Quiz.md

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

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

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

   • rescale
   • Rescale_image
   • normalize
   • normalize_image

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

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

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

   • Every Image will be 300x300 pixels, with 3 bytes to define color
   • There will be 300 images, each size 300, loaded in batches of 3
   • Every Image will be 300x300 pixels, and there should be 3 Convolutional Layers
   • There will be 300 horses and 300 humans, loaded in batches of 3

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

   • No risk, that’s a great result
   • You’re underfitting on your validation data
   • You’re overfitting on your validation data
   • 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 less information in the images
   • There was more condensed information in the images