Configure a Keras model for training
compile(object, optimizer, loss, metrics = NULL, loss_weights = NULL, sample_weight_mode = NULL, weighted_metrics = NULL, target_tensors = NULL, ...)
Model object to compile.
Name of optimizer or optimizer object.
Name of objective function or objective function. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of objectives. The loss value that will be minimized by the model will then be the sum of all individual losses.
List of metrics to be evaluated by the model during training
and testing. Typically you will use
Optional list specifying scalar coefficients to weight
the loss contributions of different model outputs. The loss value that will
be minimized by the model will then be the weighted sum of all indvidual
losses, weighted by the
If you need to do timestep-wise sample weighting
(2D weights), set this to "temporal".
List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing
By default, Keras will create placeholders for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensors (in turn, Keras will not expect external
data for these targets at training time), you
can specify them via the
When using the Theano/CNTK backends, these arguments
are passed into K.function. When using the TensorFlow backend,
these arguments are passed into
Other model functions: