Coursera《Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning》(Quiz of Week4)
Using Real-world Images
本博客为Coursera上的课程《Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning》第四周的测验。
Using Image Generator, how do you label images?
A. It’s based on the directory the image is contained in
B. You have to manually do it
C. It’s based on the file name
D. TensorFlow figures it out from the contents
What method on the Image Generator is used to normalize the image?
How did we specify the training size for the images?
A. The training_size parameter on the validation generator
B. target_size parameter on the training generator
C. The target_size parameter on the validation generator
D. The training_size parameter on the training generator
When we specify the input_shape to be (300, 300, 3), what does that mean?
A. Every Image will be 300x300 pixels, and there should be 3 Convolutional Layers
B. Every Image will be 300x300 pixels, with 3 bytes to define color
C. There will be 300 horses and 300 humans, loaded in batches of 3
D. There will be 300 images, each size 300, loaded in batches of 3
If your training data is close to 1.000 accuracy, but your validation data isn’t, what’s the risk here?
A. No risk, that’s a great result
B. You’re overfitting on your validation data
C. You’re overfitting on your training data
D. You’re underfitting on your validation data
Convolutional Neural Networks are better for classifying images like horses and humans because:
A. In these images, the features may be in different parts of the frame
B. There’s a wide variety of horses
C. There’s a wide variety of humans
D. All of the above
After reducing the size of the images, the training results were different. Why?
A. We removed some convolutions to handle the smaller images
B. There was more condensed information in the images
C. The training was faster
D. There was less information in the images