How does Keras 1d convolution layer work with word embeddings – text classification problem? (Filters, kernel size, and all hyperparameter)

I would try to explain how 1D-Convolution is applied on a sequence data. I just use the example of a sentence consisting of words but obviously it is not specific to text data and it is the same with other sequence data and timeseries. Suppose we have a sentence consisting of m words where each … Read more

How to calculate the number of parameters for convolutional neural network?

Let’s first look at how the number of learnable parameters is calculated for each individual type of layer you have, and then calculate the number of parameters in your example. Input layer: All the input layer does is read the input image, so there are no parameters you could learn here. Convolutional layers: Consider a … Read more

Intuitive understanding of 1D, 2D, and 3D convolutions in convolutional neural networks [closed]

I want to explain with picture from C3D. In a nutshell, convolutional direction & output shape is important! ↑↑↑↑↑ 1D Convolutions – Basic ↑↑↑↑↑ just 1-direction (time-axis) to calculate conv input = [W], filter = [k], output = [W] ex) input = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1] output-shape is 1D array example) graph … Read more

Why binary_crossentropy and categorical_crossentropy give different performances for the same problem?

The reason for this apparent performance discrepancy between categorical & binary cross entropy is what user xtof54 has already reported in his answer below, i.e.: the accuracy computed with the Keras method evaluate is just plain wrong when using binary_crossentropy with more than 2 labels I would like to elaborate more on this, demonstrate the … Read more