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Convolutional Neural Networks in TensorFlow Coursera Exercise Quiz Answers

Question 1: What does flow_from_directory give you on the Image Generator?

  • The ability to easily load images for training
  • The ability to pick the size of training images
  • The ability to automatically label images based on their directory name
  • All of the above

Question 2: If my Image is sized 150×150, and I pass a 3×3 Convolution over it, what size is the resulting image?

  • 148×148
  • 150×150
  • 153×153
  • 450×450

Question 3: If my data is sized 150×150, and I use Pooling of size 2×2, what size will the resulting image be?

  • 300×300
  • 148×148
  • 149×149
  • 75×75

Question 4: If I want to view the history of my training, how can I access it?

  • Create a variable ‘history’ and assign it to the return of or model.fit_generator
  • Pass the parameter ‘history=true’ to the
  • Use a model.fit_generator
  • Download the model and inspect it

Question 5: What’s the name of the API that allows you to inspect the impact of convolutions on the images?

  • The model.pools API
  • The model.layers API
  • The model.images API
  • The model.convolutions API

Question 6: When exploring the graphs, the loss levelled out at about .75 after 2 epochs, but the accuracy climbed close to 1.0 after 15 epochs. What’s the significance of this?

  • There was no point training after 2 epochs, as we overfit to the validation data
  • There was no point training after 2 epochs, as we overfit to the training data
  • A bigger training set would give us better validation accuracy
  • A bigger validation set would give us better training accuracy

Question 7: Why is the validation accuracy a better indicator of model performance than training accuracy?

  • It isn’t, they’re equally valuable
  • There’s no relationship between them
  • The validation accuracy is based on images that the model hasn’t been trained with, and thus a better indicator of how the model will perform with new images.
  • The validation dataset is smaller, and thus less accurate at measuring accuracy, so its performance isn’t as important

Question 8: Why is overfitting more likely to occur on smaller datasets?

  • Because in a smaller dataset, your validation data is more likely to look like your training data
  • Because there isn’t enough data to activate all the convolutions or neurons
  • Because with less data, the training will take place more quickly, and some features may be missed
  • Because there’s less likelihood of all possible features being encountered in the training process.

Question 1: How do you use Image Augmentation in TensorFLow

  • Using parameters to the ImageDataGenerator
  • With the keras.augment API
  • You have to write a plugin to extend tf.layers
  • With the tf.augment API

Question 2: If my training data only has people facing left, but I want to classify people facing right, how would I avoid overfitting?

  • Use the ‘horizontal_flip’ parameter
  • Use the ‘flip’ parameter and set ‘horizontal’
  • Use the ‘flip’ parameter
  • Use the ‘flip_vertical’ parameter around the Y axis

Question 3: When training with augmentation, you noticed that the training is a little slower. Why?

  • Because the augmented data is bigger
  • Because the image processing takes cycles
  • Because there is more data to train on
  • Because the training is making more mistakes

Question 4: What does the fill_mode parameter do?

  • There is no fill_mode parameter
  • It creates random noise in the image
  • It attempts to recreate lost information after a transformation like a shear
  • It masks the background of an image

Question 5: When using Image Augmentation with the ImageDataGenerator, what happens to your raw image data on-disk.

  • It gets overwritten, so be sure to make a backup
  • A copy is made and the augmentation is done on the copy
  • Nothing, all augmentation is done in-memory
  • It gets deleted

Question 6: How does Image Augmentation help solve overfitting?

  • It slows down the training process
  • It manipulates the training set to generate more scenarios for features in the images
  • It manipulates the validation set to generate more scenarios for features in the images
  • It automatically fits features to images by finding them through image processing techniques

Question 7: When using Image Augmentation my training gets…

  • Slower
  • Faster
  • Stays the Same
  • Much Faster

Question 8: Using Image Augmentation effectively simulates having a larger data set for training.

  • False
  • True

Question 1: If I put a dropout parameter of 0.2, how many nodes will I lose?

  • 20% of them
  • 2% of them
  • 20% of the untrained ones
  • 2% of the untrained ones

Question 2: Why is transfer learning useful?

  • Because I can use all of the data from the original training set
  • Because I can use all of the data from the original validation set
  • Because I can use the features that were learned from large datasets that I may not have access to
  • Because I can use the validation metadata from large datasets that I may not have access to

Question 3: How did you lock or freeze a layer from retraining?

  • tf.freeze(layer)
  • tf.layer.frozen = true
  • tf.layer.locked = true
  • layer.trainable = false

Question 4: How do you change the number of classes the model can classify when using transfer learning? (i.e. the original model handled 1000 classes, but yours handles just 2)

  • Ignore all the classes above yours (i.e. Numbers 2 onwards if I’m just classing 2)
  • Use all classes but set their weights to 0
  • When you add your DNN at the bottom of the network, you specify your output layer with the number of classes you want
  • Use dropouts to eliminate the unwanted classes

Question 5: Can you use Image Augmentation with Transfer Learning Models?

  • No, because you are using pre-set features
  • Yes, because you are adding new layers at the bottom of the network, and you can use image augmentation when training these

Question 6: Why do dropouts help avoid overfitting?

  • Because neighbor neurons can have similar weights, and thus can skew the final training
  • Having less neurons speeds up training

Question 7: What would the symptom of a Dropout rate being set too high?

  • The network would lose specialization to the effect that it would be inefficient or ineffective at learning, driving accuracy down
  • Training time would increase due to the extra calculations being required for higher dropout

Question 8: Which is the correct line of code for adding Dropout of 20% of neurons using TensorFlow

  • tf.keras.layers.Dropout(20)
  • tf.keras.layers.DropoutNeurons(20),
  • tf.keras.layers.Dropout(0.2),
  • tf.keras.layers.DropoutNeurons(0.2),

Question 1: The diagram for traditional programming had Rules and Data In, but what came out?

  • Answers
  • Binary
  • Machine Learning
  • Bugs

Question 2: Why does the DNN for Fashion MNIST have 10 output neurons?

  • To make it train 10x faster
  • To make it classify 10x faster
  • Purely Arbitrary
  • The dataset has 10 classes

Question 3: What is a Convolution?

  • A technique to make images smaller
  • A technique to make images larger
  • A technique to extract features from an image
  • A technique to remove unwanted images

Question 4: Applying Convolutions on top of a DNN will have what impact on training?

  • It will be slower
  • It will be faster
  • There will be no impact
  • It depends on many factors. It might make your training faster or slower, and a poorly designed Convolutional layer may even be less efficient than a plain DNN!

Question 5: What method on an ImageGenerator is used to normalize the image?

  • normalize
  • flatten
  • rezize()
  • rescale

Question 6: When using Image Augmentation with the ImageDataGenerator, what happens to your raw image data on-disk.

  • A copy will be made, and the copies are augmented
  • A copy will be made, and the originals will be augmented
  • Nothing
  • The images will be edited on disk, so be sure to have a backup

Question 7: Can you use Image augmentation with Transfer Learning?

  • No – because the layers are frozen so they can’t be augmented
  • Yes. It’s pre-trained layers that are frozen. So you can augment your images as you train the bottom layers of the DNN with them

Question 8: When training for multiple classes what is the Class Mode for Image Augmentation?

  • class_mode=’multiple’
  • class_mode=’non_binary’
  • class_mode=’categorical’
  • class_mode=’all’

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