Convolutional Neural Networks in TensorFlow complete course is currently being offered by through Coursera platform and is Course 2 of 4 in the DeepLearning.AI TensorFlow Developer Professional Certificate.

About this Course

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In Course 2 of the TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. 

Also Check: How to Apply for Coursera Financial Aid

Convolutional Neural Networks in TensorFlow Quiz Answers - Coursera!

Coursera - Convolutional Neural Networks in TensorFlow Quiz Answers!

Convolutional Neural Networks in TensorFlow Week 1 Quiz Answers

Q1. What does flow_from_directory give you on the ImageGenerator?

  • 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

Q2. 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

Q3. 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

Q4. 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

Q5. 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

Q6. 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

Q7. 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

Q8. 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.

Convolutional Neural Networks in TensorFlow Week 2 Quiz Answers

Q1. 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

Q2. 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

Q3. 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

Q4. 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

Q5. 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

Q6. 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

Q7. When using Image Augmentation my training gets…

  • Slower
  • Faster
  • Stays the Same
  • Much Faster

Q8. Using Image Augmentation effectively simulates having a larger data set for

  • False
  • True 

Convolutional Neural Networks in TensorFlow Week 3 Quiz Answers

Q1. 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

Q2. 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

Q3. How did you lock or freeze a layer from retraining?

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

Q4. 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

Q5. 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

Q6. 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

Q7. 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

Q8. Which is the correct line of code for adding Dropout of 20% of neurons using

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

Convolutional Neural Networks in TensorFlow Week 4 Quiz Answers

Q1. The diagram for traditional programming had Rules and Data In, but what

came out?

  • Answers
  • Binary
  • Machine Learning
  • Bugs

Q2. 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

Q3. 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

Q4. 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!

Q5. What method on an ImageGenerator is used to normalize the image?

  • normalize
  • flatten
  • rezize()
  • rescale

Q6. 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

Q7. 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

Q8. When training for multiple classes what is the Class Mode for Image

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

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