWjdHcWYPOUvpZj2/8k98k15r7P/6AH334A57vHVAVJf08J5WKuXeYKkz9kIlGClBKoJRAqpAfIdWCN27fIM0GZM4yOdyj3xsx6I+wSU61CCa+4XCLp58+4fT0tDFN4WWTUbo0Bq07ORP8Z0uU9rOkCwXUeBz3ca36S5d2mM1mfPLJJ820ibYPuQ2G9jSLLoc7A+zo6nOOk5MTrt280Wi71tqQSKyj2XfLvc5E1b0WyTlHkmRY6zGm5Nq1G2xtbbG9vc18PifTCaYoOD484eToGFNWCOeJgowWEiElDhlW3ouKmgdvLVJAL88RUtEf9tgaZEyLGaeLkrQ0DK+N8drXEWXBFgrBIxXLG7V+4UP+qza1wyz/rulC8PauttzlRBFM9+7da4DUnoHa5sbrNPT4zHU2USklJycnPHv2bMUL0854sqls7ee0jzfdu1gsmsmHb775Jl/84pfZ3d0NyiACYxxVZZhOp8zn8yC/eQ/Whex+UpGphKT21+tk2dGstaRKIr1jNBywPR6Ras32aMign1MWc27dusXNG7cYDoeYajlXamdnB2NMsyVJwnA4BFbzd8Xffx90YThq3C8bNvTuNE2Zz+dorXn06FFjrupGn7cn4MHL5ZWKMQH7+/sMh8PGRBOnjCgp1pTr7LPPk13b94zH22RZxs7ODnfu3GHQH7G3/5STkwnHx6eMrl5Hp45UpWihSbMMV2nm3mMrgyQhzvl0PkzFca7CmBKJJen1UBLyRJNrjRCQ5X28kCSF486d1yhmRT1Hv+Lje/coihJjLNPpjMFg2Ig5wZ68as76+wIpXCCgaq2JeXrC7Mxea9WQED3+/Plz8izYBNM0aSbmxQpsB1q0Vx8BGo22DWhjQuRRnud88MEH/Orhr3PlyhVmsxn94SAMsS2vTFcsWUeLxYLRaIC1vgmQCW0v+bVf/Y3Gv54kGY8ePSZNUxZlgXWS08mMUS/n6o2bfPrwHsNM0ssUvTTEMhwfTPA2JFDDh7yyQlryVJNlfa7sXgIpGI2CW/nSeMyiCN/42u3X6OVjMhXe7Z3g2pXr7O8/46OPPuL6tZss5lPCIhIhWQasLgInCCbAsixXQNteeOJF7XzeiHQeXRigeu8RnXPR4K91yt7e3kp6nk1AiVp8OwayqZwN757NZhhjmsQQMUHFeeWN+1UZ1TfBF87RNKqUiq2trcYOvFiUaB2SX/T7ffYOnteLAwcxJkkS8kEfzDzE2uYZAzvAlQJbCqyHEodMPVpn9LKEfp4zGo3wInBDY6FYVFSVIx8MGV+6jFIJWMjSENaY1SmAdnevcHCwx3/80z9uAk+WHezFbfeTppV8WZDCBQFqpG5va1sBPvnkk8alB6wAsU3xelehCk6As0HRQDPk37t3j6tXr3Lp0qXAeeXZBSXa/1+n9QeOWTZavTGGwaDHtWvXKMuS2WyBMZaqMngPQmmm80UdXSVxQpJmOXmvT3Gy14giSkiUT7GlwDiPVR4hHWmq6eUpvV5Orx8CRrRK0VpivcR6wWB4icu7VzGVrV2voTP3einj8Sh0lr2nfPD++0wmJ7Vp6sXD/Do9oHt+0z2flS4EUBsQrAGCEIKyLLl//34TA9m+1o1zfFHcY7cjRLAnScKDBw94++23uXHjBg5PrtVK59nUAEvALrlqFDu01ozHY65evcrJyQRrXRNAM58VTKdzSmPo9XogBU4Kkiwl7eUc71coDIrwzMFgALkONt5egrUFOpFkSdJkewnuYYVOe3iXkGrJaGub4aUdTkP2eXQSAm+chSzrAZJLl3b41re+xcOH9zk6OmqG/PPqcd3v1f16a8l5z9lEFwKojdzoz07oS9OUP//z764ANQI0bu05Vu2op3h/+z1tas9/Ukrx+PFj3nvvPXq9Hm984U1cljZ21HWmp7Nc1TOfLxiOBnVZDFtbW1zevcrW6BI/3vsYISTeBQ1fqYST0ykyTVG9HtlgSKIk+c4u73zlq+w9+gHOeZwPnq1+f0Cu+wit8MJhXXDzSll3WkAmGfOFwbsSlQ25eft13vzyVxldusbxwQwpNYnO8N6iJEihKYoF8/mcb33rW3zta1/lRz/6ER+9/wHPD5+e227LDrz6u3t99Xh9x38RXQigAg1Q43FbEbp7926j7UcAWmsawLaH36iArbOlds1T7cqqqgqP59NPP+XRo0dcu3GdS8PBZx764wgQp0wfHBzgXRBZgvIX3hVAfInZbEqv1wvph3o9fFWipOLGrZv0hwNwEmGDPTVRKVLVkfXe1iGJdhmilyRIqUF6dNJjvHOZW7dfY3vnMvOygnrhiJBFJix20V4w+eTkBKUE29vbXLly5VygvmhIP4+jnicabKILAdRNBY9rhz558gQIJhkhBFIrTFk19zfKmFgu/wNngdmWUdvvasQJKTh+fsje3h7z6QyuQAhyia5aCNNMlpk/hPDNudiJqqoCF9Kd7z19wunzfRJR8oW3vwxeN3n6vZDMi5LBpQHD4ZAsy5gVC6SQbF+6RNa/hPQ9sAXCGpQLsQFCWjQCUBjvKW0VZg7oHOcEMsnpD8dcuX6TKzduQ9ZjcvycXjbEmHLZuZTAesfJ6SnjrS1Ojqa1nN1jvHWFYGZfLwIIghzrWQ824cHjgFXAxiYIbSZ4WVP+hTD4J0pjK4OzFdZVSAVluaA36HPvk49BeqQWqDShNCFYxXuBKW1Y3seCRCFRpDpj0BtyaWub2WSOrRyJSrGVQ6uURGcoGeI6JQotE5zxVIUhVSnFvOTZp884eHaAMxXOVnhrwIfFdSQ+xKs4GzYf9qKerw8SJTWJlqhqxrWthHFW8vzhB1wfZ/SU5fTkEKUUC6sYXbpCLx+QJXWAiU6YG8ez/VNuv/WL5Fu3EMkOveEVlBYgKnqZJJEW6cNqKf28z2C4BaqPSkao3pikP+b2F74EScZkVqDSrO5UDmMXVL7ES0tpFxSmYF6VGCspFrCYCbTcQuoepXFIoSkLQy/PQ304g3AW0aqTsC2zw0jqIC0cwoct74Up2F4KhEpI8yHuJXnlheCo0QwlhEBUAi8l1gVON5ucNvOWrK0oioosy0ilwvmlD97jcT5EOx0dngQLgQ72V2MVUkmsq5p36kRSVRbrKnSisNZzenLEaDTi9q0b7GyPg2NBheCVxnqAwTvfpBkPjNrVe4EzFqUF2num8xOoZiT1lO1v/9//lpuvfZHdq7f55MkTro8vUznH7s4YZ6tm9oFDcjSZ8aV/8C2Ortzk6eN7HO9/SjpwKOlQ3pHmQQ42XlIah3UClQ64fO0W453LXLl2h3QwZjKdYzwkOiVNEqSEylVUZUWRJMH8lQ04PDohFWnjYHHecuvGVZ4+K0L9a4G1Fa4yaCExtWwqgtgZ9AvvmskMcc5aqLNwsigKlAqmOmclxcLQy/svhZELAdR18l+cBBcVpjRNcbVtNZGKmS0a71Q7Er09P31vb68x6McpJlHp6sqv0Xa4u7vLzs5O4/eWfmnyaosVZ22ooil3gkIhqIoSYSq0lqAUZTFl79ljVNYD7zg9eU5/OGA+neF8hXfhe7IkQfmQt3S0NSZLvsATrXj68CPAI7VESkVlHaWxGK9Isj5Xr9/mxu3XSfMBaZpRFBXOQaoTsjzHGBfCF6XC+mBjdU4EcQGNsxUVJVo6eqMUpW8xmR5wfHhEVZSkW2NiBu8og0Zlamm5qStInFWmgmIb5l0luoft00zLfhFdCKAKXw8fbRnSB190TMnjvSfPMnCOLEs5PjxpQNpVjOLc8iRJuHHjBsPhkOl0GhSb1n0R6MPhkNFoRJIk3Llzh+vXr5MkSTDWK5p03m23LawHK94iCSvx+Sq4NsMwWOEqxf6zT5Fpj63Lt3jy8D7bl3c5PnQkUtLvD0iSLMx/koqqKhkNR7UVARazExbzCYtizmRe0e8PyYYp47zPaLzNa299GakTFqVlOlsgkrxRQG1VIaQOz1YpSnqMVcECUYVw2sX8lK1+j63tHkLAvXsPmU5OEN6FdTqcxeLxUuDEMjufqFcilMgglXa0+0jWWmaLAq1ThgPBaHjp3AD1Nl0IoHZ7HtSmJBG8JP28F1KW1YCNrsl4X5y2AqsZAIfDIa+//jpZlnH//v2V90WTk9aa4XDI1atXGY/H3Lx5swnIiBw1erraSlt3HwoTZrlKZxtZTsqwGIvAkWjB3t4BpYPrIkGkY3qZIoY0xg5hbciyJ5WiKMNM1uGlK3zpF77B84OnPN8/YD6Zc+XyNYbDIb3hkH5vSDraZj6ZsCgqqsrS12FYrYqSubPk/SEIhVIJKvGUpcOYisV8hsQzHg25NMrBLbj38V3+/M+DpyrUhw55UfuD2vtmWlw0tp2tGapc26beeyaTCZPJjEF/wttvDV4tjhqjdrwPmfo9cWECz9ZowGg04OjoiNJbrA1ZOnAxN1V7Pn0In1NKMBwOOTg44C//8i/QWgf/fT/MCHDONlNO0jRhd3eba9eucPny5dorZbBWtmTU1SitNnC7YE2EQFiLKxdIV5GnmjyReGsw1YSr2yNOyznvfvdP+C//+X9LkiZolSKEoj8c0Ut7qDRBB70qzDb1ICToPGd0+RZf+EqYsQCAc1jjqKqK/adHQTzxKUI5Dp8fhYDwJCPr5Tzb3yNNcoTQOCs5PTplNjlFS0eWQOFn/Ke//iEf/fD7lOWEhJRU91DeMp1P2bn1Gmma8OmnJ2idtoBaWwZcaAdqTtsFqhCCk5MTPvzwLvNZxT/7p5L9/f2XwsiFAGqiJLYqSVRe+/Y1vSzFOM/R0RHOVigtmE3nQWGxS+9P15YZfe2Hh4criyNEcSBy3piZutfrsbW1hRCCfj8EvMT05c6F5BVtUaEt256J6PdhblOeKEpj0VI0FoI001QLg0hDw54cH/K3f/MDbr32JrdvvcZkMuPu3R+hhGY4GnFpPGZnZwcpQWqFUpI0U0gR3KLloiJRmqpahufNijJMdBQebx2p1mRJEsrpwgIcx0enaJ2iZMpiNuVg7ylbgx7XXr/GH/2//57ne/e5eWObRA948PAZ81kF3qIlzOdTVJKFtO6VbX12DUrn8agGqCvXCArWcDhksVjQ74949913m2ChF9GFAOqKQb7Frfb2nnF6esrrr7/O9evX+Y9/9udMp9Ng2lkscyNF6vr+zwtLi96oLAtpKtdVmIjRyZ1yruOk3oegGuHD3HvrKuJSQ0J6hLNUpgAHRWHBmpDHNe8xHm/T6w1IdUpRVAFQh0fsPX2GThW9Xo+8n5PVvnyAVIR05o39WKcMBlCVhrIsWSwWSGicCUmWQZ7gxp7pdM7zgyMWsynYCq0S7r7/A7SouH5lG2lLisUM6V2Qr50NIxeEbC8reWNtff6sLbVrX405q4JDZA5+mbT5RXQhgCp8sMkFGdRivMN6ODzYB2d54403uH79Ot/+9reZ11MpEh3nlre9Tu2KaR8vJ8BBTHQRA4T7pKlGa9nYGcEFTiZXlwdqxwa0xYH4/nhsbJicqJVAJxJc1TgrXC0+JEmIS93a2uLD9z8gTTOuXbnK7nYAo60qnj9/TlFVLIqC0+lpKE8SOlWukmaUCGGPaR2nainKRUi1eXRcZzrMkFpz6eplFApvDP08ZX5yyOHzx/gyY//ZIwaqop8pikWBLRZo6eoZsQbvVtMQAfXqre0ad0CdZXBNTgTnXB09tqBYWPDq1ZJRo7/embKJAZXAl7/85TrbXp+DgwOstexubwUXZGvoaUeft8HUvgar06+992RZxvb2Nnmer2RaaYPvTKjgGmDGYykcThgW1Yx5OWMnF/Vkujp+wQlKB4vCotM+vXzEyemc4XCEtcF9qwQoETrBeDhiOMjRehupFLOqoKgTvy3mU8piaRWRUuKMwdlQniRJ+MIX3qiDVWTtuVIsZnM+fXSfJ4/vMz3Z49PHn7AnPLdvXCZzC2bPT8gTSSZTCiyFLTGFwAnVeJWMMSG3AHU6ocbZ4YMTBL065Df1tExksb+/xxfe/OLGpZO6dCGAGqdEeO8RKmnySEmdcHR01EQhVVVFUZQkiWaTU+08UQBYUYZgmXepC8Tu73iuDdz4jrYM5pwJK0SLkPTC+mWoIjJkzvOuzmySpMzmJVVcCKIqUSLMjxJC8OnJMToNuaCyfo/eaEie5wjhmU4U0jsKU4WpylJijGsyGzpLY/qp6szUpfFYV9LPNbs7Wxwf3GNy/Iwbb76GcAt6iaQ3HGKqwFFV7V7ytgorYLe/vbaTilgPOHx0DXOWo7brM2bAuXTpEv3+K2Twj3nfF4tFiOEsByRZD6FkPcffNlnkYuS5Z5mtwxO9UyBW8tacDYZw3qBEWBDCY3HerCSmiFO3nTcrlbtihmJzUIrFh+QXMpwPoIkBNQrvQ2dROqeXD5kVJ7iG49Z5/a3DmpI8zZhXBZOTY5CCrd1teoMBWZ4gvSNNNEpLlFAoresJeyHgRAS7fl1TlspZHj94jNIwGqZcvbrLw48FxWKCFAZrStB5nc5AILxCEVylzntca3UU58L3dQPd18nz7eM4ckYlN8/zV8uO+v33vhdSHSrZaN153qfXHzAYDZnNSkoTAn6zrMd0NqttjrXC4+VSmPcyACWab+JxzJziRHOfs+CMDcZ561AOpPXNhvNBg25V+jqwxmOFA18hrEXjQ2BGjAGtQxCrAsqiwOlg2FcI5kWBcwZfhUUvpPd4JJPZtLYTGxyeeZ2ySCnB+NKIfpbjpSBVOqx/Wq/EAhIpwvcVRcHhySmT07CCDMIGOfHwmPlkyiDvcXJ8zPXtcRixymBNUFkO86APuJh7f2VWRUjI4UXoYHiJFyJkQ9gQEBUnT7ZnYDR5vl5AFwKo2yOHVFOk8WgLfvKc+TEcGvBohM4xMsVUDudTRlsDyvmUUIWCuDjUUkbVy/n4dV5+KRKMMPQGGRKBsxZXVjz8+BNSLyiOTtDzsNzkPO8xHA7Rt66g0xSpNcXccTSfNx6v09PTJplElGV7qSD1p+TKI0tLqoYIa/BVCFL2pcGUltnklNPFKQ/ufcx0XjDsabytmC9KTN2RhNL08wE4hzQVOIuZLyhOpxhjOHj6jERnjZiktSbJcvDBBOdqS0CT10sKkIpcpyRaMDs5pTgx2BnkV4aYSuHyFBJNRc3ZpabXGzI7miF6GVKnLIqCREtsuQBX1aOUxCCoXIJ2HlmLPrbJprjaqWNMQ1yM7WXoQgB1OMqC8V2AcC4I6daj5hZkglM5s8IxmZ7iEaS2h/RnE010PUcrJBwSh1YKJQQVFlOVlKLk/3v3u+SZYmswRMmQP7XX65EOMpI8aYaouMJdmqbs7OyQttZiUkphU8XJwWNuXr3MsD/CWkciFFJqvK8ah0EsrzHBlHRSzbA2KIheBK1eCdkkw9AtFtVk2/Oegjq7dhKCSXoG8sEAF2c+tBbu8AiMtRTTEoHl+PCQx48fs//0Cdeu7gYHg/CI2ngfnB62KaNQJcY4jA3plfJEI4Sq0x0JJAolVFhTawN1MwJ2swKeRxcCqINhTi/NSGTI2yRrWWdrnFF5waISoCvSDIz1CGnxrQWh1wKzpjZwtfZIYVHC44TFeoOzhiuXR6RakmgZOooocc5j5jMwEj9PmYt6wTaZoLTgb07CUBpFDyGDknD92mVyLcmSnMorRKKhHhJdS2uezaqwWENhcLrOPCiDQb8JtKlXJtSSsHx5osmSFGNLyjrg2ftlIg2gmSGqlKKql7MEkIlG6ZSycCi5XF5HtAJ6tE4QTiGkxzlFaYvlPDVRm8CMWy5aZx0Oj/MKh2tmIgQvoVvJhhjb4mXXoe3ShQCqxIUGj3bMuuJPTyfMCsN0YVkYSJKQM0qIKDuuav7ropma34CWgLc4Z8AbtIJEh2AMq4BMoWScWl2xPdoK6XJkcA0aE0L6pITx9XHtHYsrXVuk1ExOnjcrWBvv0M4FT40PmUcEAfAnJyccHx4hpEbIYLt1QgbNWYRa2draQnhQ9SIT/bTmtkrhkVgPzlpMaSkLw2S2QOuwmnbe7zGfFcznYSZrJnoUVYktF+SZDo6ONGEwGNVhlgHYgqDJB+N8SWWDxcD6BWVhsN5jjcd5i3AmWDW8wyAxXpBGU5gIYX9dO2q0QETAvlIG/zQJC+lmSQ7O15mhF1gnmS0qjicFxgnStIdUQTkpbFkb6Ddr4O1rwnmkre2dUqClJkkhTxXDQQ8tHXkSlplMVcijenkrQYiQgc6isBaE8zgBqbIoFZejdFg8UglGW9uN+9Uaz6xa1FmzJaeLgnlpKSrDZDrj8eNHqCQhy8KKeflgSJJmpEmG0in/f3t3k9Q2DIBh+JNlO05Jp53pQdqLcKTeEc7ArjNJUyeBOLGlLpRAO/SHXcQ5qIsAAALLSURBVPiG99mw9QwvwpJt6eF+KCsSw1FZkxazTm0TT/tldfr44VOZkKTpcffs7z/Wjw8BjtPTfWA3jeq6cvJ0OkjK5Uj2OpSdrkNdlgNz2ThAQeWbrnnTqptlbUdpvx8U67acFzVJOq28BMXTJK58hlMe6CWNefzt9zAM4+Nk6rx+arXg3/d9ucfqRinp9Ex6p6yo7XbQw30JYqrK6W8KTxvMPnvNTs/DPUupfI1SVZWaqi5vyVeV0nHUGJNyUymqvEzc1JU2m1519bRdeIxRoQqqQzj9B0gKqYwoOZWnafshabVaKeZKi9n7MpGZjoptrVW/1sNh0nq91maz0XK5LPsQBklKiu1MdVPugetmphjKxGga9prSUV2sVcfTOVG5bMUzn8+VqzJ77vteUy6PKcsoeP7DfHp6td9uNGsqtU3QcrnUVdfq7u5Oi6tOi3fn0/bKctlhv5FirW/LXuv9qEOcq52VPVfbOiqko1IOSgo6pqBRtVqVQeFPoR6OWbvd7nR95V7312OW/iX87d4OeE1exTdTwP8QKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQKiwQ6kuEcK0QbhTC9aUv5a0KOedLX8PrF8KNpM+SbpXzl0tfzlvEiPoyXyXdnn7iAhhRYYERFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRYIFRZ+AuVM8pWpEKZiAAAAAElFTkSuQmCC
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<h3 id="A-segmentation-example">A segmentation example<a class="anchor-link" href="#A-segmentation-example">¶</a></h3>
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<p>Now we are going to look at the <a href="http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/">camvid dataset</a> (at least a small sample of it), where we have to predict the class of each pixel in an image. Each image in the 'images' subfolder has an equivalent in 'labels' that is its segmentations mask.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">camvid</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">CAMVID_TINY</span><span class="p">)</span>
<span class="n">path_lbl</span> <span class="o">=</span> <span class="n">camvid</span><span class="o">/</span><span class="s1">'labels'</span>
<span class="n">path_img</span> <span class="o">=</span> <span class="n">camvid</span><span class="o">/</span><span class="s1">'images'</span>
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<p>We read the classes in 'codes.txt' and the function maps each image filename with its corresponding mask filename.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">codes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">camvid</span><span class="o">/</span><span class="s1">'codes.txt'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
<span class="n">get_y_fn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">path_lbl</span><span class="o">/</span><span class="n">f</span><span class="s1">'</span><span class="si">{x.stem}</span><span class="s1">_P</span><span class="si">{x.suffix}</span><span class="s1">'</span>
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<p>The data block API allows us to quickly get everything in a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> and then we can have a look with <code>show_batch</code>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">SegmentationItemList</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path_img</span><span class="p">)</span>
<span class="o">.</span><span class="n">split_by_rand_pct</span><span class="p">()</span>
<span class="o">.</span><span class="n">label_from_func</span><span class="p">(</span><span class="n">get_y_fn</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">codes</span><span class="p">)</span>
<span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">get_transforms</span><span class="p">(),</span> <span class="n">tfm_y</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">128</span><span class="p">)</span>
<span class="o">.</span><span class="n">databunch</span><span class="p">(</span><span class="n">bs</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">camvid</span><span class="p">)</span>
<span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">imagenet_stats</span><span class="p">))</span>
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<p>As before, we load our model, export the <a href="/basic_train.html#Learner"><code>Learner</code></a> then create a new one with <a href="/basic_train.html#load_learner"><code>load_learner</code></a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">unet_learner</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mini_train'</span><span class="p">);</span>
<span class="n">learn</span><span class="o">.</span><span class="n">export</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">load_learner</span><span class="p">(</span><span class="n">camvid</span><span class="p">)</span>
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<p>And now we can a prediction on an image.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_ds</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">);</span>
</pre></div>
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<p>To visualize the predictions, we can use the <a href="/vision.image.html#Image.show"><code>Image.show</code></a> method.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
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<h2 id="Text">Text<a class="anchor-link" href="#Text">¶</a></h2>
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<p>Next application is text, so let's start by importing everything we'll need.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai.text</span> <span class="k">import</span> <span class="o">*</span>
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<h3 id="Language-modelling">Language modelling<a class="anchor-link" href="#Language-modelling">¶</a></h3>
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<p>First let's look a how to get a language model ready for inference. Since we'll load the model trained in the <a href="/tutorial.data.html">visualize data tutorial</a>, we load the <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> used there.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">imdb</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">IMDB_SAMPLE</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_lm</span> <span class="o">=</span> <span class="n">load_data</span><span class="p">(</span><span class="n">imdb</span><span class="p">)</span>
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<p>Like in vision, we just have to type <code>learn.export()</code> after loading our pretrained model to save all the information inside the <a href="/basic_train.html#Learner"><code>Learner</code></a> we'll need. In this case, this includes all the vocabulary we created. The only difference is that we will specify a filename, since we have several model in the same path (language model and classifier).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">language_model_learner</span><span class="p">(</span><span class="n">data_lm</span><span class="p">,</span> <span class="n">AWD_LSTM</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'mini_train_lm'</span><span class="p">,</span> <span class="n">with_opt</span><span class="o">=</span><span class="kc">False</span><span class="p">);</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">file</span> <span class="o">=</span> <span class="s1">'export_lm.pkl'</span><span class="p">)</span>
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<p>Now let's define our inference learner.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">load_learner</span><span class="p">(</span><span class="n">imdb</span><span class="p">,</span> <span class="n">file</span> <span class="o">=</span> <span class="s1">'export_lm.pkl'</span><span class="p">)</span>
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<p>Then we can predict with the usual method, here we can specify how many words we want the model to predict.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s1">'This is a simple test of'</span><span class="p">,</span> <span class="n">n_words</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
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<pre>'This is a simple test of the critique made out of the concerns on the consequences of it and the called Sub Cooper comparisons'</pre>
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<p>You can also use beam search to generate text.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">beam_search</span><span class="p">(</span><span class="s1">'This is a simple test of'</span><span class="p">,</span> <span class="n">n_words</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">beam_sz</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
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<pre>'This is a simple test of This is a simple test of the quality of the English version of the English version . \n \n The English version is'</pre>
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