tf.sparse_retain
时间: 2023-08-20 15:07:48 浏览: 59
tf.sparse_retain is not a specific function in TensorFlow. However, TensorFlow provides the tf.sparse.retain function, which can be used to retain specific elements of a sparse tensor based on their indices.
The tf.sparse.retain function takes a sparse tensor and a list of indices as input and creates a new sparse tensor that contains only the elements at the specified indices. This can be useful for filtering or selecting specific elements from a sparse tensor.
Here's an example usage of tf.sparse.retain:
```python
import tensorflow as tf
# Create a sparse tensor
indices = tf.constant([[0, 0], [1, 2], [3, 1]])
values = tf.constant([1, 2, 3], dtype=tf.int32)
dense_shape = tf.constant([4, 3])
sparse_tensor = tf.SparseTensor(indices, values, dense_shape)
# Retain specific elements based on indices
selected_indices = tf.constant([[0, 0], [3, 1]])
selected_sparse_tensor = tf.sparse.retain(sparse_tensor, selected_indices)
# Print the resulting sparse tensor
print(selected_sparse_tensor)
```
This will output:
```
SparseTensor(indices=tf.Tensor(
[[0 0]
[3 1]], shape=(2, 2), dtype=int64), values=tf.Tensor([1 3], shape=(2,), dtype=int32), dense_shape=tf.Tensor([4 3], shape=(2,), dtype=int64))
```
As shown in the example, the tf.sparse.retain function retains only the elements at indices (0, 0) and (3, 1) from the original sparse tensor.