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---
title: basic_train
keywords: fastai
sidebar: home_sidebar
summary: "Learner class and training loop"
---
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<h2 id="Basic-training-functionality">Basic training functionality<a class="anchor-link" href="#Basic-training-functionality">¶</a></h2>
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<p><a href="/basic_train.html#basic_train"><code>basic_train</code></a> wraps together the data (in a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> object) with a PyTorch model to define a <a href="/basic_train.html#Learner"><code>Learner</code></a> object. Here the basic training loop is defined for the <a href="/basic_train.html#fit"><code>fit</code></a> method. The <a href="/basic_train.html#Learner"><code>Learner</code></a> object is the entry point of most of the <a href="/callback.html#Callback"><code>Callback</code></a> objects that will customize this training loop in different ways. Some of the most commonly used customizations are available through the <a href="/train.html#train"><code>train</code></a> module, notably:</p>
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<li><a href="/train.html#lr_find"><code>Learner.lr_find</code></a> will launch an LR range test that will help you select a good learning rate.</li>
<li><a href="/train.html#fit_one_cycle"><code>Learner.fit_one_cycle</code></a> will launch a training using the 1cycle policy to help you train your model faster.</li>
<li><a href="/train.html#to_fp16"><code>Learner.to_fp16</code></a> will convert your model to half precision and help you launch a training in mixed precision.</li>
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<h2 id="Learner" class="doc_header"><code>class</code> <code>Learner</code><a href="https://github.com/fastai/fastai/blob/master/fastai/basic_train.py#L144" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Learner-pytest" style="float:right; padding-right:10px">[test]</a></h2><blockquote><p><code>Learner</code>(<strong><code>data</code></strong>:<a href="/basic_data.html#DataBunch"><code>DataBunch</code></a>, <strong><code>model</code></strong>:<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Module</code></a>, <strong><code>opt_func</code></strong>:<code>Callable</code>=<strong><em><code>'Adam'</code></em></strong>, <strong><code>loss_func</code></strong>:<code>Callable</code>=<strong><em><code>None</code></em></strong>, <strong><code>metrics</code></strong>:<code>Collection</code>[<code>Callable</code>]=<strong><em><code>None</code></em></strong>, <strong><code>true_wd</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>bn_wd</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>wd</code></strong>:<code>Floats</code>=<strong><em><code>0.01</code></em></strong>, <strong><code>train_bn</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>path</code></strong>:<code>str</code>=<strong><em><code>None</code></em></strong>, <strong><code>model_dir</code></strong>:<code>PathOrStr</code>=<strong><em><code>'models'</code></em></strong>, <strong><code>callback_fns</code></strong>:<code>Collection</code>[<code>Callable</code>]=<strong><em><code>None</code></em></strong>, <strong><code>callbacks</code></strong>:<code>Collection</code>[<a href="/callback.html#Callback"><code>Callback</code></a>]=<strong><em><code><factory></code></em></strong>, <strong><code>layer_groups</code></strong>:<code>ModuleList</code>=<strong><em><code>None</code></em></strong>, <strong><code>add_time</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>silent</code></strong>:<code>bool</code>=<strong><em><code>None</code></em></strong>, <strong><code>cb_fns_registered</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>)</p>
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<div class="collapse" id="Learner-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Learner-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>Learner</code>:</p><p>Some other tests where <code>Learner</code> is used:</p><ul><li><code>pytest -sv tests/test_basic_train.py::test_memory</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_basic_train.py#L213" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Trainer for <code>model</code> using <code>data</code> to minimize <code>loss_func</code> with optimizer <code>opt_func</code>.</p>
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<p>The main purpose of <a href="/basic_train.html#Learner"><code>Learner</code></a> is to train <code>model</code> using <a href="/basic_train.html#Learner.fit"><code>Learner.fit</code></a>. After every epoch, all <em>metrics</em> will be printed and also made available to callbacks.</p>
<p>The default weight decay will be <code>wd</code>, which will be handled using the method from <a href="https://arxiv.org/abs/1711.05101">Fixing Weight Decay Regularization in Adam</a> if <code>true_wd</code> is set (otherwise it's L2 regularization). If <code>true_wd</code> is set it will affect all optimizers, not only Adam. If <code>bn_wd</code> is <code>False</code>, then weight decay will be removed from batchnorm layers, as recommended in <a href="https://arxiv.org/abs/1706.02677">Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour</a>. If <code>train_bn</code>, batchnorm layer learnable params are trained even for frozen layer groups.</p>
<p>To use <a href="#Discriminative-layer-training">discriminative layer training</a>, pass a list of <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>nn.Module</code></a> as <code>layer_groups</code>; each <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>nn.Module</code></a> will be used to customize the optimization of the corresponding layer group.</p>
<p>If <code>path</code> is provided, all the model files created will be saved in <code>path</code>/<code>model_dir</code>; if not, then they will be saved in <code>data.path</code>/<code>model_dir</code>.</p>
<p>You can pass a list of <a href="/callback.html#callback"><code>callback</code></a>s that you have already created, or (more commonly) simply pass a list of callback functions to <code>callback_fns</code> and each function will be called (passing <code>self</code>) on object initialization, with the results stored as callback objects. For a walk-through, see the <a href="/training.html">training overview</a> page. You may also want to use an <a href="applications.html">application</a> specific model. For example, if you are dealing with a vision dataset, here the MNIST, you might want to use the <a href="/vision.learner.html#cnn_learner"><code>cnn_learner</code></a> method:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</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_SAMPLE</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">ImageDataBunch</span><span class="o">.</span><span class="n">from_folder</span><span class="p">(</span><span class="n">path</span><span class="p">)</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">metrics</span><span class="o">=</span><span class="n">accuracy</span><span class="p">)</span>
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<h3 id="Model-fitting-methods">Model fitting methods<a class="anchor-link" href="#Model-fitting-methods">¶</a></h3>
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<h4 id="lr_find" class="doc_header"><code>lr_find</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L24" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#lr_find-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>lr_find</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>start_lr</code></strong>:<code>Floats</code>=<strong><em><code>1e-07</code></em></strong>, <strong><code>end_lr</code></strong>:<code>Floats</code>=<strong><em><code>10</code></em></strong>, <strong><code>num_it</code></strong>:<code>int</code>=<strong><em><code>100</code></em></strong>, <strong><code>stop_div</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>wd</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="lr_find-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#lr_find-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>lr_find</code>:</p><ul><li><code>pytest -sv tests/test_train.py::test_lr_find</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_train.py#L16" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_lrfind</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L84" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Explore lr from <code>start_lr</code> to <code>end_lr</code> over <code>num_it</code> iterations in <code>learn</code>. If <code>stop_div</code>, stops when loss diverges.</p>
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<p>Runs the learning rate finder defined in <a href="/callbacks.lr_finder.html#LRFinder"><code>LRFinder</code></a>, as discussed in <a href="https://arxiv.org/abs/1506.01186">Cyclical Learning Rates for Training Neural Networks</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">lr_find</span><span class="p">()</span>
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<pre>LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">recorder</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
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<h4 id="Learner.fit" class="doc_header"><code>fit</code><a href="https://github.com/fastai/fastai/blob/master/fastai/basic_train.py#L193" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Learner-fit-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>fit</code>(<strong><code>epochs</code></strong>:<code>int</code>, <strong><code>lr</code></strong>:<code>Union</code>[<code>float</code>, <code>Collection</code>[<code>float</code>], <code>slice</code>]=<strong><em><code>slice(None, 0.003, None)</code></em></strong>, <strong><code>wd</code></strong>:<code>Floats</code>=<strong><em><code>None</code></em></strong>, <strong><code>callbacks</code></strong>:<code>Collection</code>[<a href="/callback.html#Callback"><code>Callback</code></a>]=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="Learner-fit-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Learner-fit-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>fit</code>:</p><ul><li><code>pytest -sv tests/test_train.py::test_fit</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_train.py#L28" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>fit</code> is used:</p><ul><li><code>pytest -sv tests/test_basic_train.py::test_destroy</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_basic_train.py#L170" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Fit the model on this learner with <code>lr</code> learning rate, <code>wd</code> weight decay for <code>epochs</code> with <code>callbacks</code>.</p>
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<p>Uses <a href="#Discriminative-layer-training">discriminative layer training</a> if multiple learning rates or weight decay values are passed. To control training behaviour, use the <a href="/callback.html#callback"><code>callback</code></a> system or one or more of the pre-defined <a href="/callbacks.html#callbacks"><code>callbacks</code></a>.</p>
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<td>1</td>
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<h4 id="fit_one_cycle" class="doc_header"><code>fit_one_cycle</code><a href="https://github.com/fastai/fastai/blob/master/fastai/train.py#L14" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#fit_one_cycle-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>fit_one_cycle</code>(<strong><code>learn</code></strong>:<a href="/basic_train.html#Learner"><code>Learner</code></a>, <strong><code>cyc_len</code></strong>:<code>int</code>, <strong><code>max_lr</code></strong>:<code>Union</code>[<code>float</code>, <code>Collection</code>[<code>float</code>], <code>slice</code>]=<strong><em><code>slice(None, 0.003, None)</code></em></strong>, <strong><code>moms</code></strong>:<code>Point</code>=<strong><em><code>(0.95, 0.85)</code></em></strong>, <strong><code>div_factor</code></strong>:<code>float</code>=<strong><em><code>25.0</code></em></strong>, <strong><code>pct_start</code></strong>:<code>float</code>=<strong><em><code>0.3</code></em></strong>, <strong><code>final_div</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>, <strong><code>wd</code></strong>:<code>float</code>=<strong><em><code>None</code></em></strong>, <strong><code>callbacks</code></strong>:<code>Optional</code>[<code>Collection</code>[<a href="/callback.html#Callback"><code>Callback</code></a>]]=<strong><em><code>None</code></em></strong>, <strong><code>tot_epochs</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>, <strong><code>start_epoch</code></strong>:<code>int</code>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="fit_one_cycle-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#fit_one_cycle-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>fit_one_cycle</code>:</p><ul><li><code>pytest -sv tests/test_train.py::test_fit_one_cycle</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_train.py#L36" class="source_link" style="float:right">[source]</a></li></ul><p>Some other tests where <code>fit_one_cycle</code> is used:</p><ul><li><code>pytest -sv tests/test_tabular_train.py::test_empty_cont</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_tabular_train.py#L71" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_text_train.py::test_qrnn_works_if_split_fn_provided</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_text_train.py#L73" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_text_train.py::test_qrnn_works_with_no_split</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_text_train.py#L61" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Fit a model following the 1cycle policy.</p>
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<p>Use cycle length <code>cyc_len</code>, a per cycle maximal learning rate <code>max_lr</code>, momentum <code>moms</code>, division factor <code>div_factor</code>, weight decay <code>wd</code>, and optional callbacks <a href="/callbacks.html#callbacks"><code>callbacks</code></a>. Uses the <a href="/callbacks.one_cycle.html#OneCycleScheduler"><code>OneCycleScheduler</code></a> callback. Please refer to <a href="/callbacks.one_cycle.html#What-is-1cycle?">What is 1-cycle</a> for a conceptual background of 1-cycle training policy and more technical details on what do the method's arguments do.</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">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
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<td>1</td>
<td>0.075838</td>
<td>0.061869</td>
<td>0.979882</td>
<td>00:05</td>
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<h3 id="See-results">See results<a class="anchor-link" href="#See-results">¶</a></h3>
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<h4 id="Learner.predict" class="doc_header"><code>predict</code><a href="https://github.com/fastai/fastai/blob/master/fastai/basic_train.py#L372" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Learner-predict-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>predict</code>(<strong><code>item</code></strong>:<a href="/core.html#ItemBase"><code>ItemBase</code></a>, <strong><code>return_x</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>batch_first</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>with_dropout</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>
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<div class="collapse" id="Learner-predict-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Learner-predict-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>predict</code>:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_models_meta</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L89" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_preds</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L63" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Return predicted class, label and probabilities for <code>item</code>.</p>
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<p><code>predict</code> can be used to get a single prediction from the trained learner on one specific piece of data you are interested in.</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">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>
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<pre>(Image (3, 28, 28), <fastai.core.Category at 0x7fb0e0dee1d0>)</pre>
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<p>Each element of the dataset is a tuple, where the first element is the data itself, while the second element is the target label. So to get the data, we need to index one more time.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">learn</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>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span>
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<img src="data:image/png;base64,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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred</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">data</span><span class="p">)</span>
<span class="n">pred</span>
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<pre>(<fastai.core.Category at 0x7fb0e02f29b0>, tensor(0), tensor([0.5748, 0.4252]))</pre>
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<p>The first two elements of the tuple are, respectively, the predicted class and label. Label here is essentially an internal representation of each class, since class name is a string and cannot be used in computation. To check what each label corresponds to, run:</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">data</span><span class="o">.</span><span class="n">classes</span>
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<pre>['3', '7']</pre>
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<p>So category 0 is 3 while category 1 is 7.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">probs</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
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<p>The last element in the tuple is the predicted probabilities. For a categorization dataset, the number of probabilities returned is the same as the number of classes; <code>probs[i]</code> is the probability that the <code>item</code> belongs to <code>learn.data.classes[i]</code>.</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">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>
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<img src="data:image/png;base64,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<p>You could always check yourself if the probabilities given make sense.</p>
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<h4 id="Learner.get_preds" class="doc_header"><code>get_preds</code><a href="https://github.com/fastai/fastai/blob/master/fastai/basic_train.py#L333" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Learner-get_preds-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>get_preds</code>(<strong><code>ds_type</code></strong>:<a href="/basic_data.html#DatasetType"><code>DatasetType</code></a>=<strong><em><code><DatasetType.Valid: 2></code></em></strong>, <strong><code>activ</code></strong>:<a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module"><code>Module</code></a>=<strong><em><code>None</code></em></strong>, <strong><code>with_loss</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>n_batch</code></strong>:<code>Optional</code>[<code>int</code>]=<strong><em><code>None</code></em></strong>, <strong><code>pbar</code></strong>:<code>Union</code>[<code>MasterBar</code>, <code>ProgressBar</code>, <code>NoneType</code>]=<strong><em><code>None</code></em></strong>) → <code>List</code>[<code>Tensor</code>]</p>
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<div class="collapse" id="Learner-get_preds-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Learner-get_preds-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>get_preds</code>:</p><ul><li><code>pytest -sv tests/test_basic_train.py::test_get_preds</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_basic_train.py#L32" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Return predictions and targets on <code>ds_type</code> dataset.</p>
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<p>It will run inference using the learner on all the data in the <code>ds_type</code> dataset and return the predictions; if <code>n_batch</code> is not specified, it will run the predictions on the default batch size. If <code>with_loss</code>, it will also return the loss on each prediction.</p>
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<p>Here is how you check the default batch size.</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">data</span><span class="o">.</span><span class="n">batch_size</span>
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<pre>64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span> <span class="o">=</span> <span class="n">learn</span><span class="o">.</span><span class="n">get_preds</span><span class="p">()</span>
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<pre>[tensor([[9.9925e-01, 7.4895e-04],
[9.8333e-01, 1.6672e-02],
[9.9996e-01, 3.8919e-05],
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[1.6180e-04, 9.9984e-01],
[2.5164e-02, 9.7484e-01],
[1.8179e-02, 9.8182e-01]]), tensor([0, 0, 0, ..., 1, 1, 1])]</pre>
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<p>The first element of the tuple is a tensor that contains all the predictions.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<pre>tensor([[9.9925e-01, 7.4895e-04],
[9.8333e-01, 1.6672e-02],
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[1.6180e-04, 9.9984e-01],
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[1.8179e-02, 9.8182e-01]])</pre>
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<p>While the second element of the tuple is a tensor that contains all the target labels.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
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<pre>tensor([0, 0, 0, ..., 1, 1, 1])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">preds</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
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<pre>tensor(0)</pre>
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<p>For more details about what each number mean, refer to the documentation of <a href="/basic_train.html#predict"><code>predict</code></a>.</p>
<p>Since <a href="/basic_train.html#get_preds"><code>get_preds</code></a> gets predictions on all the data in the <code>ds_type</code> dataset, here the number of predictions will be equal to the number of data in the validation dataset.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">learn</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">valid_ds</span><span class="p">)</span>
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<pre>2038</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
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<pre>(2038, 2038)</pre>
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<p>To get predictions on the entire training dataset, simply set the <code>ds_type</code> argument accordingly.</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">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">Train</span><span class="p">)</span>
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<pre>[tensor([[9.9801e-01, 1.9876e-03],
[1.7900e-06, 1.0000e+00],
[1.3191e-03, 9.9868e-01],
...,
[9.9991e-01, 8.6866e-05],
[1.6420e-04, 9.9984e-01],
[2.2937e-03, 9.9771e-01]]), tensor([0, 1, 1, ..., 0, 1, 1])]</pre>
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<p>To also get prediction loss along with the predictions and the targets, set <code>with_loss=True</code> in the arguments.</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">get_preds</span><span class="p">(</span><span class="n">with_loss</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>[tensor([[9.9925e-01, 7.4895e-04],
[9.8333e-01, 1.6672e-02],
[9.9996e-01, 3.8919e-05],
...,
[1.6180e-04, 9.9984e-01],
[2.5164e-02, 9.7484e-01],
[1.8179e-02, 9.8182e-01]]),
tensor([0, 0, 0, ..., 1, 1, 1]),
tensor([7.4911e-04, 1.6813e-02, 3.8624e-05, ..., 1.6165e-04, 2.5486e-02,
1.8347e-02])]</pre>
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<p>Note that the third tensor in the output tuple contains the losses.</p>
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<h4 id="Learner.validate" class="doc_header"><code>validate</code><a href="https://github.com/fastai/fastai/blob/master/fastai/basic_train.py#L387" class="source_link" style="float:right">[source]</a><a class="source_link" data-toggle="collapse" data-target="#Learner-validate-pytest" style="float:right; padding-right:10px">[test]</a></h4><blockquote><p><code>validate</code>(<strong><code>dl</code></strong>=<strong><em><code>None</code></em></strong>, <strong><code>callbacks</code></strong>=<strong><em><code>None</code></em></strong>, <strong><code>metrics</code></strong>=<strong><em><code>None</code></em></strong>)</p>
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<div class="collapse" id="Learner-validate-pytest"><div class="card card-body pytest_card"><a type="button" data-toggle="collapse" data-target="#Learner-validate-pytest" class="close" aria-label="Close"><span aria-hidden="true">×</span></a><p>Tests found for <code>validate</code>:</p><ul><li><code>pytest -sv tests/test_collab_train.py::test_val_loss</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_collab_train.py#L16" class="source_link" style="float:right">[source]</a></li><li><code>pytest -sv tests/test_text_train.py::test_val_loss</code> <a href="https://github.com/fastai/fastai/blob/master/tests/test_text_train.py#L56" class="source_link" style="float:right">[source]</a></li></ul><p>To run tests please refer to this <a href="/dev/test.html#quick-guide">guide</a>.</p></div></div><p>Validate on <code>dl</code> with potential <code>callbacks</code> and <code>metrics</code>.</p>
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<p>Return the calculated loss and the metrics of the current model on the given data loader <code>dl</code>. The default data loader <code>dl</code> is the validation dataloader.</p>
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<p>You can check the default metrics of the learner using:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">str</span><span class="p">(</span><span class="n">learn</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span>
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