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Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network ...

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Is it a good practice to always apply `ReduceLROnPlateau()`, given that models benefit from reducing learning rate once learning stagnates?

The rationale behind the keras function ReduceLROnPlateau() is that models benefit from reducing learning rate once learning stagnates. Is it a good practice to ...
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0answers
11 views

Matrix multiplication issue (shapes not alligned)

I am building an RNN using numpy only and have started on the forward propagation section. However i am having some issues aligning my matrices. The issue is on this line: ...
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1answer
12 views

How to compute accuracy for CNN when outputs are one-hot encoded

I want to compute accuracy of my model on test data. I have three classes so actual output might look like this : [0 1 0] and the predicted output might look ...
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0answers
13 views

How to program derivatives for recurrent weights

I understand that the formula for the gradient of W and U is this: How might I go around programming ds/dst-1 for example?
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1answer
19 views

parse pdf into Json or Xml [on hold]

I want to create a neural net that can obtain some specific words from a pdf document into JSON or XML. For example let's assume that I have a pdf containing some information about countries and i ...
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1answer
12 views

Is the multilayer perceptron only able to accept 1d vector of input data? If yes, why is this so?

I am going through the tutorial at the link below which uses MNIST handwritten digit database. https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-...
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0answers
12 views

How to implement YOLO in my CNN model?

I build a CNN model using keras on the cat vs dog dataset. Now what I want is with the image classification my model should also locate that animal on that image. I searched on web and found that YOLO ...
2
votes
1answer
10 views

Setting up Neural Network for this problem

I have a question regarding neural networks considering I am not an expert in NN. Assume have a 5 by 5 grid that depending on me pushing any square (or combination of squares) some of those squares (...
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0answers
23 views

how to check the classes the keras classifier/Neural Network is trained on?

I have trained a CNN using keras for Image classification with 3 classes. The results are bad and I'm trying to understand what the classifier has learnt and what it has not. It's only giving me an ...
2
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0answers
18 views

Deep Learning: Does starting the training on a smaller subset of the data make sense?

I trained a deep neural network with a small subset of my data, which allowed me to go through many epochs in a short amount of time and allowed the model to perform reasonably, then I gave it the ...
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2answers
20 views

Custom conditional Keras metric

I am trying to create the following metric for my neural network using keras $$ s = \left\{ \begin{array}{ll} \sum_{i=1}^{n} e^{\frac{-d_i}{10}}-1 & \quad d < 0 \\ ...
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1answer
20 views

Multiple Regression Outputs from neural networks [on hold]

I have a regression problem which I have to predict 10 numerical values from a provided data. For example let's say I have a data set containing `X1, X2, X3, X4, X5, X6 ...2001Q1 ,2001Q2 ,2001Q3 ,...
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1answer
5 views

Train neural network for regression with negative samples

I have training samples which have have vector $\vec x$ as input and a vector $\vec y$ as output - both vectors have real (float) numbers $\in \mathbb R$ as entries. I want to train a neural network ...
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1answer
13 views

Keras backend function equivalent for str.format [on hold]

How to perform the following function using Keras backend? "{0:b}".format(37)
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0answers
16 views

Intuition / Importance of intermediate supervision in deep learning

These days, I have seen many papers using intermediate supervision. Single Network When using a single neural network, multiple neurons output predictions, perhaps by processing data in different ...
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0answers
4 views

Using two generative adversarial nets to classify articles - what is a good approach?

I'm trying to create a deep learning network to classify news article based on the text and associated image. The idea comes from a novel use of GANs to classify based on generated data. My approach ...
2
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2answers
24 views

Is it a good idea to normalize the outputs of a Neural Network for Regression, when the different outputs vary in magnitude?

I understood that it is not necessary to scale the output of a neural Network when I predict a single value via regression. Is it necessary do normalize the Outputs of my neural Network if I have ...
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1answer
9 views

Adding the input layer - units with a decimal

I took the course Machine Learning A-Z from Udemy and am trying to apply what I learned in the tutorials. Theye taught us in the "Adding the input layer" portion of an ANN that the units is based off ...
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0answers
8 views

Simple explanation of LSTM data set and training phase

I cannot understand the training procedure of the LSTM (and other recurrent nets). My data is time series of length 2000 points. As suggested on the internet (and keras framework), this should be ...
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0answers
15 views

GAN isn't stable

I built GAN. When I checked generator network and it is very unstable. This is my code for generator: ...
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0answers
11 views

What research is this similar to — Deep CNN for generating images by classifying individual pixels — not a GAN [on hold]

Generative adversarial models (GAN) use deconvolution to generate images (generator model) combined with convolution to distinguish between real and generated (determinator). However, multiple models ...
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1answer
29 views

using unsupervised learning algorithms on images

I am working on a project to classify images of types of cloth (shirt, tshirt, pant etc). While this is a standard supervised classification problem, the accuracy of the neural network is not good. ...
2
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0answers
37 views

How to backpropogate?

I am following this paper. You don't have to read it all as I'm about to explain the best I can. Consider the network that they have developed: This network takes as an input a vector of 10 values. ...
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0answers
32 views

Loss function for Hierarchical Multi-label classification

I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels (e.g., a simple MLP branch inside a ...
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1answer
26 views

How to display the value of activation?

I have built my network and would like to see how the activation of a particular layer change after each epoch of training. For example, as code shown below, I want to see the activation values of "...
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0answers
14 views

Backpropogation through FC layer [closed]

I have a CNN. Layers are as follows ConvLayer -> Relu -> maxpool -> FC -> output(softmax) Input to FC layer is a vector of 384. Neurons in FC layer ...
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0answers
8 views

How to represent data-set in a RNN?

I could not find this anywhere, how is a data-set represented in an RNN (one hot vector method)? Lets take an example of CNN, here we have fixed pixel * pixel size ...
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2answers
687 views

Should use sklearn or tensorflow for neural networks?

I have just started learning Neural Networks for deep learning from cs231. I am trying to implement Neural Network in Python. I am looking at using Tensorflow or scikit-learn. What are some pros and ...
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0answers
5 views

Find a most relevant geopoint from given set of points with parameters [closed]

I have a task to get most probable geolocations for text articles. I do not need to program any NLP, because I use OpenCalais API for this part of task. After I get entities from OpenCalais I filter ...
2
votes
1answer
100 views

In RNNs why do networks always use the last output vs the last input?

All the descriptions of RNNs introduce some equation like: $\ h_t = f_W(h_{t-1},x_t)$ and I'm wondering why we don't just go straight to the "source", ie the last input like : $\ h_t = f_W(x_{t-1}...
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1answer
19 views

How to tinker with CNN architectures?

I was thinking of creating a CNN. Now it is known CNN takes long times to train so it is advisable to stick to known architectures and hyper-parameters. My question is: I want to tinker with the CNN ...
3
votes
1answer
19 views

input and output to fully connected layer

I have an input vector of size 384. It is to be inputted to FC layer with one hidden layer. The output should be a vector of 3. How is it possible? What is the math behind the calculations in FC layer?...
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2answers
17 views

Preprocessing of Sudoku Dataset from Kaggle

Dataset: https://www.kaggle.com/bryanpark/sudoku I would like to create a neural network for this Dataset. Feature: ...
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1answer
23 views

How to verify hand written signature?

I trying to create a model for determining whether a questioned hand written signature matches known signature samples, and predict if the signature is genuine or forgeries. I'm guessing I'll have to ...
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0answers
13 views

overffitting and underfitting in Network neural [closed]

How to realize that my neural network has the problem of overffitting or undefitting? Is this problem related to learning rate? when i use ...
2
votes
1answer
14 views

Backpropogation through Maxpool and relu

Why is backpropagation through maxpool and relu needed? Purpose of backpropagation is to update weights while on the other hand maxpool and relu only perform a simple operation on the input. They don'...
2
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2answers
27 views

How does a FC layer work in a typical CNN

I am new to CNNs and NNs. I am reading this blog: CNN and I am confused about this part: What confuses me is the operation that will be performed on an input vector/matrix. Will we be using a typical ...
2
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0answers
18 views

Full convolution vs convolution operation [closed]

I am trying to implement backpropogation algorithm from scratch. For that I read this blog:backpropogation You don't have to read the entire blog. At the end is mentioned formula to find gradients. ...
3
votes
1answer
37 views

How to make it possible for a neural network to tune its own hyper parameters?

I am curious about what would happen to hyperparameters when they would be set by a neural network itself or by creating a neural network that encapsulates and influences the hyperparameters of the ...
1
vote
1answer
25 views

General equation - calculating backpropagation [closed]

How to calculate new weights for neurons - what is the general equation for it?
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0answers
11 views

Is it possible to train for precision in Tensorflow?

Can I train a binary classifier in Tensorflow to maximize precision?
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1answer
17 views

Backpropagation - simplest explanation

Could you please explain in simplest way the algorithm (mathematical equation) of back-prop? I read lot of articles, I know for what it is, and I understand the intuition behind it, but I still don't ...
1
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1answer
18 views

Does MLPClassifier (sklearn) support different activations for different layers?

Documentation says 'activation' argument specifies "Activation function for the hidden layer". Does that mean you cannot use different activation functions in different layers?
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0answers
9 views

Why my Simple DL model with default SGD working very poorly in keras?

When I'm answering questions in StackOverflow, I found this question about the poor performance of DL model. I tried to implement the same code in my machine to see whats happening. I have also ...
2
votes
1answer
16 views

What is max aggregation on a set of word embeddings?

In a paper I see: $\mathcal{Q}$ is a set of words. $\psi_{G^w}$ are word embeddings. so, $\{\psi_{G^w}(w_t), \forall w_t \in \mathcal{Q}\}$ gives me a set of embeddings for all words in $\mathcal{Q}...
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0answers
30 views

Formula for backpropgation?

I am implementing a CNN from scratch. And for that I needed to understand backpropogation. Given that, we have a standard CNN with on Convlayer accompanied by Relu, Maxpool and then Fc layer and ...
1
vote
2answers
24 views

Few activation functions handling various problems - neural networks

How can a few activation functions in neural networks handle so many different problems? I know some basics theory behind ANN, but I can't get what functions like the sigmoid function etc. have in ...
2
votes
1answer
35 views

Python OneHotEncoder Using Many Dummy Variables or better practice?

I am building a neural network and am at the point of using OneHotEncoder on many independent(categorical) variables. I would like to know if I am approaching this properly with dummy variables or if ...