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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. So, you cannot change dimensions like you mentioned. A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems
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What will a host on an ethernet network do if it receives a frame with a unicast destination mac address that does not match its own mac address The concept of cnn itself is that you want to learn features from the spatial domain of the image which is xy dimension It will discard the frame
It will forward the frame to the next host
It will remove the frame from the media 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 to rnn which is better The task i want to do is autonomous driving using sequences of images.
What is your knowledge of rnns and cnns Do you know what an lstm is? 0 i am working on lstm and cnn to solve the time series prediction problem But i don't know if it is better than what i predicted using lstm
Could using lstm and cnn together be better than predicting using lstm alone?
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 Pooling), upsampling (deconvolution), and copy and crop operations.
