dropout | save a page for offline access and archival | Reinforcement Learning library

by jondashkyle JavaScript Version: 1.0.2 License: Apache-2.0

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kandi X-RAY | dropout Summary

dropout is a JavaScript library typically used in Artificial Intelligence, Reinforcement Learning applications. dropout has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i dropout' or download it from GitHub, npm.

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dropout has a low active ecosystem.

It has 69 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

dropout has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of dropout is 1.0.2

Quality

dropout has no bugs reported.

Security

dropout has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

dropout is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

dropout releases are not available. You will need to build from source code and install.

Deployable package is available in npm.

Installation instructions are not available. Examples and code snippets are available.

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dropout Key Features

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dropout Examples and Code Snippets

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Community Discussions

Trending Discussions on dropout

How to add several binary classifiers at the end of a MLP with Keras?

Is it possible to combine 2 neural networks?

How and where can i freeze classifier layer?

How to increse and decreses the model accuracy and batch size respectively

Pytorch Inferencing form the model is giving me different results every time

Facing ValueError: Shapes (None, None) and (None, 256, 256, 12) are incompatible

Which PyTorch modules are affected by model.eval() and model.train()?

GoogleNet Implantation ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size

Why flatten() is not working in co-lab whereas it worked in kaggle-notebook posted by other user?

Trouble understanding behaviour of modified VGG16 forward method (Pytorch)

QUESTION

How to add several binary classifiers at the end of a MLP with Keras?

Asked 2021-Jun-15 at 02:43

Say I have an MLP that looks like:

...

ANSWER

Answered 2021-Jun-15 at 02:43

In your problem you are trying to use Sequential API to create the Model. There are Limitations to Sequential API, you can just create a layer by layer model. It can't handle multiple inputs/outputs. It can't be used for Branching also.

Below is the text from Keras official website: https://keras.io/guides/functional_api/

The functional API makes it easy to manipulate multiple inputs and outputs. This cannot be handled with the Sequential API.

Also this stack link will be useful for you: Keras' Sequential vs Functional API for Multi-Task Learning Neural Network

Now you can create a Model using Functional API or Model Sub Classing.

In case of functional API Your Model will be

Assuming Output_1 is classification with 17 classes Output_2 is calssification with 2 classes and Output_3 is regression

Source https://stackoverflow.com/questions/67977986

QUESTION

Is it possible to combine 2 neural networks?

Asked 2021-Jun-13 at 00:55

I have a NET like (exemple from here)

...

ANSWER

Answered 2021-Jun-07 at 14:26

The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:

Source https://stackoverflow.com/questions/67872719

QUESTION

How and where can i freeze classifier layer?

Asked 2021-Jun-12 at 20:29

If I need to freeze the output layer of this model which is doing the classification as I don't need it.

...

ANSWER

Answered 2021-Jun-11 at 15:33

You are confusing a few things here (I think)

Freezing layers

You freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).

Usually we freeze part of the network creating features, in your case it would be everything up to self.head.

After that, we usually only train bottleneck (self.head in this case) to fine-tune it for the task at hand.

In case of your model it would be:

Source https://stackoverflow.com/questions/67939448

QUESTION

How to increse and decreses the model accuracy and batch size respectively

Asked 2021-Jun-11 at 14:23

İ am working on transfer learning for multiclass classification of image datasets that consists of 12 classes. As a result, İ am using VGG19. However, the accuracy of the model is as much lower than the expectation. İn addition train and valid accuracy do not increase. Besides that İ ma trying to decrease the batch size which is still 383

My code:

...

ANSWER

Answered 2021-Jun-10 at 15:05

383 on the log is not the batch size. It's the number of steps which is data_size / batch_size.

The problem that training does not work properly is probably because of very low or high learning rate. Try adjusting the learning rate.

Source https://stackoverflow.com/questions/67923564

QUESTION

Pytorch Inferencing form the model is giving me different results every time

Asked 2021-Jun-11 at 09:55

I have created and trained one very simple network in pytorch as shown below:

...

ANSWER

Answered 2021-Jun-11 at 09:55

I suspect this is due to you not having set the model to inference mode with

Source https://stackoverflow.com/questions/67934643

QUESTION

Facing ValueError: Shapes (None, None) and (None, 256, 256, 12) are incompatible

Asked 2021-Jun-10 at 10:22

İ am working on transfer learning for multiclass classification of image datasets that consists of 12 classes. As a result, İ am using VGG19. However, I am facing an error i.e. Facing ValueError: Shapes (None, None) and (None, 256, 256, 12) are incompatible. Moreover, İ have flaten layers too

My code:

...

ANSWER

Answered 2021-Jun-10 at 10:22

As @Frightera mentioned in the comments, you have defined Sequential 2 times.
And I have to add that you DON'T have to complicate the model from the first time, try to run a simple one because VGG19 will do all the work for you.
Adding many Dense layers after the VGG19 doesn't mean you get better scores, as the number of layers is a hyperparameter.
Also try to fix a small learning rate at the beginning as 0.1, 0.05, or 0.01.

Source https://stackoverflow.com/questions/67918032

QUESTION

Which PyTorch modules are affected by model.eval() and model.train()?

Asked 2021-Jun-08 at 21:20

The model.eval() method modifies certain modules (layers) which are required to behave differently during training and inference. Some examples are listed in the docs:

This has [an] effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Is there an exhaustive list of which modules are affected?

...

ANSWER

Answered 2021-Mar-13 at 14:22

Searching site:https://pytorch.org/docs/stable/generated/torch.nn. "during evaluation" on google, it would appear the following modules are affected:

Base class Modules Criteria _InstanceNorm InstanceNorm1d
InstanceNorm2d
InstanceNorm3d track_running_stats=True _BatchNorm BatchNorm1d
BatchNorm2d
BatchNorm3d
SyncBatchNorm _DropoutNd Dropout
Dropout2d
Dropout3d
AlphaDropout
FeatureAlphaDropout

Source https://stackoverflow.com/questions/66534762

QUESTION

GoogleNet Implantation ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size

Asked 2021-Jun-08 at 08:22

I am trying to implement GoogleNet inception network to classify images for classification project that I am working on, I used the same code before but with AlexNet network and the training was fine, but once I changed the network to GoogleNet architecture the code kept throwing the following error:

...

ANSWER

Answered 2021-Jun-08 at 08:22

GoogleNet is different than Alexnet, in GoogleNet your model has 3 outputs, 1 main and 2 auxiliary outputs connected in intermediate layers during training:

Source https://stackoverflow.com/questions/67869346

QUESTION

Why flatten() is not working in co-lab whereas it worked in kaggle-notebook posted by other user?

Asked 2021-Jun-07 at 20:58

I am working on a project for pneumonia detection. I have looked over kaggle for notebooks on the same. there was a user who stacked two pretrained model densenet169 and mobilenet. I copies whole kaggle notebook from the user where he didn't get any error, but when I ran it in google colab I get this error in this part:

part where error is:

...

ANSWER

Answered 2021-Jun-07 at 20:58

You have mixed up your imports a bit.

Here is a fixed version of your code

Source https://stackoverflow.com/questions/67877688

QUESTION

Trouble understanding behaviour of modified VGG16 forward method (Pytorch)

Asked 2021-Jun-07 at 14:13

I have modified VGG16 in pytorch to insert things like BN and dropout within the feature extractor. By chance I now noticed something strange when I changed the definition of the forward method from:

...

ANSWER

Answered 2021-Jun-07 at 14:13

I can't run your code, but I believe the issue is because linear layers expect 2d data input (as it is really a matrix multiplication), while you provide 4d input (with dims 2 and 3 of size 1).

Please try squeeze

Source https://stackoverflow.com/questions/67870887

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Install dropout

You can install using 'npm i dropout' or download it from GitHub, npm.

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