深度学习研究理解10：Very Deep Convolutional Networks for Large ... | very deep convolutional networks for large scale image recognitionvery deep convolutional networks for large scale image recognition
In assignment 3, Jeremy explains how convolutional and max-pooling layers work, and gives added insights into training a model.
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Blogs/Very Deep Convolutional Networks for Large-Scale Image ... | very deep convolutional networks for large scale image recognitionTo accomplish analysis faster, you can breach the archetypal into two: one with untrainable convolutional layers, addition with trainable close layers. Precompute appearance application the aboriginal model, alternation the added model. This access saves a lot of computation. Convolutional layers are area your ciphering is taken up. Close layers are area your anamnesis is taken up.
Jeremy additionally defines concepts of underfitting and overfitting and tells about means to accord with underfitting and overfitting.
If underfitting. Remove dropout afterwards finetuning by replacing layers with dropout with anticipation 0, again finetune more. Use lower acquirements amount (RMSProp(lr=0.00001, rho=0.7))
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Vision and Multimedia Reading Group: DeCAF: a Deep Convolutional Acti… | very deep convolutional networks for large scale image recognitionSteps for abbreviation overfitting:
1. Add added data
2. Use abstracts augmentation.
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Detection, Segmentation and Fine-grained Localization - ppt download | very deep convolutional networks for large scale image recognitionIt’s accessible to do abstracts accession on images application keras.image.ImageDataGenerator class. It can zoom, shift, circle and cast images about aural ambit that you set. When we use abstracts augmentation, we can’t precompute anything, so it takes longer.
3. Use architectures that generalize well
4. Add regularization (dropout, L1, L2).
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VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION ... | very deep convolutional networks for large scale image recognitionNowadays bodies use dropout in all layers. Usually abate dropouts in aboriginal layers, beyond dropouts in after layers. Dropout on aboriginal layers makes advice bare for all after layers.
5. Reduce architectonics complexity
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Very Deep Convolutional Networks for Large-Scale Image Recognition ... | very deep convolutional networks for large scale image recognition["1241.6"]
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