ReduceLROnPlateau

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form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> <span class="k-location-slug-pointer">►</span> <a href='/api/'>Keras 3 API documentation</a> / <a href='/api/callbacks/'>Callbacks API</a> / ReduceLROnPlateau </div> <div class='k-content'> <h1 id="reducelronplateau">ReduceLROnPlateau</h1> <p><span style="float:right;"><a href="https://github.com/keras-team/keras/tree/v3.6.0/keras/src/callbacks/reduce_lr_on_plateau.py#L11">[source]</a></span></p> <h3 id="reducelronplateau-class"><code>ReduceLROnPlateau</code> class</h3> <div class="codehilite"><pre><span></span><code><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span> <span class="n">monitor</span><span class="o">=</span><span class="s2">"val_loss"</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">,</span> <span class="n">cooldown</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">min_lr</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> </code></pre></div> <p>Reduce learning rate when a metric has stopped improving.</p> <p>Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.</p> <p><strong>Example</strong></p> <div class="codehilite"><pre><span></span><code><span class="n">reduce_lr</span> <span class="o">=</span> <span class="n">ReduceLROnPlateau</span><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="s1">'val_loss'</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">min_lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">reduce_lr</span><span class="p">])</span> </code></pre></div> <p><strong>Arguments</strong></p> <ul> <li><strong>monitor</strong>: String. Quantity to be monitored.</li> <li><strong>factor</strong>: Float. Factor by which the learning rate will be reduced. <code>new_lr = lr * factor</code>.</li> <li><strong>patience</strong>: Integer. Number of epochs with no improvement after which learning rate will be reduced.</li> <li><strong>verbose</strong>: Integer. 0: quiet, 1: update messages.</li> <li><strong>mode</strong>: String. One of <code>{'auto', 'min', 'max'}</code>. In <code>'min'</code> mode, the learning rate will be reduced when the quantity monitored has stopped decreasing; in <code>'max'</code> mode it will be reduced when the quantity monitored has stopped increasing; in <code>'auto'</code> mode, the direction is automatically inferred from the name of the monitored quantity.</li> <li><strong>min_delta</strong>: Float. Threshold for measuring the new optimum, to only focus on significant changes.</li> <li><strong>cooldown</strong>: Integer. Number of epochs to wait before resuming normal operation after the learning rate has been reduced.</li> <li><strong>min_lr</strong>: Float. Lower bound on the learning rate.</li> </ul> <hr /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#reducelronplateau'>ReduceLROnPlateau</a> </div> <div class='k-outline-depth-3'> <a href='#reducelronplateau-class'><code>ReduceLROnPlateau</code> class</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>

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ReduceLROnPlateau