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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. This is achieved by using 1x1 convolutions with fewer output channels than input channels. A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems
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But if you have separate cnn to extract features, you can extract features for last 5 frames and then pass these features to rnn In a cnn (such as google's inception network), bottleneck layers are added to reduce the number of feature maps (aka channels) in the network, which, otherwise, tend to increase in each layer 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? I am training a convolutional neural network for object detection Apart from the learning rate, what are the other hyperparameters that i should tune
And in what order of importance 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.
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?
