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It seems they use a shared RNN which process each row sequentially on the sequence of concatenated channels of individual pixels. From the paper Implementation with channels last Let the output of the ConvNet be of size (batch_size, height, width, channels). The RNN expects an input of size (batch_size, sequence_length, input_size)`. So you have to reshape ...


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If you use a single layer CNN, then each vector in the resulting activation maps would be related to the original 3x3 block. However, if you stack multiple CNN layers, you increase the receptive field of each resulting vector, as shown in the image below (taken from here): After the CNNs, you can certainly compute an LSTM. There are, however, some design ...


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As you can see in lines 286-296 in newmodels.py the model can use two different loss functions for the four different outputs. loss = {'pred1':lossfxn, 'pred2':lossfxn, 'pred3':lossfxn, 'final': losses.tversky_loss} loss_weights = {'pred1':1, 'pred2':1, 'pred3':1, 'final':1} model....


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The backpropagation algorithm attributes a penalty per weight in the network. To get the associated gradient for each weight we need to backpropagate the error back to its layer using the derivative chain rule. Flattening layer The derivative of a layer depends on the function that is being applied. In the case of the flattening layer it is simply reshaping (...


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Is it reasonable to use a CNN instead of an LSTM, even though it is a time series? Yes, it is. Convolutional Neural Networks are applied to any kind of data in which neighboring information is supposedly relevant for the analysis of the data. CNN are very popular with images, where data is correlated in space, and in video, where correlation happens both in ...


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I do not think there is a special kind of format that needs to be followed as long as the image is clear and readable, which (imho) it is for your case. Regarding the last 2/3 layers, the final layer is the output with 1 unit, so you pictured it correctly, along as the article mentions the output shape (that is not a multi-output situation). Good luck with ...


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In absolute, it is one CNN wich takes 3 inputs images. You could see it as 3 separate features extractors (CNN) which merge their results while trained together. The author obtain 3 2D input from a 3D images by keeping 3 2D images; one in each plane. Each of these images has multiple channel because they slices the input among the respective axis. It is ...


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The problem is that your ResNet-18 follows the architecture for ImageNet as outlined in the ResNet paper: However, spatial input dimensions of ImageNet are different from CIFAR10 (32x32) so the architecture does not match your input. Instead you can follow the author's description of their CIFAR10 architecture in section 4.2 of the same paper: The plain/...


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I think you might be misunderstanding the phrase "extract" here. Think of it as "gets activated by" instead. For example, the "nose filter" gets activated by inputs which look like a human nose (more precisely: it gets activated by activation maps of previous layers which correspond to a nose in the input image). And simply put, ...


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In transfer learning there are two parameters which influence the basic setup to go for: Size of new dataset Similarity of new dataset to dataset of pre-trained model When your dataset is small the problem is that high capacity pre-trained models can easily overfit if you re-train too many layers. And since you re-trained multiple layers this could be an ...


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It is possible to build that kind of a CNN. It is important to maintain uniform distribution for both the classes ('cat' and 'not cat'). That is you should have an almost equal number of samples for each of these classes to avoid biasing your model to the 'non-cat' class just because it has huge number of examples. The number of non-cat examples can be ...


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Andrew Ng is making this point in comparison to a simple Neural network. Let's say you have a 10x10 image, In a dense neural network, - We will connect every 100 neurons to the 100 in the next layer.(Dense) - Over that, each all will have a distinct weight (No sharing) So, total parm = 10K In a Convolution Neural Network, the approach is as shown in this ...


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In fact these involve different aspects of parameters in a CNN. Parameter sharing, means one parameter may be shared by more than one input/connection. So this reduces total amount of independent parameters. Parameters shared are non-zero. Sparsity of connections means that some parameters are simply missing (ie are zero), nothing to do with sharing same ...


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