在训练机器学习模型过程中,需要使用损失函数(loss function)来更新模型参数。不同的损失函数惩罚的内容不同,会在训练过程中将模型引到不同的方向。而在时间序列预测中,按照不同的惩罚目标选择或设计损失函数,也会影响模型最终的表现能力。欧几里得损失函数(Euclidean loss,亦即 MSE)是常用的损失函数,这里不再赘述。本文...
The execution of this loss function is tested on tf~v2. User can install tensorflow-gpu for executing the model on gpu. Setup Compiling the cython file. cython sdtw/soft_dtw_fast.pyx Building the package python setup.py build python setup.py build_ext --inplace Example import tensorflow...
A differentiable learning loss; Introduction: supervised learning: learn a mapping that links an input to an output object. output object is a time series. Prediction: two multi-layer perceptrons, the first use Euclidean loss and the second use soft-DTW as a loss function. ---> soft-DTW, ...
内容提示: Soft-DTW: a Differentiable Loss Function for Time-SeriesMarco Cuturi 1 Mathieu Blondel 2AbstractWe propose in this paper a differentiable learningloss between time series, building upon the cel-ebrated dynamic time warping (DTW) discrep-ancy. Unlike the Euclidean distance, DTW can...
Python mblondel/soft-dtw Star551 Code Issues Pull requests Python implementation of soft-DTW. time-seriesdtwneural-networksdynamic-time-warpingsoft-dtw UpdatedJun 19, 2024 Python PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA ...
We show in this paper that soft-DTW is a differentiable loss function, and that both its value and its gradient can be computed with quadratic time/space complexity (DTW has quadratic time and linear space complexity). We show that our regularization is particularly well suited to average and...
To address the problems of low signal-to-noise ratio and difficult feature extraction of N400 data, we propose a Soft-DTW-based single-subject short-distance event-related potential averaging method by using the advantages of differentiable and efficient Soft-DTW loss funct...
This enables to use soft-DTW for time series averaging or as a loss function, between a ground-truth time series and a time series predicted by a neural network, trained end-to-end using backpropagation. Supported features soft-DTW (forward pass) and gradient (backward pass) computations, ...
(DTW) similarity measure as loss function of a feed-forward neural network. The effectiveness of this approach was assessed both with simulation and experimental studies. We first used a model of the sEMG generation process to test the feasibility of the method. Then, we evaluated the proposed ...
This enables to use soft-DTW for time series averaging or as a loss function, between a ground-truth time series and a time series predicted by a neural network, trained end-to-end using backpropagation. Supported features soft-DTW (forward pass) and gradient (backward pass) computations, ...