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?
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