How does backpropagation work with ONNX in the computational graph?

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Backpropagation in ONNX works by calculating gradients of a given output with respect to its inputs. The computational graph created by ONNX consists of nodes that represent mathematical operations and edges that represent input and output connections between nodes. When a neural network is trained, backpropagation is used to adjust the weights and biases in the network to minimize the loss function.

First, the forward pass is performed on the input data by applying the operations represented by the nodes in the computational graph. The output is then compared to the target output and the loss is calculated. Backpropagation then computes the gradient of the loss with respect to the parameters of the neural network, using the chain rule of differentiation to propagate the error gradients backwards through the computational graph.

Each node in the graph has a backward pass function that calculates the gradients of the inputs and parameters given the gradients of the outputs. These gradients are then used to update the weights and biases of the network using an optimization algorithm such as stochastic gradient descent.

Overall, backpropagation in ONNX allows for efficient gradient computation and optimization of neural networks, making it a powerful tool for deep learning applications.