deepnet | Implementation of some deep learning algorithms | Machine Learning library

by nitishsrivastava Python Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | deepnet Summary

deepnet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Example Codes applications. deepnet has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However deepnet build file is not available. You can download it from GitHub.

Implementation of some deep learning algorithms.

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Quality

Security

License

Reuse

Support

deepnet has a medium active ecosystem.

It has 881 star(s) with 445 fork(s). There are 121 watchers for this library.

It had no major release in the last 6 months.

There are 60 open issues and 47 have been closed. On average issues are closed in 105 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deepnet is current.

Quality

deepnet has 0 bugs and 0 code smells.

Security

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

deepnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deepnet is licensed under the BSD-3-Clause License. This license is Permissive.

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

Reuse

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

deepnet has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed deepnet and discovered the below as its top functions. This is intended to give you an instant insight into deepnet implemented functionality, and help decide if they suit your requirements.

Computes up the input layer
Convolve input
Probability Max Pool
MaxPool op
Read data from disk
Load a sequence
Calculates the loss
ConvolutionOuter
Wrapper around local outp
Convolutional convolutional
Compute the derivative of the layer
Update the value of a parameter
Returns the loss function
Loads parameters from the hyperparameters
Gets the loss function
Load parameters from proto
Merge two arrays
Train one batch
Convert a model to a matrix
Displays the model
Sample the text
Create models from a pretrained model
Evaluate a single batch
Creates a data_pbtxt file
Evaluate the forward loss
Make trainers

Get all kandi verified functions for this library.

deepnet Key Features

No Key Features are available at this moment for deepnet.

deepnet Examples and Code Snippets

No Code Snippets are available at this moment for deepnet.

Community Discussions

Trending Discussions on deepnet

How to use database models in Python Flask?

Caret: How to set up custom model deepnet

Error in Calculating neural network Test Accuracy

Fixing incorrect dimensions in PyTorch neural network

Trying to fix the construction of a neural network (error message: negative dimension?)

QUESTION

How to use database models in Python Flask?

Asked 2021-Jun-15 at 02:32

I'm trying to learn Flask and use postgresql with it. I'm following this tutorial https://realpython.com/flask-by-example-part-2-postgres-sqlalchemy-and-alembic/, but I keep getting error.

...

ANSWER

Answered 2021-Jun-15 at 02:32

I made a new file database.py and defined db there.

database.py

Source https://stackoverflow.com/questions/67976688

QUESTION

Caret: How to set up custom model deepnet

Asked 2021-Mar-08 at 22:39

I want to use some of the parameters of the original deepnet package, so I set up a custom model. I read Caret's documentation (Custom Model), but it doesn't work.

Here is my code for setting up the customized model:

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ANSWER

Answered 2021-Mar-08 at 19:27

I found the answer myself...

It was a simple mistake: I had to remove the quotation marks in method when applying the customized model:

Source https://stackoverflow.com/questions/66535566

QUESTION

Error in Calculating neural network Test Accuracy

Asked 2020-Jun-10 at 15:58

I tried to train my neural network, and then evaluate it's testing accuracy. I am using the code at the bottom of this post to train. The fact is that for other neural networks, I can evaluate the testing accuracy with my code without issue. However, for this neural network (which I constructed correctly according to the description of the neural network paper), I can't evaluate the testing accuracy properly and its giving me the traceback below. So maybe something's wrong in my forward pass?

Here is the training and testing code:

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ANSWER

Answered 2020-Jun-10 at 05:35

You are trying to load a state dict that belongs to another model.

The error shows that your model is the class AlexNet.

Source https://stackoverflow.com/questions/62295662

QUESTION

Fixing incorrect dimensions in PyTorch neural network

Asked 2020-Jun-07 at 01:48

I am trying to train my neural network, which is written in PyTorch, but I got the following traceback because of incorrect dimensions. Got the following traceback

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ANSWER

Answered 2020-Jun-06 at 21:50

The first convolution doesn't use padding.

Source https://stackoverflow.com/questions/62237372

QUESTION

Trying to fix the construction of a neural network (error message: negative dimension?)

Asked 2020-May-24 at 21:37

This is a model based on the description on page 12, section B.3 of the paper https://arxiv.org/pdf/1609.04836.pdf

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ANSWER

Answered 2020-May-24 at 08:13

this means that you can't apply any operation because you reduce too much the dimension inside your network (it is below 0).

Looking at your data format seems like your images are (3, 32, 32), so the channels are the first dimension. Keras by default applies convolution with channels in the last dimensions. To override the error try to define data_format='channels_first' in convolutional and in pooling layers

Source https://stackoverflow.com/questions/61982617

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

Vulnerabilities

No vulnerabilities reported

Install deepnet

You can download it from GitHub.
You can use deepnet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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 .

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