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Computes the "logical and" of elements across dimensions of a tensor. (deprecated arguments)
tf.compat.v1.reduce_all(
input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None,
keep_dims=None
)
Reduces input_tensor along the dimensions given in axis.
Unless keepdims is true, the rank of the tensor is reduced by 1 for each
of the entries in axis, which must be unique. If keepdims is true, the
reduced dimensions are retained with length 1.
If axis is None, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
x = tf.constant([[True, True], [False, False]])
tf.reduce_all(x) # False
tf.reduce_all(x, 0) # [False, False]
tf.reduce_all(x, 1) # [True, False]
Args | |
|---|---|
input_tensor
|
The boolean tensor to reduce. |
axis
|
The dimensions to reduce. If None (the default), reduces all
dimensions. Must be in the range [-rank(input_tensor),
rank(input_tensor)).
|
keepdims
|
If true, retains reduced dimensions with length 1. |
name
|
A name for the operation (optional). |
reduction_indices
|
The old (deprecated) name for axis. |
keep_dims
|
Deprecated alias for keepdims.
|
Returns | |
|---|---|
| The reduced tensor. |
Numpy Compatibility
Equivalent to np.all