Convolutional Neural Networks in TensorFlow complete course is currently being offered by deeplearning.ai 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 deeplearning.ai 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.
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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 model.fit or
model.fit_generator
- Pass
the parameter ‘history=true’ to the model.fit
- 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
training.
- 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
TensorFlow
- 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
Augmentation?
- class_mode=’multiple’
- class_mode=’non_binary’
- class_mode=’categorical’
- class_mode=’all’
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