Split a dataset created by Tensorflow dataset API in to Train and Test?

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Does anyone know how to split a dataset created by the dataset API (tf.data.Dataset) in Tensorflow into Test and Train?

Assuming you have all_dataset variable of tf.data.Dataset type:

test_dataset = all_dataset.take(1000) 
train_dataset = all_dataset.skip(1000)

Test dataset now has first 1000 elements and the rest goes for training.

tfds.Split, Instead what you can do is to essentially slice the dataset up so that every X records becomes a validation record. Now for our example, we're going to slice it so that we have a 3/1 train/validation split. Meaning 3 records will go to training, then 1 record to validation, then repeat. So the first dataset. Enum for dataset splits. tfds.Split( name ) Datasets are typically split into different subsets to be used at various stages of training and evaluation. TRAIN: the training data. VALIDATION: the validation data. If present, this is typically used as evaluation data while iterating on a model (e.g. changing hyperparameters, model architecture

You may use Dataset.take() and Dataset.skip():

train_size = int(0.7 * DATASET_SIZE)
val_size = int(0.15 * DATASET_SIZE)
test_size = int(0.15 * DATASET_SIZE)

full_dataset = tf.data.TFRecordDataset(FLAGS.input_file)
full_dataset = full_dataset.shuffle()
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.skip(val_size)
test_dataset = test_dataset.take(test_size)

For more generality, I gave an example using a 70/15/15 train/val/test split but if you don't need a test or a val set, just ignore the last 2 lines.

Take:

Creates a Dataset with at most count elements from this dataset.

Skip:

Creates a Dataset that skips count elements from this dataset.

You may also want to look into Dataset.shard():

Creates a Dataset that includes only 1/num_shards of this dataset.


Disclaimer I stumbled upon this question after answering this one so I thought I'd spread the love

Split a dataset created by Tensorflow dataset API in to Train and Test?, Short description In datasets like tf_flowers, only one split is provided. way to split this set into a training set, a validation set and a test set. All DatasetBuilders expose various data subsets defined as splits (eg: train, test). When constructing a tf.data.Dataset instance using either tfds.load() or tfds.DatasetBuilder.as_dataset(), one can specify which split(s) to retrieve. It is also possible to retrieve slice(s) of split(s) as well as combinations of those. Slicing API. Examples

Now Tensorflow doesn't contain any tools for that. You could use sklearn.model_selection.train_test_split to generate train/eval/test dataset, then create tf.data.Dataset respectively.

tensorflow/datasets, I am trying to split the iris dataset into train/test with 2/3 for training and /​questions/56553357/splitting-a-tensorflow-dataset-using-tfds-percent Be careful with this as the data is not guarantee to be generated in the same order for the train set unless Contact GitHub · Pricing · API · Training · Blog · About. I am reading a csv file using tf.contrib.data.make_csv_dataset to form a dataset, and then I use the command take() to form another dataset with just one element, but still it returns all elments.

@apatsekin,@ted recently i don't have a reputation above 50, so I just have to reply the answer here, i'm wondering it's reasonable to using .take method directly to fetch test dataset or not. if the dataset is shuffled in every epoch, then will it get a different TRAIN/TEST split, as in training process we need the test set should never appear in train set. so should this will be a problem

or we add a parameter in shuffle:

train_size = int(0.7 * DATASET_SIZE)
val_size = int(0.15 * DATASET_SIZE)
test_size = int(0.15 * DATASET_SIZE)

full_dataset = tf.data.TFRecordDataset(FLAGS.input_file)
full_dataset = full_dataset.shuffle( reshuffle_each_iteration = False )
train_dataset = full_dataset.take(train_size)
test_dataset = full_dataset.skip(train_size)
val_dataset = test_dataset.skip(val_size)
test_dataset = test_dataset.take(test_size)

Splitting a dataset from tfds results in inaccurate partition sizes , Learn how to use the TensorFlow Dataset API to create professional, high be called during the training, validation and/or testing of your model in TensorFlow. to load the data from the package and split it into train and validation datasets. All DatasetBuilders expose various data subsets defined as splits (eg: train, test). When constructing a tf.data.Dataset instance using either tfds.load() or tfds.DatasetBuilder.as_dataset(), one can specify which split(s) to retrieve. It is also possible to retrieve slice(s) of split(s) as well as combinations of those.

You can use shard:

dataset = dataset.shuffle()  # optional
trainset = dataset.shard(2, 0)
testset = dataset.shard(2, 1)

See: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard

TensorFlow Dataset API tutorial – build high performance data , Fortunately, TensorFlow has a built-in API, called Dataset to make it easier to As before, we want to have a train dataset and a test dataset to split a data into train and test, use train_test_split function from sklearn.model_selection. you need to determine the percentage of splitting. test_size=0.33 means that 33% of the original data will be for test and remaining will be for train. This function will return four elements the data and labels for train and test sets.

How to use Dataset in TensorFlow, Split arrays or matrices into random train and test subsets should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. Each descriptor can be composed with other using addition or slice. Ex: split = tfds.Split.TRAIN.subsplit(tfds.percent[0:25]) + tfds.Split.TEST The resulting split will correspond to 25% of the train split merged with 100% of the test split

sklearn.model_selection.train_test_split, Tensorflow(TF) has opened a space for whole new ways of creating datasets, which Tensorflow has been maturing over the years, this year with tf.data API the TF be split (e.g. TRAIN and TEST );; and the individual records in the dataset. value: The Tensor to split. num_or_size_splits: Either an integer indicating the number of splits along axis or a 1-D integer Tensor or Python list containing the sizes of each output tensor along axis. If a scalar, then it must evenly divide value.shape[axis]; otherwise the sum of sizes along the split axis must match that of the value.

TF.data reborn from the ashes - Prince Canuma, In this tutorial we will learn how to use TensorFlow's Dataset module tf.data to build The Dataset API allows you to build an asynchronous, highly optimized data and as you can see, we now have a batch created from the shuffled Dataset ! be to split the dataset into train / dev / test in advance and already shuffle the  New in version 0.16: If the input is sparse, the output will be a scipy.sparse.csr_matrix.Else, output type is the same as the input type.

Comments
  • take(), skip(), and shard() all have their own problems. I just posted my answer over here. I hope it better answers your question.
  • Thank you very much @ted! Is there a way to divide the dataset in a stratified way? Or, alternatively, how can we have an idea of the class proportions (suppose a binary problem) after the train/val/test split? Thanks a lot in advance!
  • Have a look at this blogpost I wrote; eventhough it's for multilabel datasets, should be easily usable for single label, multiclass datasets -> vict0rs.ch/2018/06/17/multilabel-text-classification-tensorflow
  • shard is depricated