Description Usage Arguments Author(s) References Examples
Optimization functions to use in compiling a keras model. See keras_compile()
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | SGD(lr = 0.01, momentum = 0, decay = 0, nesterov = FALSE,
clipnorm = -1, clipvalue = -1)
RMSprop(lr = 0.001, rho = 0.9, epsilon = 1e-08, decay = 0,
clipnorm = -1, clipvalue = -1)
Adagrad(lr = 0.01, epsilon = 1e-08, decay = 0, clipnorm = -1,
clipvalue = -1)
Adadelta(lr = 1, rho = 0.95, epsilon = 1e-08, decay = 0,
clipnorm = -1, clipvalue = -1)
Adam(lr = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08,
decay = 0, clipnorm = -1, clipvalue = -1)
Adamax(lr = 0.002, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08,
decay = 0, clipnorm = -1, clipvalue = -1)
Nadam(lr = 0.002, beta_1 = 0.9, beta_2 = 0.999, epsilon = 1e-08,
schedule_decay = 0.004, clipnorm = -1, clipvalue = -1)
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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. |
clipnorm |
float >= 0. Gradients will be clipped when their L2 norm exceeds this value. Set to -1 to disable. |
clipvalue |
float >= 0. Gradients will be clipped when their absolute value exceeds this value. Set to -1 to disable. |
rho |
float >= 0 to be used in RMSprop |
epsilon |
float >= 0. Fuzz factor. |
beta_1 |
float, 0 < beta < 1. Generally close to 1. |
beta_2 |
float, 0 < beta < 1. Generally close to 1. |
schedule_decay |
float >= 0. Learning rate decay over each schedule in Nadam. |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | if(keras_available()) {
X_train <- matrix(rnorm(100 * 10), nrow = 100)
Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)
mod <- Sequential()
mod$add(Dense(units = 50, input_shape = dim(X_train)[2]))
mod$add(Activation("relu"))
mod$add(Dense(units = 3))
mod$add(Activation("softmax"))
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = SGD())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = RMSprop())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = Adagrad())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = Adadelta())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = Adam())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = Adamax())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = Nadam())
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
verbose = 0, validation_split = 0.2)
}
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