Usage of optimizers
An optimizer is one of the two arguments required for compiling a Keras model:
model = Sequential()
model.add(Dense(20, 64, init='uniform'))
model.add(Activation('tanh'))
model.add(Activation('softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
You can either instantiate an optimizer before passing it to model.compile()
, as in the above example, or you can call it by its name. In the latter case, the default parameters for the optimizer will be used.
# pass optimizer by name: default parameters will be used
model.compile(loss='mean_squared_error', optimizer='sgd')
Base class
keras.optimizers.Optimizer(**kwargs)
All optimizers descended from this class support the following keyword argument:
- clipnorm: float >= 0.
Note: this is base class for building optimizers, not an actual optimizer that can be used for training models.
SGD
keras.optimizers.SGD(lr=0.01, momentum=0., decay=0., nesterov=False)
Arguments:
- lr: float >= 0. Learning rate.
- momentum: float >= 0. Parameter updates momentum.
- decay: float >= 0. Learning rate decay over each update.
- nesterov: boolean. Whether to apply Nesterov momentum.
Adagrad
keras.optimizers.Adagrad(lr=0.01, epsilon=1e-6)
It is recommended to leave the parameters of this optimizer at their default values.
Arguments:
- lr: float >= 0. Learning rate.
- epsilon: float >= 0.
Adadelta
keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-6)
It is recommended to leave the parameters of this optimizer at their default values.
Arguments:
- lr: float >= 0. Learning rate. It is recommended to leave it at the default value.
- rho: float >= 0.
- epsilon: float >= 0. Fuzz factor.
For more info, see "Adadelta: an adaptive learning rate method" by Matthew Zeiler.
RMSprop
keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-6)
It is recommended to leave the parameters of this optimizer at their default values.
Arguments:
- lr: float >= 0. Learning rate.
- rho: float >= 0.
- epsilon: float >= 0. Fuzz factor.
Adam
keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
Adam optimizer, proposed by Kingma and Lei Ba in Adam: A Method For Stochastic Optimization. Default parameters are those suggested in the paper.
Arguments:
- lr: float >= 0. Learning rate.
- beta_1, beta_2: floats, 0 < beta < 1. Generally close to 1.
- epsilon: float >= 0. Fuzz factor.