From my experience – the problem lies in loading Keras to one process and then spawning a new process when the keras has been loaded to your main environment. But for some applications (like e.g. training a mixture of Kerasmodels) it’s simply better to have all of this things in one process. So what I … Read more
It’s explained well here. If the input variables are combined linearly, as in an MLP [multilayer perceptron], then it is rarely strictly necessary to standardize the inputs, at least in theory. The reason is that any rescaling of an input vector can be effectively undone by changing the corresponding weights and biases, leaving you with … Read more
Prune‘s and Bharat‘s answers gives the overall purpose of a “Python” layer: a general purpose layer which is implemented in python rather than c++. I intend this answer to serve as a tutorial for using “Python” layer. A Tutorial for “Python” layer what is a “Python” layer? Please see the excellent answers of Prune and … Read more
I came across this phenomenon several times. Here are my observations: Gradient blow up Reason: large gradients throw the learning process off-track. What you should expect: Looking at the runtime log, you should look at the loss values per-iteration. You’ll notice that the loss starts to grow significantly from iteration to iteration, eventually the loss … Read more
In this post, I will advise you of: How to map navigational instructions to action sequences with an LSTM neural network Resources that will help you learn how to use neural networks to accomplish your task How to install and configure neural network libraries based on what I needed to learn the hard way General … Read more
Preliminary facts In functional sense, the sigmoid is a partial case of the softmax function, when the number of classes equals 2. Both of them do the same operation: transform the logits (see below) to probabilities. In simple binary classification, there’s no big difference between the two, however in case of multinomial classification, sigmoid allows … Read more
When using the image dataset input layer (with either lmdb or leveldb backend) caffe only supports one integer label per input image. If you want to do regression, and use floating point labels, you should try and use the HDF5 data layer. See for example this question. In python you can use h5py package to … Read more
Units: The amount of “neurons”, or “cells”, or whatever the layer has inside it. It’s a property of each layer, and yes, it’s related to the output shape (as we will see later). In your picture, except for the input layer, which is conceptually different from other layers, you have: Hidden layer 1: 4 units … Read more
Yes There is! Credit: It was hard to find the information and get it working but here is an example copying from the principles and code found here and here. Requirements: Before we start, there are two requirement for this to be able to succeed. First you need to be able to write your activation … Read more
A little understanding of the actual meanings (and mechanics) of both loss and accuracy will be of much help here (refer also to this answer of mine, although I will reuse some parts)… For the sake of simplicity, I will limit the discussion to the case of binary classification, but the idea is generally applicable; … Read more