dataset_use_spec.qmd

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execute:
  freeze: true
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[R/feature_spec.R](https://github.com/rstudio/tfdatasets//blob/main/R/feature_spec.R#L1057) 

# dataset_use_spec

## Transform the dataset using the provided spec.

## Description
 Prepares the dataset to be used directly in a model.The transformed dataset is prepared to return tuples (x,y) that can be used directly in Keras. 


## Usage
```r
 
dataset_use_spec(dataset, spec) 
```

## Arguments
|Arguments|Description|
|---|---|
| dataset | A TensorFlow dataset. |
| spec | A feature specification created with `feature_spec()`. |



## Value
 A TensorFlow dataset. 


## Examples
```{r, eval=ecodown::examples_not_run()}
 
library(tfdatasets) 
data(hearts) 
hearts <- tensor_slices_dataset(hearts) %>% dataset_batch(32) 
 
# use the formula interface 
spec <- feature_spec(hearts, target ~ age) %>% 
  step_numeric_column(age) 
 
spec_fit <- fit(spec) 
final_dataset <- hearts %>% dataset_use_spec(spec_fit) 
 
```

## See Also

  

- `feature_spec()` to initialize the feature specification. 

- `fit.FeatureSpec()` to create a tensorflow dataset prepared to modeling. 

- steps to a list of all implemented steps. 

  Other Feature Spec Functions:  `feature_spec()`, `fit.FeatureSpec()`, `step_bucketized_column()`, `step_categorical_column_with_hash_bucket()`, `step_categorical_column_with_identity()`, `step_categorical_column_with_vocabulary_file()`, `step_categorical_column_with_vocabulary_list()`, `step_crossed_column()`, `step_embedding_column()`, `step_indicator_column()`, `step_numeric_column()`, `step_remove_column()`, `step_shared_embeddings_column()`, `steps`