Compile a keras model
Models must be compiled before being fit or used for prediction. This function changes to input model object itself, and does not produce a return value.
keras_compile(model, optimizer, loss, metrics = NULL, sample_weight_mode = NULL)
model |
a keras model object created with Sequential |
optimizer |
name of optimizer) or optimizer object. See Optimizers. |
loss |
name of a loss function. See Details for possible choices. |
metrics |
vector of metric names to be evaluated by the model during training and testing. See Details for possible options. |
sample_weight_mode |
if you need to do timestep-wise sample
weighting (2D weights), set this to |
Possible losses are
mean_squared_error
mean_absolute_error
mean_absolute_percentage_error
mean_squared_logarithmic_error
squared_hinge
hinge
categorical_crossentropy
sparse_categorical_crossentropy
binary_crossentropy
kullback_leibler_divergence
poisson
cosine_proximity.
Possible metrics are:
binary_accuracy
categorical_accuracy
sparse_categorical_accuracy
top_k_categorical_accuracy
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other models: LoadSave,
Predict, Sequential,
keras_fit
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)
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