11 Tensorized comparison functions

8.12

11 Tensorized comparison functions🔗ℹ

These functions compare two tensors of rank 1 and the same shape and return a tensor where an element is 1.0 if the corresponding scalars in the two argument tensors would return #t when compared with the given scalar comparison function.

procedure
(=-1 t0 t1) → tensor?
t0 : tensor?
t1 : tensor?

Compare the scalars in t0 and t1 with =-0-0

procedure
(<-1 t0 t1) → tensor?
t0 : tensor?
t1 : tensor?

Compare the scalars in t0 and t1 with <-0-0

procedure
(<=-1 t0 t1) → tensor?
t0 : tensor?
t1 : tensor?

Compare the scalars in t0 and t1 with <=-0-0

procedure
(>-1 t0 t1) → tensor?
t0 : tensor?
t1 : tensor?

Compare the scalars in t0 and t1 with >-0-0

procedure
(>=-1 t0 t1) → tensor?
t0 : tensor?
t1 : tensor?

Compare the scalars in t0 and t1 with >=-0-0

1	Overview
2	Entry points
3	List functions
4	Tensor functions
5	Extended Functions
6	Automatic Differentiation
7	Differentiable extended numerical functions
8	Non-differentiable extended numerical functions
9	Base-rank (non-extended) differentiable functions
10	Boolean comparison functions
11	Tensorized comparison functions
12	Hyperparameters
13	Gradient Descent Functions and Hyperparameters
14	Layer functions
15	Loss Functions
16	Building blocks for neural networks
17	He Initialization
18	Random number functions
19	Models and Accuracy
20	Logging
21	Utilities
22	Setting tensor implementations