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import tensorflow as tf


help(tf.boolean_mask)


    

    




Help on function boolean_mask in module tensorflow.python.ops.array_ops:

boolean_mask(tensor, mask, name='boolean_mask')
    Apply boolean mask to tensor.  Numpy equivalent is `tensor[mask]`.
    
    ```python
    # 1-D example
    tensor = [0, 1, 2, 3]
    mask = np.array([True, False, True, False])
    boolean_mask(tensor, mask) ==> [0, 2]
    ```
    
    In general, `0 < dim(mask) = K <= dim(tensor)`, and `mask`'s shape must match
    the first K dimensions of `tensor`'s shape.  We then have:
      `boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]`
    where `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order).
    
    Args:
      tensor:  N-D tensor.
      mask:  K-D boolean tensor, K <= N and K must be known statically.
      name:  A name for this operation (optional).
    
    Returns:
      (N-K+1)-dimensional tensor populated by entries in `tensor` corresponding
      to `True` values in `mask`.
    
    Raises:
      ValueError:  If shapes do not conform.
    
    Examples:
    
    ```python
    # 2-D example
    tensor = [[1, 2], [3, 4], [5, 6]]
    mask = np.array([True, False, True])
    boolean_mask(tensor, mask) ==> [[1, 2], [5, 6]]
    ```

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