compile.keras.engine.training.Model {keras} | R Documentation

## Configure a Keras model for training

### Description

Configure a Keras model for training

### Usage

    ## S3 method for class 'keras.engine.training.Model'
    compile(object, optimizer, loss,
      metrics = NULL, loss_weights = NULL, sample_weight_mode = NULL,
      weighted_metrics = NULL, target_tensors = NULL, ...)

### Arguments

`object` | Model object to compile.
`optimizer` | Name of optimizer or optimizer instance.
`loss` | 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.
`metrics` | List of metrics to be evaluated by the model during training and testing. Typically you will use `metrics='accuracy'`. To specify different metrics for different outputs of a multi-output model, you could also pass a named list such as `metrics=list(output_a = 'accuracy')`.
`loss_weights` | 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 `loss_weights` coefficients.
`sample_weight_mode` | If you need to do timestep-wise sample weighting (2D weights), set this to "temporal". `NULL` defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different `sample_weight_mode` on each output by passing a list of modes.
`weighted_metrics` | List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing
`target_tensors` | By default, Keras will create a placeholder 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 tensor (in turn, Keras will not expect external data for these targets at training time), you can specify them via the `target_tensors` argument. It should be a single tensor (for a single-output sequential model),
`...` | When using the Theano/CNTK backends, these arguments are passed into K.function. When using the TensorFlow backend, these arguments are passed into `tf$Session()$run`.

### See Also

Other model functions: `evaluate.keras.engine.training.Model`, `evaluate_generator`, `fit.keras.engine.training.Model`, `fit_generator`, `get_config`, `get_layer`, `keras_model_sequential`, `keras_model`, `multi_gpu_model`, `pop_layer`, `predict.keras.engine.training.Model`, `predict_generator`, `predict_on_batch`, `predict_proba`, `summary.keras.engine.training.Model`, `train_on_batch`

[Package keras version 2.2.4 Index]