CODEWITHSUNDEEP
pythontensorflowdeep-learning
Shivanath
Shivanath

Reputation: 29

"... has insufficient rank for batching." What is the problem with this 3 line code?

this is my first question here. I've been wanting to create a dataset with the popular IMDb dataset for learning purpose. The directories are as follows: .../train/pos/ and .../train/neg/ . I created a function which will merge text files with its labels and getting a error. I need your help to debug!

def datasetcreate(filepath, label):
    filepaths = tf.data.Dataset.list_files(filepath)
    return tf.stack([tf.data.Dataset.from_tensor_slices((_, tf.constant(label, dtype='int32'))) for _ in tf.data.TextLineDataset(filepaths)])
datasetcreate(['aclImdb/train/pos/*.txt'],1)

And this is the error I'm getting:

ValueError: Value tf.Tensor(b'An American in Paris was, in many ways, the ultimate.....dancers of all time.', shape=(), dtype=string) has insufficient rank for batching.

Why does this happen and what can I do to get rid of this? Thanks.

Upvotes: 3

Views: 4017

Answers (1)

Stefan
Stefan

Reputation: 1084

Your code has two problems:

First, the way you load your TextLineDatasets, your loaded tensors contain string objects, which have an empty shape associated, i.e. a rank of zero. The rank of a tensor is the length of the shape property.

Secondly, you are trying to stack two tensors with different rank, which is would throw another error because, a sentence (a sequence of tokens) has a rank of 1 and the label as scalar has a rank of 0.

If you just need the dataset, I recommend to use the Tensorflow Dataset package, which has many ready-to-use datasets available.

If want to solve your particular problem, one way to fix your data pipeline is by using Datasest.interleave and the Dataset.zip functions.

# load positive sentences
filepaths = list(tf.data.Dataset.list_files('aclImdb/train/pos/*.txt'))
sentences_ds = tf.data.Dataset.from_tensor_slices(filepaths)
sentences_ds = sentences_ds.interleave(lambda text_file: tf.data.TextLineDataset(text_file) )
sentences_ds = sentences_ds.map( lambda text: tf.strings.split(text) )

# dataset for labels, create 1 label per file
labels = tf.constant(1, dtype="int32", shape=(len(filepaths)))
label_ds = tf.data.Dataset.from_tensor_slices(labels)

# combine text with label datasets
dataset =  tf.data.Dataset.zip( (sentences_ds, label_ds) )

print( list(dataset.as_numpy_iterator() ))

First, you use the interleave function to combine multiple text datasets to one dataset. Next, you use tf.strings.split to split each text to its tokens. Then, you create a dataset for your positive labels. Finally, you combine the two datasets using zip.

IMPORTANT: To train/run any DL models on your dataset, you will likely need further pre-processing for your sentences, e.g. build a vocabulary and train word-embeddings.

Upvotes: 3

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