Optimizers

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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'> ► <a href='/api/'>Keras 3 API documentation</a> / Optimizers </div> <div class='k-content'> <h1 id="optimizers">Optimizers</h1> <h2 id="available-optimizers">Available optimizers</h2> <ul> <li><a href="/api/optimizers/sgd/">SGD</a></li> <li><a href="/api/optimizers/rmsprop/">RMSprop</a></li> <li><a href="/api/optimizers/adam/">Adam</a></li> <li><a href="/api/optimizers/adamw/">AdamW</a></li> <li><a href="/api/optimizers/adadelta/">Adadelta</a></li> <li><a href="/api/optimizers/adagrad/">Adagrad</a></li> <li><a href="/api/optimizers/adamax/">Adamax</a></li> <li><a href="/api/optimizers/adafactor/">Adafactor</a></li> <li><a href="/api/optimizers/Nadam/">Nadam</a></li> <li><a href="/api/optimizers/ftrl/">Ftrl</a></li> <li><a href="/api/optimizers/lion/">Lion</a></li> <li><a href="/api/optimizers/lamb/">Lamb</a></li> <li><a href="/api/optimizers/loss_scale_optimizer/">Loss Scale Optimizer</a></li> </ul> <hr /> <h2 id="usage-with-compile-amp-fit">Usage with <code>compile()</code> & <code>fit()</code></h2> An optimizer is one of the two arguments required for compiling a Keras model: <div class="codehilite"><pre><code>import keras from keras import layers model = keras.Sequential() model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,))) model.add(layers.Activation('softmax')) opt = keras.optimizers.Adam(learning_rate=0.01) model.compile(loss='categorical_crossentropy', optimizer=opt) </code></pre></div> You can either instantiate an optimizer before passing it to <code>model.compile()</code> , as in the above example, or you can pass it by its string identifier. In the latter case, the default parameters for the optimizer will be used. <div class="codehilite"><pre><code># pass optimizer by name: default parameters will be used model.compile(loss='categorical_crossentropy', optimizer='adam') </code></pre></div> <hr /> <h2 id="learning-rate-decay--scheduling">Learning rate decay / scheduling</h2> You can use a <a href="/api/optimizers/learning_rate_schedules">learning rate schedule</a> to modulate how the learning rate of your optimizer changes over time: <div class="codehilite"><pre><code>lr_schedule = keras.optimizers.schedules.ExponentialDecay( initial_learning_rate=1e-2, decay_steps=10000, decay_rate=0.9) optimizer = keras.optimizers.SGD(learning_rate=lr_schedule) </code></pre></div> Check out <a href="/api/optimizers/learning_rate_schedules">the learning rate schedule API documentation</a> for a list of available schedules. <hr /> <h2 id="base-optimizer-api">Base Optimizer API</h2> These methods and attributes are common to all Keras optimizers. <a href="https://github.com/keras-team/keras/tree/v3.6.0/keras/src/optimizers/optimizer.py#L21">[source]</a> <h3 id="optimizer-class"><code>Optimizer</code> class</h3> <div class="codehilite"><pre><code>keras.optimizers.Optimizer() </code></pre></div> Abstract optimizer base class. If you intend to create your own optimization algorithm, please inherit from this class and override the following methods: <ul> <li><code>build</code>: Create your optimizer-related variables, such as momentum variables in the SGD optimizer.</li> <li><code>update_step</code>: Implement your optimizer's variable updating logic.</li> <li><code>get_config</code>: serialization of the optimizer.</li> </ul> Example <div class="codehilite"><pre><code>class SGD(Optimizer): def __init__(self, **kwargs): super().__init__(**kwargs) self.momentum = 0.9 def build(self, variables): super().build(variables) self.momentums = [] for variable in variables: self.momentums.append( self.add_variable_from_reference( reference_variable=variable, name="momentum" ) ) def update_step(self, gradient, variable, learning_rate): learning_rate = ops.cast(learning_rate, variable.dtype) gradient = ops.cast(gradient, variable.dtype) m = self.momentums[self._get_variable_index(variable)] self.assign( m, ops.subtract( ops.multiply(m, ops.cast(self.momentum, variable.dtype)), ops.multiply(gradient, learning_rate), ), ) self.assign_add(variable, m) def get_config(self): config = super().get_config() config.update( { "momentum": self.momentum, "nesterov": self.nesterov, } ) return config </code></pre></div> <hr /> <a href="https://github.com/keras-team/keras/tree/v3.6.0/keras/src/optimizers/base_optimizer.py#L342">[source]</a> <h3 id="applygradients-method"><code>apply_gradients</code> method</h3> <div class="codehilite"><pre><code>Optimizer.apply_gradients(grads_and_vars) </code></pre></div> <hr /> <h3 id="variables-property"><code>variables</code> property</h3> <div class="codehilite"><pre><code>keras.optimizers.Optimizer.variables </code></pre></div> <hr /> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#optimizers'>Optimizers</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#available-optimizers'>Available optimizers</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#usage-with-compile-amp-fit'>Usage with <code>compile()</code> & <code>fit()</code></a> </div> <div class='k-outline-depth-2'> ◆ <a href='#learning-rate-decay--scheduling'>Learning rate decay / scheduling</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#base-optimizer-api'>Base Optimizer API</a> </div> <div class='k-outline-depth-3'> <a href='#optimizer-class'><code>Optimizer</code> class</a> </div> <div class='k-outline-depth-3'> <a href='#applygradients-method'><code>apply_gradients</code> method</a> </div> <div class='k-outline-depth-3'> <a href='#variables-property'><code>variables</code> property</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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Optimizers