Filter NaN values in Tensorflow dataset

Question

Is there an easy way to filter all entries containing a nan value from a tensorflow.data.Dataset instance? Like the dropna method in Pandas?

---

Short example:

import numpy as np
import tensorflow as tf

X = tf.data.Dataset.from_tensor_slices([[1,2,3], [0,0,0], [np.nan,np.nan,np.nan], [3,4,5], [np.nan,3,4]])
y = tf.data.Dataset.from_tensor_slices([np.nan, 0, 1, 2, 3])
ds = tf.data.Dataset.zip((X,y))
ds = foo(ds)  # foo(x) = ?
for x in iter(ds): print(str(x))

What can I use for foo(x) to get the following output:

(<tf.Tensor: shape=(3,), dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(), dtype=float32, numpy=0.0>)
(<tf.Tensor: shape=(3,), dtype=float32, numpy=array([3., 4., 5.], dtype=float32)>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>)

If you want to try for yourself, here is Google Colab notebook.

Answer 1

I had a slightly different approach than the existing answer. Rather than using sum, I'm using tf.reduce_any:

filter_nan = lambda x, y: not tf.reduce_any(tf.math.is_nan(x)) and not tf.math.is_nan(y)

ds = tf.data.Dataset.zip((X,y)).filter(filter_nan)

list(ds.as_numpy_iterator())

[(array([0., 0., 0.], dtype=float32), 0.0),
 (array([3., 4., 5.], dtype=float32), 2.0)]

Answer 2

How about:

def any_nan(t):
    return tf.reduce_sum(
        tf.cast(
            tf.math.is_nan(t),
            tf.int32,
        )
    ) > tf.constant(0)


>>> ds_filtered = ds.filter(lambda x, y: not any_nan(x) and not any_nan(y))
>>> for x in iter(ds_filtered): print(str(x))
(<tf.Tensor: shape=(3,), dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(), dtype=float32, numpy=0.0>)
(<tf.Tensor: shape=(3,), dtype=float32, numpy=array([3., 4., 5.], dtype=float32)>, <tf.Tensor: shape=(), dtype=float32, numpy=2.0>)