3 releases
0.1.5-alpha.0 | Mar 25, 2023 |
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0.1.4-alpha.0 | Mar 3, 2023 |
0.1.3-alpha.0 | Mar 2, 2023 |
#8 in #xxx
1MB
5.5K
SLoC
caffe2op-conditional crate description
ConditionalOp
The ConditionalOp
is a mathematical operator
used in deep learning computations. The operator
computes a conditional statement on a given input
and produces an output based on the
condition. This is commonly used in various neural
network architectures such as conditional
generative models, where the output is conditioned
on some input.
Note: This crate is currently being translated from C++ to Rust, and some function bodies may still be in the process of translation.
The conditional operator can be defined as follows:
Given an input X
, we compute a boolean condition
C
on X
. We then compute two functions F
and
T
on X
, such that F
is applied if the
condition is false
and T
is applied if the
condition is true
. The output Y
is then
defined as:
Y = C ? T(X) : F(X)
where ?
represents the conditional operator.
Usage
The ConditionalOp
operator can be used in
various deep learning models, such as conditional
generative models, where the output is conditioned
on some input. The caffe2op-conditional
crate
provides an implementation of the operator in
Rust, which can be used in projects that require
efficient computation of conditional statements.
When using this crate, some considerations to note include ensuring that the input tensors have compatible shapes and sizes, as well as efficient memory allocation and management for the output tensor.
Overall, the caffe2op-conditional
crate can be
a useful tool for implementing conditional
statements in deep learning computations,
providing efficient and flexible computation of
boolean conditions on input data.
Examples
46> cool! in which architectures is this sort of operator used?
The Conditional operator can be used in various types of architectures where conditional computations are required, such as in certain types of neural networks. For example, in recurrent neural networks, the output of a previous time step can be used as input for the current time step. However, the computation at the current time step may depend on some condition or other inputs. In such cases, the Conditional operator can be used to perform the appropriate computations based on the given condition or inputs. Similarly, in certain types of generative models like GANs (Generative Adversarial Networks), the generator network can use Conditional operators to generate outputs based on a given condition or label.
Dependencies
~37MB
~425K SLoC