predict.keras.engine.training.model.qmd

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execute:
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[R/model.R](https://github.com/rstudio/keras//blob/main/R/model.R#L872) 

# predict.keras.engine.training.Model

## Generate predictions from a Keras model

## Description
 Generates output predictions for the input samples, processing the samples in a batched way. 


## Usage
```r
 
## S3 method for class 'keras.engine.training.Model'
predict( 
  object, 
  x, 
  batch_size = NULL, 
  verbose = "auto", 
  steps = NULL, 
  callbacks = NULL, 
  ... 
) 
```

## Arguments
|Arguments|Description|
|---|---|
| object | Keras model |
| x | Input data (vector, matrix, or array). You can also pass a `tfdataset` or a generator returning a list with `(inputs, targets)` or `(inputs, targets, sample_weights)`. |
| batch_size | Integer. If unspecified, it will default to 32. |
| verbose | Verbosity mode, 0, 1, 2, or "auto". "auto" defaults to 1 for for most cases and defaults to `verbose=2` when used with ParameterServerStrategy or with interactive logging disabled. |
| steps | Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `NULL`. |
| callbacks | List of callbacks to apply during prediction. |
| ... | Unused |



## Value
 vector, matrix, or array of predictions 



## See Also
 Other model functions:  `compile.keras.engine.training.Model()`, `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_generator()`, `predict_on_batch()`, `predict_proba()`, `summary.keras.engine.training.Model()`, `train_on_batch()`