# Tag Info

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The Flatten layer is used for collapsing an ND tensor into a 1D tensor. In your case, the inputs appear to be $28\times28$ images, so Flatten will convert that into a tensor with shape $1\times768$. Note that no information is lost. Flatten layers are usually used where you have a convolutional layer with dimensions $N\times M \times C$ (where $N$,$M$ are ...

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Estimating from an observation is a function, but "really counting" is a process. Feed-forward neural networks can learn arbitrary functions from training examples, but they cannot represent (and therefore cannot learn) processes. They can attempt to estimate the results of completing a process as a function, but that is not the same thing as ...

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A unified neural network model consists of one neural network as opposed to other models that rely on two or more neural networks. For example, from page two of the YOLO paper: 2. Unified Detection We unify the separate components of object detection into a single neural network. Our network uses features from the entire image to predict each bounding box. ...

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I'm not aware of a direct way for finding the best NN architecture for a given task, but the recommended way, as far as I know, is to devise a network that can overfit the training data, and then apply regularization on top of it. That way, you can be almost sure you're not underfitting/underperforming due to network capacity.

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As said in the comments, I wouldn't use Machine Learning for that. You can achieve that result using something like OpenCV. For example: Get the "Naked" Background image: If you don't have it, you can easily calculate it by making an average of each image: background = np.mean(images, axis=0) For each image, calculate the pixel difference between ...

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Well, in regards to properties of CNNs in regards to local versus global features, you should familiarize yourself with the concepts of invariance and equivariance. At some point you should also learn about the Picasso problem which is a consequence of the invariances and equivariances of CNNs + pooling. That will probably also mean you'll encounter Capsule ...

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An Image is Worth 16X16 Words: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE A Transformer consists of alternating layers of multiheaded self-attention. The Transformer Paper adapts a NLP architecture for making Image Classification. For that, it first need to tokenize the image (like a piece of text). The tokenization is done by splitting the image into fixed-...

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I think the answer to your question is much more a rule of thumb than an appropriate analytical answer. First of all, I would like to remark that Batch Normalization [1] are applied most commonly to convolutional layer, constituting what is called a "convolutional block" (Convolution + Batch Normalization + Activation). Thus, for giving you an idea ...

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It sounds like you have structured/tabular data. So, a fully-connected feedforward network should do the job.

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Model/network design has multiple guidelines, a basic one is: The solving capacity of the network should be larger than the possibility space of the problem to be solved. Solving capacity (learning capacity) of a network (dense usually) can be calculated as the product of number of neurons in all layers, for example: Input shape: 10 values Network shape: [...

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The most obvious way more classes increase the network size it the output layer, but I don't believe there is a rule of thumb for the size of the entire network. As I understand it, there is no clear answer how big a network needs to be to achieve a certain performance with regard to the number of layers compared to the number of classes. This is a very ...

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