neural-network | For educational purposes | Machine Learning library
kandi X-RAY | neural-network Summary
kandi X-RAY | neural-network Summary
A dead simple neural network. For educational purposes only.
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Top functions reviewed by kandi - BETA
- Create a full homology
- Activate the activation coefficient .
- Get the neuron value .
- Set value .
- Create a new output .
- Adds a connection to the pool .
neural-network Key Features
neural-network Examples and Code Snippets
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Trending Discussions on neural-network
QUESTION
I am training a VAE model with 9100 images (each of size 256 x 64). I train the model with Nvidia RTX 3080. First, I load all the images into a numpy array of size 9100 x 256 x 64 called traindata
. Then, to form a dataset for training, I use
ANSWER
Answered 2021-Jun-04 at 14:50That's because holding all elements of your dataset in the buffer is expensive. Unless you absolutely need perfect randomness, you should use a smaller buffer_size
. All elements will eventually be taken, but in a more deterministic manner.
This is what's going to happen with a smaller buffer_size
, say 3. The buffer is the brackets, and Tensorflow samples a random value in this bracket. The one randomly picked is ^
QUESTION
I'm trying to train a CoreML sound classifier on device, on iOS, and I have been struggling to find learning resources on the topic. The sound classifier is used to determine whether a snippet of music is similar to a collection of other songs. Hence the output of the classifier is just a label of either "match" / "no match".
It is so simple to train with the CreateML app workflow. I am simply trying to get the same kind of training on device in iOS, but as far as I know (please correct me if I'm wrong) iOS doesn't support createML.
I have been trying to adapt code from various source to get this to work in an iOS playground. I can only find resources on training image classifiers, these two have been the most helpful (1, 2).
Please see the code that I have come up with so far below.
...ANSWER
Answered 2021-Jun-02 at 18:52I have managed to solve the error related to the mlUpdate task, the issue was that I was referencing the .mlmodel instead of the compiled version, which is .mlmodelc . When building the iOS app from Xcode this file is automatically generated.
I now get the following error:
QUESTION
Say I have some text and I want to classify them into three groups food, sports, science
. If I have a sentence I dont like to each mushrooms
we can use wordembedding (say 100 dimensions) to create a 6x100
matrix for this particular sentense.
Ususally when training a neural-network our data is a 2D array with the dimensions n_obs x m_features
If I want to train a neural network on wordembedded sentences(i'm using Pytorch) then our input is 3D n_obs x (m_sentences x k_words)
e.g
...ANSWER
Answered 2021-May-05 at 14:51Technically the input will be 1D, but that doesn't matter.
The internal architecture of your neural network will take care of recognizing the different words. You could for example have a convolution with a stride equal to the embedding size.
You can flatten a 2D input to become 1D and it will work fine. This is the way you'd normally do it with word embeddings.
QUESTION
I have implemented and trained the model from the following website, and using the author's source code:
I am now running an image through the trained network and want to get the network output (feature maps etc.) at every stage.
My ApproachTo that end, I have tried making sub-models from groups of layers from the full model (called sizedModel
in my code) and checking their output.
I have done that for the first L1(Conv2D)
ANSWER
Answered 2021-May-02 at 03:16If I understand your question properly, you want to get output feature maps of each layer of a model. Normally, as we mentioned in the comment box, a model with one (or multiple) inputs and one (or multiple) outputs. But in order to inspect the activation feature maps of inside layers, we can adopt some strategies. Some possible scenarios: (1). Want to get output feature maps of each layer in run-time or training time. (2). Want to get output feature maps of each layer in the inference time or after training. And as you quoted:
I am now running an image through the trained network and want to get the network output (feature maps etc.) at every stage.
That goes to number 2, get the feature maps in inference time. Below is a simple possible workaround to do this. First, we build a model, and then after training, we will modify the trained model to get feature maps of each layer within it (technically creating the same model with some modification).
QUESTION
The script runs correctly, and it is using the GPU as I have seen activity on my CUDA GPU Performance when the script finally runs.
However, it takes 166 secs to actually start running the model, running the model takes 3 seconds.
My setup is the following:
...ANSWER
Answered 2021-Apr-29 at 15:44RTX 3060
cards are based on the Ampere
architecture for which compatible CUDA version start with 11.x.
Your issue can be resolved once you upgrade tensorflow version to 2.4.0
, CUDA to 11.0
and cuDNN to 8.0
.
For more details you can refer here.
QUESTION
I have written code for a neural network but when I train my network it does not produce the desired output (network not learning and sometimes NaN values when training). What wrong with my back propagation algorithm? Attached below is how I derived the formula for weight and bias gradients respectively. Full code can be found here.
...ANSWER
Answered 2021-Mar-17 at 02:42The NaN you see is due to underflow, you need to use BigDecimal class instead of double for higher precision. Refer these for better understanding bigdecimal class java sample use , BigDecimal API Reference
QUESTION
Thank you for @Prune's critical comments on my questions.
I am trying to find the relationship between batch size and training time by using MNIST dataset.
By reading numerous questions in stackoverflow, such as this one: How does batch size impact time execution in neural networks? people said that the training time will be decreased when I use small batch size.
However, by trying out these two, I found that training with batch size == 1 takes way more time than batch size == 60,000. I set epoch as 10.
I split my MMIST dataset into 60k for the training and 10k for the testing.
This below is my code and results.
...ANSWER
Answered 2021-Mar-20 at 00:42This is a borderline question; you should still be able to extract this understanding from the basic literature ... eventually.
Your insight is exactly correct: you are measuring execution time per epoch, rather than total Time-to-Train (TTT). You have also carried the generic "smaller batches" advice ad absurdum: a batch size of 1 is almost guaranteed to be sub-optimal.
The mechanics are very simple at a macro level.
With a batch size of 60k (the entire training set), you run all 60k images through the model, average their results, and then do one back-propagation for that average result. This tends to lose the learning you can get from focusing on little-seen features.
With a batch size of 1, you run each image individually through the model, average the one result (a very simple operation :-) ), and do a back propagation. This tends to over-emphasize individual effects, especially retaining superstitious effects from each single image. It also gives too much weight to the initial assumptions of the first few images.
The most obvious effect of the tiny batch size is that you're doing 60k back-props instead of 1, so each epoch takes much longer.
Either of these approaches is an extreme case, usually absurd in application.
You need to experiment to find the "sweet spot" that gives you the fastest convergence to acceptable (near-optimal) accuracy. There are a few considerations in choosing your experimental design:
- Memory size: you want to be able to ingest the entire batch into memory at once. This allows your model to pipeline reading and processing. If you exceed available memory, you will lose a lot of time to swapping. If you under-use the memory, you leave some potential performance untapped.
- Processors: if you're on a multi-processor chip, you want to keep them all busy. If you care to assign processors through your OS controls, you'll also want to play with how many to assign to model computation, and how many to assign to I/O and system use. For instance, in one project I did, our group found that our 32 cores were best used with 28 allocated to computation, 4 reserved for I/O and other system functions.
- Scaling: some characteristics work best in powers of 2. You may find that a batch size that is 2^n or 3 * 2^n for some n, works best, simply because of block sizes and other system allocations.
The experimental design that has worked best for me over the years is to start with a power of 2 that is roughly the square root of the training set size. For you, there's an obvious starting guess of 256. Thus, you'd run experiments at perhaps 64, 128, 256, 512, and 1024. See which ones give you the fastest convergence.
Then do one step of refinement, using that factor of 3. For instance, if you find that the best performance comes at 128, also try 96 and 192.
You will likely see very little difference between your "sweet spot" and the adjacent batch sizes; this is the nature of most complex information systems.
QUESTION
I'm having some trouble following a guide at: https://www.geeksforgeeks.org/ml-neural-network-implementation-in-c-from-scratch/ I have installed the eigen library with vcpkg and it seems to be working because it gives no error.
Code:
...ANSWER
Answered 2021-Mar-02 at 21:26Exactly what it says on the tin, the list of members in the class declaration:
QUESTION
Colab link is here:
The data is imported the following was
...ANSWER
Answered 2021-Mar-01 at 15:15You set label_mode='categorical'
then this is a multi-class classification and you need to use softmax
activation in your last dense layer. Because softmax force the outputs sum to be equal to 1. You can kinda interpret them as probabilities. With sigmoid
it will not be possible to find the dominant class. It can assign any values without restriction.
My model's last layer: Dense(5, activation = 'softmax')
My model's loss: loss=tf.keras.losses.CategoricalCrossentropy()
, same as yours. Labels are one-hot-encoded in this case.
Explanation: I used a 5 class classification for demo purposes, but it follows the same logic.
QUESTION
I'm interested in training both a CNN model and a simple linear feed forward model in PyTorch, and after training to add more filters -- to the CNN layers, & neurons -- to the linear model layers and the outputs (e.g. from binary classification to multiclass classification) of both. By adding them I specifically mean to keep the weights that were trained constant, and to add random initialized weights to the new, incoming weights.
There's an example of a CNN model here, and an example of a simple linear feed forward model here
...ANSWER
Answered 2021-Feb-26 at 21:20This one was a bit tricky and requires slice
(see this answer for more info about slice
, but it should be intuitive). Also this answer for slice trick. Please see comments for explanation:
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