Topic 1 Question 39
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively. How should the Specialist address this issue and what is the reason behind it?
The learning rate should be increased because the optimization process was trapped at a local minimum.
The dropout rate at the flatten layer should be increased because the model is not generalized enough.
The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
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コメント(17)
DROPOUT HELPS PREVENT OVERFITTING https://keras.io/layers/core/#dropout
THE BEAUTIFUL ANSER SHOULD BE B.
👍 51DonaldCMLIN2021/09/20Correct answer is B! Number of epochs shoud be decresed.
👍 4eganilovic2021/11/02- 正解だと思う選択肢: B
I vote B for all the reasons already mentioned. Dropouts help prevent overfitting
👍 3ovokpus2022/06/26
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