# Unable to read TFRecord using tf.data.TFRecordDataset

**URL:** <https://discuss.ai.google.dev/t/unable-to-read-tfrecord-using-tf-data-tfrecorddataset/30012>\
**Category:** TensorFlow\
**Tags:** learning, datasets, models, help\_request\
**Created:** [August 26, 2021, 11:53am UTC](https://discuss.ai.google.dev/t/unable-to-read-tfrecord-using-tf-data-tfrecorddataset/30012 "2021-08-26T11:53:11Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![TensorOverflow](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/tensoroverflow/32/7323_2.png) [@TensorOverflow](https://discuss.ai.google.dev/u/TensorOverflow)\
**Post date:** [August 26, 2021, 11:53am UTC](https://discuss.ai.google.dev/t/unable-to-read-tfrecord-using-tf-data-tfrecorddataset/30012/1 "2021-08-26T11:53:11Z")

</div>

I am trying to read a TFRecord file like this:

```
dataset = tf.data.TFRecordDataset("./tfrecords/train.record").map(_extract_fn).batch(3)

```

However, when I run

`features, labels = iter(dataset).next()`

I get this error:

```
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [4], [batch]: [2] [Op:IteratorGetNext]

```

This is the function that parses the TFRecord file:

```
    features = {
        'image/height': tf.io.FixedLenFeature([],tf.int64),
        'image/width': tf.io.FixedLenFeature([], tf.int64),
        'image/filename': tf.io.VarLenFeature(tf.string),
        'image/id': tf.io.FixedLenFeature([], tf.string),
        'image/encoded': tf.io.FixedLenFeature([],tf.string),
        'image/format': tf.io.FixedLenFeature([], tf.string),
        'image/object/class/text': tf.io.VarLenFeature(tf.string),
        'image/object/class/label': tf.io.VarLenFeature(tf.int64),
        'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),
        'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),
        'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),
        'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),
        }
sample = tf.io.parse_single_example(tfrecord, features)
data = {}

data["image/encoded"] = tf.image.decode_jpeg(sample["image/encoded"], channels=3)
label = sample['image/object/class/label'].values

return data,label

```

If I write `return data` instead and only set `features = iter(dataset).next()` it works fine.  
What is the issue here?

Thanks for any help!

---

<div class="post-metadata">

**Author:** ![Mark\_Daoust](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/mark_daoust/32/411_2.png) [@Mark\_Daoust](https://discuss.ai.google.dev/u/Mark_Daoust)\
**Post date:** [August 26, 2021, 5:52pm UTC](https://discuss.ai.google.dev/t/unable-to-read-tfrecord-using-tf-data-tfrecorddataset/30012/3 "2021-08-26T17:52:54Z")

</div>

The `label` has a variable length:

> [@TensorOverflow](#):
>
> `'image/object/class/label': tf.io.VarLenFeature(tf.int64),`

So when you try to `.batch` the dataset it can’t pack the different sized tensors together.

You either want to use `.padded_patch` or `.apply(tf.data.experimental.dense_to_ragged_batch(...))`

---

<div class="post-metadata">

**Author:** ![TensorOverflow](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/tensoroverflow/32/7323_2.png) [@TensorOverflow](https://discuss.ai.google.dev/u/TensorOverflow)\
**Post date:** [August 27, 2021, 4:41pm UTC](https://discuss.ai.google.dev/t/unable-to-read-tfrecord-using-tf-data-tfrecorddataset/30012/5 "2021-08-27T16:41:12Z")

</div>

Thank you! Using `.padded_patch` fixed the issue!