![]() Configure the learning process by picking a loss function, an optimizer, and some metrics to monitor ![]() Define a network of layers (a "model") that will map your inputs to your targets Slightly reduces the loss on this batch The typical Keras workflow: - Define your training data: input tensors and target tensors update all weights of the network in a way that compute the "loss" of the network on the batch,Ī measure of the mismatch between y_pred and y run the network on x (this is called "forward pass"), obtain predictions y_pred it's data type How do we sometimes call axis 0? sample or batch axis What four steps does training loop consist of? - draw a batch of training samples x and corresponding targets y By which three attributes tensor is defined? - the number of axes it has ![]()
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