Can you use in your workflow some quantity computed on your test data? No What's reinforcement learning? In reinforcement learning, an "agent" receives information about its environment and learns to pick actions that will maximize some reward. encoding the labels as integers and using the sparse_categorical_crossentropy loss function.
Iterate on your training data Why you shouldn't use bottleneck layers (number of neurons in a hidden layer is less than the number of classes?) You can loose some information during training What two options of features representation do you have when dealing with multilabel classification? - encoding the labels via "categorical encoding" (also known as "one-hot encoding") and usingĬategorical_crossentropy as your loss function. 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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