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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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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 ...


1

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' \...


1

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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