Questions tagged [neural-network]
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 designer having had a model of a real system.
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Loss Function for Probability Regression
I am trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
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Training value neural network AlphaGo style
I have been trying to replicate the results obtained by AlphaGo following their supervise learning protocol. The papers specify that they use a network that has two heads: a value head that predicts ...
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How to predict advantage value in deep reinforcement learning
I'm currently working on a collection of reinforcement algorithms: https://github.com/lhk/rl_gym
For deep q-learning, you need to calculate the q-values that should be predicted by your network. There ...
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Fine tuning accuracy lower than Raw Transfer Learning Accuracy
I've used transfer learning on Inception V3 with ImageNet weights on Keras with Tensorflow backend on python 2.7 to create an image classifier. I first extracted and saved the bottleneck features from ...
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Unable to transform (greatly performing) Autoencoder into Variational Autoencoder
Following the procedure described in this SO question, I am trying to transform my (greatly performing) convolutional Autoencoder into a Variational version of the same Autoencoder. As explained in ...
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Maths of Xavier initialization
The paper I read is Glorot et al (2010). And the math part is in Section 4.2.1.
Formula (5) and (10) make sense to me but I cannot derive formula (6) and (7) myself from (2) and (3).
I found many ...
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Learning a logical function with a 2 layer BDN network - manual weight setting rule question?
So I am trying to construct a 2-layer network of binary decision neurons as proposed by McCullough and Pitts (1943) to learn a logical function (a composition of AND's and OR's) such as:
$((\neg x_1\...
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how to propagate error from convolutional layer to previous layer？
I've been trying to implement a simple convolutional neural network. But I've been stuck at this problem for over a week.
To be specific, assume there are 3 layers in a convolutional pass, marked as ...
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What ML architecture fits fixed length signal regression?
My problem is of regression type -
How to estimate a fish weight using a fixed-length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
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Tensorflow, Optimizer.apply_gradient: 'NoneType' object has no attribute 'merge_call'
My program gives the following error message:
...
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LSTM Long Term Dependencies Keras
I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras.
In Keras the input data structure for X ...
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Does Word2Vec's skip-gram NNLM even produce context words?
Let me first establish what CBoW and skip-gram are supposed to do. You can skip to the next section if you think this is unnecessary.
Background
My understanding is that Word2Vec is a suite of 2 ...
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Training Machine Learning Model - Neural Network - Islands Problem
I was working on the following leetcode problem:
Given a 2d grid map of '1's (land) and '0's (water), count the number
of islands. An island is surrounded by water and is formed by
connecting ...
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Backpropagation: Relevance of the error signal of a neuron
During my quest to understand back propagation in a more rigorous approach I have come across with the definition of error signal of a neuron which is defined as follows for the $j^{\text{th}}$ neuron ...
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Saving and loading keras.callbacks.History object with np.save and np.load
I have been saving my training history in keras as follows:
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