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