first pass on the module #14

paraynaud · 2022-06-24T17:34:10Z

No description provided.

…om README.md

dpo · 2022-06-27T16:40:33Z

README.md

+pkg> test KnetNLPModels
+```
+
+This step-by-step example suppose prior knowledge [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).


Suggested change

This step-by-step example suppose prior knowledge [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).

## Example

This step-by-step example assumes prior knowledge with [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).

dpo · 2022-06-27T16:43:38Z

README.md

- the values of the neural network variable $w$;
- the objective function $\mathcal{L}(X,Y;w)$ of the loss function $\mathcal{L}$ at the point $w$ for a given minibatch $X,Y$
- the gradient $\nabla \mathcal{L}(X,Y;w)$ of the loss function at the point $w$ for a given mini-batch $X,Y$
+A `KnetNLPModel` gives the user access to:


Suggested change

A `KnetNLPModel` gives the user access to:

## Synopsis

A `KnetNLPModel` gives the user access to:

Move the "Synopsis" section above the "Example" header.

dpo · 2022-06-27T16:44:58Z

README.md

- the gradient $\nabla \mathcal{L}(X,Y;w)$ of the loss function at the point $w$ for a given mini-batch $X,Y$
+A `KnetNLPModel` gives the user access to:
+- the values of the neural network variables/weights `w`;
+- the objective/loss function `L(X, Y; w)` of the loss function `L` at the point `w` for a given minibatch `(X,Y)`


Suggested change

- the objective/loss function `L(X, Y; w)` of the loss function `L` at the point `w` for a given minibatch `(X,Y)`

- the value of the objective/loss function `L(X, Y; w)` at `w` for a given minibatch `(X,Y)`;

dpo · 2022-06-27T16:45:31Z

README.md

+A `KnetNLPModel` gives the user access to:
+- the values of the neural network variables/weights `w`;
+- the objective/loss function `L(X, Y; w)` of the loss function `L` at the point `w` for a given minibatch `(X,Y)`
+- the gradient `∇L(X, Y; w)` of the objective/loss function at the point `w` for a given mini-batch `(X,Y)`


Suggested change

- the gradient `∇L(X, Y; w)` of the objective/loss function at the point `w` for a given mini-batch `(X,Y)`

- the gradient `∇L(X, Y; w)` of the objective/loss function at `w` for a given mini-batch `(X,Y)`.

dpo · 2022-06-27T16:46:13Z

README.md

- Switch the minibatch used to evaluate the neural network
- Measure the neural network's accuracy at the current point for a given testing mini-batch
+In addition, it provides tools to:
+- Switch the minibatch used to evaluate the neural network;


Suggested change

- Switch the minibatch used to evaluate the neural network;

- switch the minibatch used to evaluate the neural network;

dpo · 2022-06-27T17:05:16Z

README.md

-
+## Default behavior
+By default, the training minibatch that evaluates the neural network doesn't change between evaluations.
+To change the training minibatch use:


Suggested change

To change the training minibatch use:

To change the training minibatch, use:

dpo · 2022-06-27T17:05:52Z

README.md

 ```
+The size of the new minibatch is the size define previously.


Suggested change

The size of the new minibatch is the size define previously.

The size of the new minibatch is the size defined earlier.

dpo · 2022-06-27T17:06:39Z

README.md


+The size of the training and testing minibatch can be set to `1/denominator` the size of the dataset with:


Suggested change

The size of the training and testing minibatch can be set to `1/denominator` the size of the dataset with:

The size of the training and test minibatches can be set to `1/p` the size of the dataset with:

dpo · 2022-06-27T17:06:50Z

README.md


+The size of the training and testing minibatch can be set to `1/denominator` the size of the dataset with:
+```julia
+set_size_minibatch!(DenseNetNLPModel, denominator) # denominator::Int > 1


Suggested change

set_size_minibatch!(DenseNetNLPModel, denominator) # denominator::Int > 1

set_size_minibatch!(DenseNetNLPModel, p) # p::Int > 1

dpo · 2022-06-27T17:07:36Z

docs/src/tutorial.md

@@ -1,118 +1,118 @@
 # KnetNLPModels.jl Tutorial


Same changes here.

dpo

You skipped a few of my comments. Please also apply them to the tutorial.

dpo · 2022-06-27T19:20:17Z

README.md

+
+A `KnetNLPModel` gives the user access to:
+- the values of the neural network variables/weights `w`;
+- the value of the objective/loss function `L(X, Y; w)` at `w` for a given minibatch `(X,Y)`


Suggested change

- the value of the objective/loss function `L(X, Y; w)` at `w` for a given minibatch `(X,Y)`

- the value of the objective/loss function `L(X, Y; w)` at `w` for a given minibatch `(X,Y)`;

dpo · 2022-06-27T19:20:27Z

README.md

+A `KnetNLPModel` gives the user access to:
+- the values of the neural network variables/weights `w`;
+- the value of the objective/loss function `L(X, Y; w)` at `w` for a given minibatch `(X,Y)`
+- the gradient `∇L(X, Y; w)` of the objective/loss function at `w` for a given mini-batch `(X,Y)`


Suggested change

- the gradient `∇L(X, Y; w)` of the objective/loss function at `w` for a given mini-batch `(X,Y)`

- the gradient `∇L(X, Y; w)` of the objective/loss function at `w` for a given mini-batch `(X,Y)`.

I already suggested the changes above.

dpo · 2022-06-27T19:21:14Z

README.md


-## Define the layers of interest
-The following code define a dense layer as an evaluable julia structure.
+This step-by-step example suppose prior knowledge [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).


Suggested change

This step-by-step example suppose prior knowledge [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).

This step-by-step example assumes prior knowledge of [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).

Also suggested before.

paraynaud · 2022-06-27T19:45:47Z

Sorry, I did not see those ones.

paraynaud · 2022-06-27T19:50:42Z

I made the PR #16 from this PR.
It incomporates the last remarks, and the CPU/GPU support that Farhad and I did.

dpo · 2022-06-27T20:34:06Z

I made the PR #16 from this PR.
It incomporates the last remarks,

Please don't do that. Keep PRs minimal and orthogonal. We're ready to merge this one.

RAYNAUD Paul (raynaudp) added 3 commits June 24, 2022 13:40

first pass on the module

01077eb

fix the documentation, replace in tutorial.md the updated tutorial fr…

8fbfa43

…om README.md

small fixes

e5d4ce9

dpo requested changes Jun 27, 2022

View reviewed changes

RAYNAUD Paul (raynaudp) added 2 commits June 27, 2022 14:43

correct remarks

a0be01a

fix of omissions

2d3f8b8

dpo requested changes Jun 27, 2022

View reviewed changes

dpo approved these changes Jun 27, 2022

View reviewed changes

dpo mentioned this pull request Jun 27, 2022

Pr merge cpu #16

Closed

paraynaud merged commit a41288d into main Jun 28, 2022

paraynaud deleted the repo_form branch January 9, 2023 22:33

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first pass on the module #14

first pass on the module #14

paraynaud commented Jun 24, 2022

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paraynaud commented Jun 27, 2022

paraynaud commented Jun 27, 2022

dpo commented Jun 27, 2022 •

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-This step-by-step example suppose prior knowledge [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).
+## Example
+This step-by-step example assumes prior knowledge with [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).

	- the objective/loss function `L(X, Y; w)` of the loss function `L` at the point `w` for a given minibatch `(X,Y)`
	- the value of the objective/loss function `L(X, Y; w)` at `w` for a given minibatch `(X,Y)`;

	- the gradient `∇L(X, Y; w)` of the objective/loss function at the point `w` for a given mini-batch `(X,Y)`
	- the gradient `∇L(X, Y; w)` of the objective/loss function at `w` for a given mini-batch `(X,Y)`.

	- Switch the minibatch used to evaluate the neural network;
	- switch the minibatch used to evaluate the neural network;

	To change the training minibatch use:
	To change the training minibatch, use:

		```
		The size of the new minibatch is the size define previously.


		The size of the training and testing minibatch can be set to `1/denominator` the size of the dataset with:

	The size of the training and testing minibatch can be set to `1/denominator` the size of the dataset with:
	The size of the training and test minibatches can be set to `1/p` the size of the dataset with:

	set_size_minibatch!(DenseNetNLPModel, denominator) # denominator::Int > 1
	set_size_minibatch!(DenseNetNLPModel, p) # p::Int > 1

	This step-by-step example suppose prior knowledge [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).
	This step-by-step example assumes prior knowledge of [julia](https://julialang.org/) and [Knet.jl](https://github.com/denizyuret/Knet.jl.git).

first pass on the module #14

first pass on the module #14

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paraynaud commented Jun 24, 2022

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paraynaud commented Jun 27, 2022

paraynaud commented Jun 27, 2022

dpo commented Jun 27, 2022 • edited Loading

dpo commented Jun 27, 2022 •

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