Description Usage Arguments Author(s) References See Also Examples
Learn the weight and bias values for am model given training data. Model must be compiled first. The model is modified in place.
1 2 3 4 |
model |
a keras model object, for example created with |
x |
numeric matrix of input data |
y |
a numeric matrix or numeric vector containing labels. |
batch_size |
integer. Number of samples per gradient update. |
epochs |
integer, the number of epochs to train the model. |
verbose |
0 for no logging to stdout, 1 for progress bar logging, 2 for one log line per epoch. |
callbacks |
list of callbacks to apply during training.
See |
validation_split |
numeric ( |
validation_data |
|
shuffle |
boolean or string (for |
class_weight |
dictionary mapping classes to a weight value, used for scaling the loss function (during training only). |
sample_weight |
numeric array of weights for the training samples |
initial_epoch |
epoch at which to start training |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other model functions: LoadSave
,
Predict
, Sequential
,
keras_compile
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 | 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( Dropout(rate = 0.5))
mod$add(Activation("relu"))
mod$add(Dense(units = 3))
mod$add(ActivityRegularization(l1 = 1))
mod$add(Activation("softmax"))
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)
# You can also add layers directly as arguments to Sequential()
mod <- Sequential(
Dense(units = 50, input_shape = ncol(X_train)),
Dropout(rate = 0.5),
Activation("relu"),
Dense(units = 3),
ActivityRegularization(l1 = 1),
Activation("softmax")
)
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)
}
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