Coursera | Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Week 4 Quiz >> Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Week 4 Quiz >> Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning


1. Using Image Generator, how do you label images?

  • You have to manually do it
  • It’s based on the file name
  • It’s based on the directory the image is contained in
  • TensorFlow figures it out from the contents

2. What method on the Image Generator is used to normalize the image?

  • normalize
  • Rescale_image
  • normalize_image
  • rescale

3. How did we specify the training size for the images?

  • The target_size parameter on the validation generator
  • The training_size parameter on the training generator
  • The training_size parameter on the validation generator
  • The target_size parameter on the training generator

4. When we specify the input_shape to be (300, 300, 3), what does that mean?

  • There will be 300 horses and 300 humans, loaded in batches of 3
  • Every Image will be 300×300 pixels, and there should be 3 Convolutional Layers
  • There will be 300 images, each size 300, loaded in batches of 3
  • Every Image will be 300×300 pixels, with 3 bytes to define color

5. If your training data is close to 1.000 accuracy, but your validation data isn’t, what’s the risk here?

  • You’re overfitting on your validation data
  • You’re underfitting on your validation data
  • No risk, that’s a great result
  • You’re overfitting on your training data

6. Convolutional Neural Networks are better for classifying images like horses and humans because:

  • In these images, the features may be in different parts of the frame
  • There’s a wide variety of horses
  • There’s a wide variety of humans
  • All of the above

7. After reducing the size of the images, the training results were different. Why?

  • The training was faster
  • We removed some convolutions to handle the smaller images
  • There was more condensed information in the images
  • There was less information in the images

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