keras_compile: Compile a keras model
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Loss functions Metrics Author(s) References See Also Examples

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.

1 2	keras_compile(model, optimizer, loss, metrics = NULL, sample_weight_mode = NULL)

`model`	a keras model object, for example 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. See `EarlyStopping()` for using these in callbacks.
`sample_weight_mode`	if you need to do timestep-wise sample weighting (2D weights), set this to `temporal`. `None` defaults to sample-wise weights (1D).

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 model functions: 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)
  
  # 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)
  
}