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The required shape of the tensor $T$ depends on the shape of other tensors that are involved in the same operations of that same tensor $T$ and the required/desired shape of the resulting tensor, in the same way that the number of columns of the matrix $M \in \mathbb{R}^{n \times m}$ needs to match the number of rows of the matrix $M' \in \mathbb{R}^{n' \...


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Check the documentation for Dense layer: Note: If the input to the layer has a rank greater than 2, then Dense computes the dot product between the inputs and the kernel along the last axis of the inputs and axis 1 of the kernel (using tf.tensordot). For example, if input has dimensions (batch_size, d0, d1), then we create a kernel with shape (d1, units), ...


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Yes, you can do that, and it is a standard practice. One famous example is the "Inception" network architecture. To keep inner subnets from "dying out", several outputs from inner layers are extracted and passed into FC->Softmax. Then all the outputs are averaged in the loss function. From practical point of view, you won't be able to ...


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If the auto-encoder is converging to the same encoding for different instances, there may be a problem in the loss function. Check the size and shape of the output of the loss function, as it may be getting confused and evaluating the wrong tensors (i.e. you may need to transpose something somewhere). Basically, assuming you are using an auto-encoder to ...


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Ok, I solved this problem The simple thing was that learning rate was too big I changed the code to this LR = batch_size/((z+1)*100000) LR=LR/3 instead of LR = batch_size/((z+1)*1000) LR=LR/3 and it seems to work well


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There are a few issues you need to address first. Normalise your data. You should try and keep your values for each input in a good range, otherwise you're never going to train anything useful. A simple way of doing this could be to divide each value by the maximum value for that input. This will ensure they are between 0 and 1, or you could divide by the ...


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