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Tune Deep Neural Networks

Programmatically and interactively tune training options, resume training from a checkpoint, and investigate adversarial examples

To learn how to set options using the trainingOptions function, see Set Up Parameters and Train Convolutional Neural Network. After you identify some good starting options, you can automate sweeping of hyperparameters or try Bayesian optimization using Experiment Manager. Use Experiment Manager to test different training configurations at the same time by running your experiment in parallel and monitor your progress by using training plots.

Catégories

Experiment Manager App
Train networks under multiple initial conditions, interactively tune training options, and assess your results
Tuning
Programmatically tune training options, resume training from a checkpoint, and investigate adversarial examples

Exemples présentés

Resume Training from Checkpoint Network

Resume Training from Checkpoint Network

Save checkpoint networks while training a deep learning network and resume training from a previously saved network.

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Try Multiple Pretrained Networks for Transfer Learning

Try Multiple Pretrained Networks for Transfer Learning

Configure an experiment that replaces layers of different pretrained networks for transfer learning.

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Experiment with Weight Initializers for Transfer Learning

Experiment with Weight Initializers for Transfer Learning

Configure an experiment that initializes the weights of convolution and fully connected layers using different weight initializers.

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Use Experiment Manager to Train Generative Adversarial Networks (GANs)

Use Experiment Manager to Train Generative Adversarial Networks (GANs)

Create a custom training experiment to generate images of flowers.

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Detect Issues During Deep Neural Network Training

Detect Issues During Deep Neural Network Training

Automatically detect issues while training a deep neural network.

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