I need to implement a classification application for neuron-signals. In the first step, I need to train a denoising autoencoder(DAE) layer for signal cleaning then, I will feed the output to a DBN network for classification. I tried to find support for these types in Tensorflow but all what I found was two models CNN and RNN. Does anyone has an idea about a robust implementation for these two models using Tensorflow?
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I would like to implement an artificial neural network via Hebbian learning for MNIST multiclass classification.
Is there a way to set or change learning rules in Keras for define an Hebbian ANN?
Or a method to change the update of the weights of the network based on a rule decided by me instead of the classic Keras settings?
Otherwise I would have thought of defining a neural network without the aid of the libraries but there would certainly be less accuracy and I would not have the practicality given by the libraries
I am trying to re-create a neural network mentioned in the following paper: "https://www.frontiersin.org/articles/10.3389/fneur.2020.00375/full"
However there's a lot of missing information. The neural network that i am trying to design:
I am a bit confused about the model above it doesn't look they concatenate the outputs from the 3 pretrained networks. It seems like they are using the imported networks as filters.
So my question is the following:
Is it possible to train 3 (4 with the last two dense and output layer) models seperately and use the 3 networks as filters. Pass their output (possibly flattened) to the last fully connected layers and get a classification and how is this usually accomplished training wise?
Or is the best approach to use the keras functional api to create a multi-input model where the outputs are concatenated before fed into the last neural network? (FYI: I've never used this api before so i don't know the limitations...)
I'm trying to train an inventory-tracking application using tensorflow object detection api and I've used this tutorial.
My image dataset is too small for training all the weights in the neural network and I want to train the few latter layers or even just the softmax layer. But I didn't find any tutorial which tells me how to declare which layers I want to train.
How can I do this?
Can anyone give me a link or github issue about this?
i am new to programming, python and all. i am tasked with a work at school that requires me to develop and evaluate abusive language detection models from a given dataset. my proposed model must be a Convolutional Neural Network with an appropriate embedding layer as a first layer. my problem is i don't know how to start as i am very new to this with no prior knowledge
First, you can start reading and have understanding of a CNN first.
https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Lastly, you can check few sample implementation here
Keras:
https://towardsdatascience.com/building-a-convolutional-neural-network-cnn-in-keras-329fbbadc5f5
Pytorch:
https://adventuresinmachinelearning.com/convolutional-neural-networks-tutorial-in-pytorch/
Tensorflow
https://www.tensorflow.org/tutorials/images/cnn
Lately I was on a Data Science meetup in my city, there was a talk about connecting Neural Networks with SVM. Unfortunately presenter had to quit right after presentation, so I wasn't able to ask some questions.
I was wondering how is that possible ? He was talking about using neural networks for his classification, and later on, he was using SVM classifier to improve his accuracy and precision by about 10%.
I am using Keras for Neural Networks and SKlearn for the rest of ML.
This is completely possible and actually quite common. You just select the output of a layer of the neural network and use that as a feature vector to train a SVM. Generally one normalizes the feature vectors as well.
Features learned by (Convolutional) Neural Networks are powerful enough that they generalize to different kinds of objects and even completely different images. For examples see the paper CNN Features off-the-shelf: an Astounding Baseline for Recognition.
About implementation, you just have to train a neural network, then select one of the layers (usually the ones right before the fully connected layers or the first fully connected one), run the neural network on your dataset, store all the feature vectors, then train an SVM with a different library (e.g sklearn).