backprop | Backprop makes it simple to use , finetune , and deploy state | Natural Language Processing library

by backprop-ai Python Version: 0.1.3 License: Non-SPDX

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

kandi X-RAY | backprop Summary

backprop is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Tensorflow, Bert, Neural Network, Transformer applications. backprop has no bugs, it has no vulnerabilities, it has build file available and it has low support. However backprop has a Non-SPDX License. You can install using 'pip install backprop' or download it from GitHub, PyPI.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models. Solve a variety of tasks with pre-trained models or finetune them in one line for your own tasks.

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Quality

Security

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Support

backprop has a low active ecosystem.

It has 240 star(s) with 12 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 4 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of backprop is 0.1.3

Quality

backprop has no bugs reported.

Security

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

License

backprop has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Load a trained model
Download a file
Resize an image
Builds the model from the given state dictionary
Finetuning optimizer
Setup pre - trained model
Upload a model to the model
Save a model to disk
Finetune the model
Set the model to the given device
Performs a single training step
Perform validation step
Process a single batch
Step single label
Step through the triplet
Performs a single step of training step
Performs a single step
Compute the logits for the given image and text
Calculate cosine similarity
Forward forward computation
Forward attention
Create a pretrained model from a pretrained model
Finetuning
Fills training and validation
Fetches training and validation
Perform cosine similarity

Get all kandi verified functions for this library.

backprop Key Features

No Key Features are available at this moment for backprop.

backprop Examples and Code Snippets

Adds a forward accumulator .

python

Lines of Code : 89

License : Non-SPDX (Apache License 2.0)

Copy

def AddForwardAccumulator(self, value, dead_branch=False):
    """Add an accumulator for each forward tensor that is needed in backprop.

    This is added to the forward loop at the first time when a tensor
    in the forward loop is used by backpro

Adds a backprop accumulator accumulator .

python

Lines of Code : 81

License : Non-SPDX (Apache License 2.0)

Copy

def AddBackpropAccumulator(self, op, grad):
    """Add an accumulation loop for every loop invariant.

    This is added to the backprop loop. It is used to accumulate partial
    gradients within each loop iteration. Called when in the gradient whil

Adds a backprop iteration .

python

Lines of Code : 65

License : Non-SPDX (Apache License 2.0)

Copy

def AddBackpropLoopCounter(self, count, outer_grad_state):
    """Add the backprop loop that controls the iterations.

    This is added to the backprop loop. It is used to control the loop
    termination of the backprop loop. Called in the outer co

Community Discussions

Trending Discussions on backprop

Use python pandas rows to create permutations to create all possible scenarios

Python-coded neural network does not learn properly

Pytorch Model always outputs 0.5 for an unkown reason

One liner tensor with conditions

Feed Forward Neural Network Always outputs Random but Similar Values

Load a saved NN model in different Python file

torch : Dijkstra's algorithm

mat1 dim 1 must match mat2 dim 0 -Pytorch

Dot product along a dimension

Checking Neural Network Gradient with Finite Difference Methods Doesn't Work

QUESTION

Use python pandas rows to create permutations to create all possible scenarios

Asked 2021-Jun-02 at 20:24

I have 5 sheets in excel with different parameters.

history

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ANSWER

Answered 2021-Jun-02 at 20:24

If idx is the index of your dataframes:

Source https://stackoverflow.com/questions/67794643

QUESTION

Python-coded neural network does not learn properly

Asked 2021-May-30 at 08:52

My network is not trained to recognize inputs separately, it either outputs the averaged result or becomes biased to one particular output. What am I doing wrong?

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ANSWER

Answered 2021-May-30 at 08:52

The matrix math of backpropagation is quite tough. It is especially confusing that the length of the lists of weight matrices and deltas (actually the list of bias arrays too) should be one less than the amount of layers in a network which makes indexing confusing. Apparently, the problem was due to misindexing. Finally it works!

Source https://stackoverflow.com/questions/67744827

QUESTION

Pytorch Model always outputs 0.5 for an unkown reason

Asked 2021-May-21 at 22:38

I have a pytorch model I'm trying to use to do facial recognition. I am using the same model structure, loss, and optimizer as a working code, but it seems like the backprop won't do anything, any output of the NN is just 0.5. Here is the code, any help/suggestions is/are appreciated.

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ANSWER

Answered 2021-May-21 at 22:38

You applied both relu and sigmoid to your final output. In this case, you want to apply only sigmoid.

Source https://stackoverflow.com/questions/67644421

QUESTION

One liner tensor with conditions

Asked 2021-May-08 at 15:59

I have two tensors:

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ANSWER

Answered 2021-May-08 at 15:51

You can sum multiplicative masks of the conditions:

Source https://stackoverflow.com/questions/67448750

QUESTION

Feed Forward Neural Network Always outputs Random but Similar Values

Asked 2021-Apr-17 at 18:12

I recently coded a neural network based on this online book and Sebastian Lague's brief series on neural networks on youtube. I coded it as faithfully to the original as possible but it didn't end up working. I am trying to solve a simple XOR problem with it but it always seems to give me random but similar values. I even tried copying and pasting the author's code, without changing anything, but it still didn't work.

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ANSWER

Answered 2021-Apr-17 at 18:12

I seem to have fixed it. I made three main changes:

I switched the a and o in the output layer error calculation which then looked like this: error = (o - a) * self.activationPrime( self.zCollection[-1] ).
When updating the weights and biases I replaced

Source https://stackoverflow.com/questions/67133094

QUESTION

Load a saved NN model in different Python file

Asked 2021-Apr-13 at 22:01

I am trying to implement the code from a Pytorch beginner's tutorial. But I have written the code for loading the saved model in another Python file. The FashionClassify file contains the code exactly as its in the tutorial.

Below is the code:

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ANSWER

Answered 2021-Apr-13 at 22:01

That's what happens when you import another file. All the code gets rerun.

Instead, in your training file:

Source https://stackoverflow.com/questions/67012541

QUESTION

torch : Dijkstra's algorithm

Asked 2021-Apr-13 at 16:15

I am working on 3D point clouds. I have the SPARSE MATRIX representation of the graph structure of the point cloud (like csr_matrix in scipy.sparse). I want to club together the points that are within certain threshold of the Geodesic distance (approximated by the path length in the graph) and process them together. TO FIND such points, I need to run some shortest path finding algorithm like Dijkstra's. In a nutshell, my idea is like this

Sample K points out of N points (that I could do using Furthest Point Sampling)
Find the nearest Geodesic neighbours (using BackProp supported algorithm) for each of K points
Process the neighbours for each point using some Neural Network

This will go in my forward function. Is there a way to implement Dijkstra’s in my functionality?

Or any other idea that I can implement?

Thank you very much!

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ANSWER

Answered 2021-Apr-13 at 16:15

I created my custom implementation for Dijkstra using priority queues as discussed here For the same, I created a custom PriorityQ class using torch function as below

Source https://stackoverflow.com/questions/66782954

QUESTION

mat1 dim 1 must match mat2 dim 0 -Pytorch

Asked 2021-Apr-13 at 04:50

I have attempted to solve this error but it has been to no avail. My CNN model is below:

The shape of X_train and X_test are: X_train shape: torch.Size([12271, 3, 100, 100]) | X_test shape: torch.Size([3068, 3, 100, 100])

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ANSWER

Answered 2021-Apr-13 at 04:50

Your final convolution's (conv3) output dimensions don't match the input dimension of first Linear layer. self.conv3's output shape will be BatchSize x 128 x 12 x 12 when resized:

Source https://stackoverflow.com/questions/67065236

QUESTION

Dot product along a dimension

Asked 2021-Mar-17 at 00:02

I have two tensors of shape [B, 3 , 240, 320] where B represents the batch size 3 represents the channels, 240 the height(H), 320 the width(W).

I need to find the dot product along the channels dimension(3 channels) thus the resulting tensor would be of shape [B, 1 , 240, 320]. My tensors have float32 elements in gpu(cuda to backprop).

Can you all please suggest how I can do that?

Thanks!

More clarification:

Let's say we have B=10, H=100, W=200. So from the above would be common for both the first and seconds tensors. If we keep B, H, W constant we get a 1D vector as the resultant tensor(with 3 elements). I need to take the dot product of these two vectors. Thus the resultant tensor is of dimension [B, 1, 240, 320]

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ANSWER

Answered 2021-Mar-17 at 00:02

Dot product is the summation of multiplication of values in two vectors:

So I am guessing you want to multiply all values along the channel dimension and need to find the summation of the result, please correct me if my understanding is wrong.

Source https://stackoverflow.com/questions/66663531

QUESTION

Checking Neural Network Gradient with Finite Difference Methods Doesn't Work

Asked 2021-Feb-28 at 20:30

After a full week of print statements, dimensional analysis, refactoring, and talking through the code out loud, I can say I'm completely stuck.

The gradients my cost function produces are too far from those produced by finite differences.

I have confirmed my cost function produces correct costs for regularized inputs and not. Here's the cost function:

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ANSWER

Answered 2021-Feb-28 at 14:19

One thought: I think your perturbation is a little large, being 1e-4. For double precision floating point numbers, it should be more like 1e-8, i.e., the root of the machine precision (or are you working with single precision?!).

That being said, finite differences can be very bad approximations to true derivatives. Specifically, floating point computations in numpy are not deterministic, as you seem to have found out. The noise in evaluations can cancel out many significant digits under some circumstances. What values are you seeing and what are you expecting?

Source https://stackoverflow.com/questions/66320255

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