Intro-To-GAN Download

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Instructions

This is quiz answer sheet only. In order to answer it you might need to do some code exercises provided in quiz guideline.

  1. GAN is a generative model. Please select all box that represents a generative models
    • [ ] Naive Bayes
    • [ ] Logistic Regression
    • [ ] Support Vector Machine
    • [ ] K-Nearest Neighbor
  2. What does adversarial refers to in GAN ?
    • [ ] Loss function
    • [ ] Optimizer
    • [ ] Input
    • [ ] Output
  3. Select all correct boxes about Generator and Discriminator
    • [ ] Generator takes input from Discriminator
    • [ ] Discriminator tries to maximize the loss function
    • [ ] Discriminator tries to generate a random noise
    • [ ] GAN input is random vector
  4. To ensure you have correctly load the data, what is the shape of X_train and X_test respectively?
    • [ ] (60000, 28, 28, 1) and (10000, 28, 28, 1)
    • [ ] (50000, 28, 28, 1) and (5000, 28, 28, 1)
    • [ ] (60000, 32, 32, 1) and (10000, 32, 32, 1)
    • [ ] (50000, 32, 32, 1) and (5000, 32, 32, 1)
  5. While loading the Mnist dataset, in line 4, what does X_train = (X_train.astype(np.float32) - 127.5)/127.5 means ?
    • [ ] Rescale the data into within the interval of -1 to 1
    • [ ] Rescale the data into within the interval of 0 to 1
    • [ ] Denormalize data
    • [ ] Reshape data
  6. If you follow the quiz guide, how many params does the generator_model has ?
    • [ ] 6,765,313
    • [ ] 6,763,777
    • [ ] 6,751,233
    • [ ] 12,544
  7. If you change the last convolution layer's filter shape into 7x7, what will be the final total parameters ?
    • [ ] 6,765,313
    • [ ] 6,763,777
    • [ ] 6,751,233
    • [ ] 12,544
  8. What does Flatten() layer means in the Discriminator?
    • [ ] To make the data standardized
    • [ ] To make the data mean = 0
    • [ ] To reshape the data into one dimensional vector
    • [ ] To compile the previous layers
  9. What is the differences between images that are not checked by discriminator (directly generated from generator), and images that previously checked by discriminator?
    • [ ] The unchecked images are smaller in size
    • [ ] The checked images are usually better
    • [ ] The unchecked images are usually better
    • [ ] The checked images are smaller in size
  10. What do you think about losses over time while training ?
    • [ ] It will be minimized overtime
    • [ ] It will be maximized overtime
    • [ ] It will just oscilate overtime
Quiz
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You have 2 attempts. Only your highest score will be taken into account.
  • Quiz 1

  • GAN is a generative model. Please select all box that represents a generative models
  • Question worth 1 point

  • Quiz 2

  • What does adversarial refers to in GAN ?
  • Question worth 1 point

  • Quiz 3

  • Select all correct boxes about Generator and Discriminator
  • Question worth 1 point

  • Quiz 4

  • To ensure you have correctly load the data, what is the shape of X_train and X_test respectively?
  • Question worth 1 point

  • Quiz 5

  • While loading the Mnist dataset, in line 4, what does X_train = (X_train.astype(np.float32) - 127.5)/127.5 means ?
  • Question worth 1 point

  • Quiz 6

  • If you follow the quiz guide, how many params does the generator_model has ?
  • Question worth 1 point

  • Quiz 7

  • If you change the last convolution layer's filter shape into 7x7, what will be the final total parameters ?
  • Question worth 1 point

  • Quiz 8

  • What does Flatten() layer means in the Discriminator?
  • Question worth 1 point

  • Quiz 9

  • What is the differences between images that are not checked by discriminator (directly generated from generator), and images that previously checked by discriminator?
  • Question worth 1 point

  • Quiz 10

  • What do you think about losses over time while training ?
  • Question worth 1 point

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