neural networks - Explanation of Spikes in training loss vs. iterations ...
41 I am training a neural network using i) SGD and ii) Adam Optimizer. When using normal SGD, I get a smooth training loss vs. iteration curve as seen below (the red one). However, when I used the Adam …
Liquid State Machine: How is it different to Spiking Neural Network …
Oct 28, 2015 · I am very new to the 'reservoir computing world', and I've heard that the Liquid State Machines (LSM) are a certain kind of spiking neuron network models (SNN). Exactly what is the …
machine learning - Is it possible to run Spiking Neural Network (SNN ...
Aug 23, 2021 · Yes, you can simulate the behavior of a speaking role network on modern architecture. Many of the features associated with spiking networks, their speed, efficiency, and very low energy …
recurrent neural network - Why does the loss/accuracy fluctuate during ...
May 14, 2018 · I use LSTM network in Keras. During the training, the loss fluctuates a lot, and I do not understand why that would happen. Here is the NN I was using initially: And here are the …
How to Implement Biological Neuron Activation in Artificial Neural …
Feb 23, 2018 · Finally, ion pumps are represented by current sources (Ip). [clarification needed] The membrane potential is denoted by Vm. EDIT: In addition to implementing a biological activation …
What are the effects of depth and width in deep neural networks?
On the other hand Zagoruyko and Komodakis argues that wide residual networks “are far superior over their commonly used thin and very deep counterparts.” Can someone summarise the current …
Training loss increases with time - Cross Validated
Jan 25, 2018 · Closed 6 years ago. I am training a model (Recurrent Neural Network) to classify 4 types of sequences. As I run my training I see the training loss going down until the point where I correctly …
Training loss goes down and up again. What is happening?
Start asking to get answers Find the answer to your question by asking. Ask question machine-learning neural-networks loss-functions lstm
What *is* an Artificial Neural Network? - Cross Validated
Aug 16, 2018 · 22 As we delve into Neural Networks literature, we get to identify other methods with neuromorphic topologies ("Neural-Network"-like architectures). And I'm not talking about the …
Should I use a categorical cross-entropy or binary cross-entropy loss ...
First of all, I realized if I need to perform binary predictions, I have to create at least two classes through performing a one-hot-encoding. Is this correct? However, is binary cross-entropy only...