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사용자 지정 훈련 루프
딥러닝 훈련 루프 및 손실 함수 사용자 지정
trainingOptions
함수가 작업에 필요한 훈련 옵션을 제공하지 않거나 사용자 지정한 출력 계층이 필요한 손실 함수를 지원하지 않을 경우에는 사용자 지정 훈련 루프를 정의할 수 있습니다. 계층 그래프가 지원하지 않는 모델의 경우 사용자 지정 모델을 함수로 정의할 수 있습니다. 자세한 내용은 사용자 지정 훈련 루프, 손실 함수 및 신경망 정의 항목을 참조하십시오.
함수
도움말 항목
사용자 지정 훈련 루프
- Train Deep Learning Model in MATLAB
Learn how to training deep learning models in MATLAB®. - 사용자 지정 훈련 루프, 손실 함수 및 신경망 정의
자동 미분을 사용하여 사용자 지정 딥러닝 훈련 루프, 손실 함수 및 신경망을 정의하는 방법을 알아봅니다. - Train Network Using Custom Training Loop
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule. - Train Sequence Classification Network Using Custom Training Loop
This example shows how to train a network that classifies sequences with a custom learning rate schedule. - Specify Training Options in Custom Training Loop
Learn how to specify common training options in a custom training loop. - Define Model Loss Function for Custom Training Loop
Learn how to define a model loss function for a custom training loop. - Update Batch Normalization Statistics in Custom Training Loop
This example shows how to update the network state in a custom training loop. - Make Predictions Using dlnetwork Object
This example shows how to make predictions using adlnetwork
object by splitting data into mini-batches. - Monitor Custom Training Loop Progress
Track and plot custom training loop progress. - 다중 입력 및 다중 출력 신경망
여러 개의 입력값이나 여러 개의 출력값을 갖는 딥러닝 신경망을 정의하고 훈련시키는 방법을 알아봅니다. - Train Network with Multiple Outputs
This example shows how to train a deep learning network with multiple outputs that predict both labels and angles of rotations of handwritten digits. - Classify Videos Using Deep Learning with Custom Training Loop
This example shows how to create a network for video classification by combining a pretrained image classification model and a sequence classification network. - Train Image Classification Network Robust to Adversarial Examples
This example shows how to train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training. - Train Neural ODE Network
This example shows how to train an augmented neural ordinary differential equation (ODE) network. - Train Robust Deep Learning Network with Jacobian Regularization
Train a neural network that is robust to adversarial examples using a Jacobian regularization scheme. - Solve Ordinary Differential Equation Using Neural Network
This example shows how to solve an ordinary differential equation (ODE) using a neural network. - Assemble Multiple-Output Network for Prediction
This example shows how to assemble a multiple output network for prediction. - Train Network in Parallel with Custom Training Loop
This example shows how to set up a custom training loop to train a network in parallel. - Run Custom Training Loops on a GPU and in Parallel
Speed up custom training loops by running on a GPU, in parallel using multiple GPUs, or on a cluster.
자동 미분
- List of Functions with dlarray Support
View the list of functions that supportdlarray
objects. - Automatic Differentiation Background
Learn how automatic differentiation works. - Use Automatic Differentiation In Deep Learning Toolbox
How to use automatic differentiation in deep learning.
딥러닝 함수 가속
- Deep Learning Function Acceleration for Custom Training Loops
Accelerate model functions and model loss functions for custom training loops by caching and reusing traces. - Accelerate Custom Training Loop Functions
This example shows how to accelerate deep learning custom training loop and prediction functions. - Check Accelerated Deep Learning Function Outputs
This example shows how to check that the outputs of accelerated functions match the outputs of the underlying function. - Evaluate Performance of Accelerated Deep Learning Function
This example shows how to evaluate the performance gains of using an accelerated function.