Usage of optimizers

An optimizer is one of the two arguments required for compiling a Keras model:

model = Sequential()
model.add(Dense(64, init='uniform', input_dim=10))
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')

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SGD

keras.optimizers.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)

Stochastic gradient descent, with support for momentum, learning rate decay, and Nesterov momentum.

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.

[source]

RMSprop

keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08)

RMSProp optimizer.

It is recommended to leave the parameters of this optimizer at their default values (except the learning rate, which can be freely tuned).

This optimizer is usually a good choice for recurrent neural networks.

Arguments

  • lr: float >= 0. Learning rate.
  • rho: float >= 0.
  • epsilon: float >= 0. Fuzz factor.

[source]

Adagrad

keras.optimizers.Adagrad(lr=0.01, epsilon=1e-08)

Adagrad optimizer.

It is recommended to leave the parameters of this optimizer at their default values.

Arguments

  • lr: float >= 0. Learning rate.
  • epsilon: float >= 0.

[source]

Adadelta

keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-08)

Adadelta optimizer.

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.

References

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Adam

keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

Adam optimizer.

Default parameters follow those provided in the original paper.

Arguments

  • lr: float >= 0. Learning rate.
  • beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1.
  • epsilon: float >= 0. Fuzz factor.

References

[source]

Adamax

keras.optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

Adamax optimizer from Adam paper's Section 7. It is a variant of Adam based on the infinity norm.

Default parameters follow those provided 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.

References