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A convolutional neural network (CNN) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer.
Why would "CNN-LSTM" be another name for RNN, when it doesn't even have RNN in it? Can you clarify this? What is your knowledge of RNNs and CNNs? Do you know what an LSTM is?
0 I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN.
A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems. CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text a...
0 I am working on LSTM and CNN to solve the time series prediction problem. I have seen some tutorial examples of time series prediction using CNN-LSTM. But I don't know if it is better than what I predicted using LST...
A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN). See this answer for more info. An example of an FCN is the u-net, which does not use any fu...
The concept of CNN itself is that you want to learn features from the spatial domain of the image which is XY dimension. So, you cannot change dimensions like you mentioned.
But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN. And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames ...
So, the convolutional layers reduce the input to get only the more relevant features from the image, and then the fully connected layer classify the image using those features, isn't it? I think I've just understood h...
Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel. There are input_channels * number_of_filters sets of weights, each of wh...
