tf.compat.v1.where

Return the elements, either from x or y, depending on the condition.

tf.compat.v1.where(
    condition, x=None, y=None, name=None
)

Migrate to TF2

Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code.

This API is compatible with eager execution and tf.function. However, this is still a legacy API endpoint originally designed for TF1. To migrate to fully-native TF2, please replace its usage with tf.where instead, which is directly backwards compatible with tf.compat.v1.where.

However,tf.compat.v1.where is more restrictive than tf.where, requiring x and y to have the same shape, and returning a Tensor with the same type and shape as x, y (if they are both non-None).

tf.where will accept x, y that are not the same shape as long as they are broadcastable with one another and with condition, and will return a Tensor with shape broadcast from condition, x, and y.

For example, the following works with tf.where but not tf.compat.v1.where:

tf.where([True, False, False, True], [1,2,3,4], [100])
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   4],
dtype=int32)>

tf.where(True, [1,2,3,4], 100)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 2, 3, 4],
dtype=int32)>

Description

If both x and y are None, then this operation returns the coordinates of true elements of condition. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in input. Indices are output in row-major order.

If both non-None, x and y must have the same shape. The condition tensor must be a scalar if x and y are scalar. If x and y are tensors of higher rank, then condition must be either a vector with size matching the first dimension of x, or must have the same shape as x.

The condition tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from x (if true) or y (if false).

If condition is a vector and x and y are higher rank matrices, then it chooses which row (outer dimension) to copy from x and y. If condition has the same shape as x and y, then it chooses which element to copy from x and y.

Args
condition	A Tensor of type bool
x	A Tensor which may have the same shape as condition. If condition is rank 1, x may have higher rank, but its first dimension must match the size of condition.
y	A tensor with the same shape and type as x.
name	A name of the operation (optional)

Returns
A Tensor with the same type and shape as x, y if they are non-None. Otherwise, a Tensor with shape (num_true, rank(condition)).

Raises
ValueError	When exactly one of x or y is non-None.