dense | jQuery plugin that blows your visitors ' retinas

by gocom JavaScript Version: 0.0.1 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | dense Summary

dense is a JavaScript library. dense has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i dense' or download it from GitHub, npm.

jQuery plugin that blows your visitors' retinas

Support

Quality

Security

License

Reuse

Support

dense has a low active ecosystem.

It has 218 star(s) with 23 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 3 have been closed. On average issues are closed in 68 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dense is 0.0.1

Quality

dense has 0 bugs and 0 code smells.

Security

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

dense code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

dense is licensed under the MIT License. This license is Permissive.

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

Reuse

dense 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.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of dense

Get all kandi verified functions for this library.

dense Key Features

No Key Features are available at this moment for dense.

dense Examples and Code Snippets

Convert a sparse matrix to a dense representation .

python

Lines of Code : 53

License : Non-SPDX (Apache License 2.0)

Copy

def sparse_to_dense(sparse_indices,
                    output_shape,
                    sparse_values,
                    default_value=0,
                    validate_indices=True,
                    name=None):
  """Converts a sparse representa

Convert a sparse tensor to a dense tensor .

python

Lines of Code : 52

License : Non-SPDX (Apache License 2.0)

Copy

def sparse_tensor_to_dense(sp_input,
                           default_value=None,
                           validate_indices=True,
                           name=None):
  """Converts a `SparseTensor` into a dense tensor.

  For this sparse tensor

Converts a dense dataset to a sparse dataset .

python

Lines of Code : 47

License : Non-SPDX (Apache License 2.0)

Copy

def dense_to_sparse_batch(batch_size, row_shape):
  """A transformation that batches ragged elements into `tf.sparse.SparseTensor`s.

  Like `Dataset.padded_batch()`, this transformation combines multiple
  consecutive elements of the dataset, which

Community Discussions

Trending Discussions on dense

Not able to get reasonable results from DenseVariational

Model.evaluate returns 0 loss when using custom model

How to pass embedded data through a specific layers of TensorFlow model?

Problem with FULLY_CONNECTED op in TF Lite

How to fix DimensionError?

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

Tensorflow ValueError: Dimensions must be equal: LSTM+MDN

Find lowest real value in complex vector

SHAP DeepExplainer with TensorFlow 2.4+ error

Why I am retrieving high value loss for neural network regression

QUESTION

Not able to get reasonable results from DenseVariational

Asked 2021-Jun-15 at 16:05

I am trying a regression problem with the following dataset (sinusoidal curve) of size 500

First, I tried with 2 dense layer with 10 units each

...

ANSWER

Answered 2021-Mar-18 at 15:40

You need to define a different surrogate posterior. In Tensorflow's Bayesian linear regression example https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_Regression.ipynb#scrollTo=VwzbWw3_CQ2z

you have the posterior mean field as such

Source https://stackoverflow.com/questions/66418959

QUESTION

Model.evaluate returns 0 loss when using custom model

Asked 2021-Jun-15 at 15:52

I am trying to use my own train step in with Keras by creating a class that inherits from Model. It seems that the training works correctly but the evaluate function always returns 0 on the loss even if I send to it the train data, which have a big loss value during the training. I can't share my code but was able to reproduce using the example form the Keras api in https://keras.io/guides/customizing_what_happens_in_fit/ I changed the Dense layer to have 2 units instead of one, and made its activation to sigmoid.

The code:

...

ANSWER

Answered 2021-Jun-12 at 17:27

As you manually use the loss and metrics function in the train_step (not in the .compile) for the training set, you should also do the same for the validation set or by defining the test_step in the custom model in order to get the loss score and metrics score. Add the following function to your custom model.

Source https://stackoverflow.com/questions/67951244

QUESTION

How to pass embedded data through a specific layers of TensorFlow model?

Asked 2021-Jun-15 at 15:28

Good day, everyone.

I want to have two separate TensorFlow models (f and g) and train both of them on the loss of f(g(x)). However, I want to use them separately, like g(x) or f(e), where e is an embedded vector but received not from g.

For example, the classical way to create the model with embedding looks like this:

...

ANSWER

Answered 2021-Jun-15 at 10:53

This can be achieved by weight sharing or shared layers. To share layers in different models in keras, you just need to pass the same instance of layer to both of the models.

Example Codes:

Source https://stackoverflow.com/questions/67980314

QUESTION

Problem with FULLY_CONNECTED op in TF Lite

Asked 2021-Jun-15 at 13:22

I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:

...

ANSWER

Answered 2021-May-25 at 01:08

I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.

Source https://stackoverflow.com/questions/67677228

QUESTION

How to fix DimensionError?

Asked 2021-Jun-15 at 08:22

I am having a strange error still could not figure it out.

...

ANSWER

Answered 2021-Jun-15 at 08:22

The shape of b is (1,10) and the shape of the expression is (10). It will work if you do

Source https://stackoverflow.com/questions/67982354

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

Tensorflow ValueError: Dimensions must be equal: LSTM+MDN

Asked 2021-Jun-14 at 19:07

I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).

Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.

Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)

x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:

(TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))

...

ANSWER

Answered 2021-Jun-14 at 19:07

for MDN model , the likelihood for each sample has to be calculated with all the Gaussians pdf , to do that I think you have to reshape your matrices ( y_true and mu) and take advantage of the broadcasting operation by adding 1 as the last dimension . e.g:

Source https://stackoverflow.com/questions/67965364

QUESTION

Find lowest real value in complex vector

Asked 2021-Jun-14 at 16:19

How can I find the smallest positive real number in a complex vector of size N by 1 in Eigen3? For example, in this case I'd like to find the value 3.64038.

...

ANSWER

Answered 2021-Jun-14 at 14:40

One option is to create a logical array and then call Eigen::select on it. Inspired by https://forum.kde.org/viewtopic.php?f=74&t=91378

In this case:

Source https://stackoverflow.com/questions/67969172

QUESTION

SHAP DeepExplainer with TensorFlow 2.4+ error

Asked 2021-Jun-14 at 14:52

I'm trying to compute shap values using DeepExplainer, but I get the following error:

keras is no longer supported, please use tf.keras instead

Even though i'm using tf.keras?

...

ANSWER

Answered 2021-Jun-14 at 14:52

TL;DR

Add tf.compat.v1.disable_v2_behavior() at the top for TF 2.4+

calculate shap values on numpy array, not on df

Full reproducible example:

Source https://stackoverflow.com/questions/66814523

QUESTION

Why I am retrieving high value loss for neural network regression

Asked 2021-Jun-14 at 13:45

I have data in the following format consisting of 80 instances. I need to predict two-parameter latency and accuracy

...

ANSWER

Answered 2021-Jun-14 at 13:45

You have very less data, just 2 columns, 80 rows and 2 target variables. All you can do is:

Add more data.
Normalize your data and then feed it to the neural network.
If neural network not giving good accuracy, try Random Forest or XGBoost.

I also want to add one thing that is your neural network architecture is wrong. Dense layer with 2 outputs and a softmax activation isn't going to give you good result here. You have to use TensorFlow's Funtional API and make 1 input 2 output neural network architecture.

Source https://stackoverflow.com/questions/67970580

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install dense

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

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: