Convolutional Neural Network 1

 Q1. CNN features

Why is convolution neural network taking off quickly in recent times?

Choose the correct answer from below:

A.     Access to large amount of digitized data

B.     Integration of feature extraction within the training process

C.      Availability of more computational power

D.     All the above

Ans:

• All the above is the correct answer.
• Using CNN, we can Access and train our model on a large amount of digitized data
• Unlike classical image D recognition where you define the image features yourself, CNN takes the image’s raw pixel data, trains the model, then extracts the features automatically for better classification.
• Using CNN, the number of training parameters is reduced significantly. And due to the availability of more computational power in recent times. The model takes less time to train.

Q2. Recognizing a cat

For an image recognition problem (recognizing a cat in a photo), which of the following architecture of neural network would be best suited to solve the problem?

Choose the correct answer from below:

A.     Multi Layer Perceptron

B.     Convolutional Neural Network

C.      Perceptron

D.     Support Vector Machine

Ans: B

The correct answer is Convolutional Neural Network.

The Convolutional Neural Network (CNN or ConvNet) is a subtype of the Neural Networks that is mainly used for applications in image and speech recognition. Its built-in convolutional layer reduces the high dimensionality of images without losing its information. That is why CNNs are especially suited for this use case.

Q3. CNN Layers

Which of the following statements is False?

Choose the correct answer from below:

A.     CNN's are prone to overfitting because of less number of parameters

B.     There are no learnable parameters in Pooling layers

C.      In a max-pooling layer, the unit that contributes(maximum entry) in the forward propagation gets all the gradient in the backpropagation

D.     None of the above

Ans: A

Correct option: CNNs are prone to overfitting because of less number of parameters

Explanation :

• The statement "CNNs are prone to overfitting because of less number of parameters" is false. CNN's are prone to overfitting when they have a lot of parameters. A neural network with a lot of parameters tries to learn too much or too many details in the training data along with the noise from the training data, which results in poor performance on unseen or test datasets, which is termed overfitting.

• There are no trainable parameters in a max-pooling layer. In the forward pass, it passes the maximum value within each filter to the next layer. In the backward pass, it propagates error in the next layer to the place from where the max value is taken, because that's where the error comes from. You can use this link to learn more about max pooling layer.

• In a max-pooling layer, the unit that gets contributed(maximum entry) in the forward propagation gets all the gradients in the backpropagation.( This is True )

Q4. Max-Pooling necessary

Why do we use Max-pooling in Convolutional Neural Networks ?

Choose the correct answer from below, please note that this question may have multiple correct answers

A.     Reduce Resolution

B.     Extract the High intensity features

C.      Extract the low intensity features

D.     Increase Resolution

Ans: A, C

The correct answers are:

• Reduce Resolution
• Extract the High intensity features

Reason:

• Max-pooling helps in extracting high intensity features.
• While Avg-pooling goes for smooth features.
• If time constraint is not a problem, then one can skip the pooling layer and use a convolutional layer to do the same.
• It also helps in reducing the resolution of the input.

Q5. Pixel

A Pixel means a Picture Element. It is the smallest Element of an image on a computer display. Given two different images (pixel grids, where cells have the value of pixels) of size 5×5, find out the type of image1 and image2 respectively.

Choose the correct answer from below:

A.     image1= Black and White, image2= color

B.     image1= color, image2= Black and White

C.      image1= Grayscale, image2= color

D.     image1= Black and White, image2= Grayscale

Ans: D

• Correct answer is image1= Black and White, image2= Grayscale
• For a binary image (Black and White), a pixel can only take a value of 0 or 255
• In a GrayScale image, it can choose values between 0 and 255.

Q6. Translation in-variance

Determine whether the given statement is true or false.

When a pooling layer is added to a convolutional neural network, translation invariance is preserved.

Note: Translation in-variance means that the system produces the same response, regardless of how its input is shifted.

Choose the correct answer from below:

A.     True

B.     False

 

Ans: A

The correct answer is True

Reason:

• Invariance means that we can recognize an object as an object, even when its appearance varies in some way. This is generally a good thing, because it preserves the object's identity, category, (etc.) across changes in the specifics of the visual input, like relative positions of the viewer/camera and the object.
• Pooling helps make the representation approximately invariant to small translations of the input.
  • If we translate the input by a small amount, the values of most of the outputs do not change.
  • Pooling can be viewed as adding a strong prior that the function the layer learns must be invariant to small translations.

Q7. True About Type of Padding

Which of the following are True about Padding in CNN?

Choose the correct answer from below, please note that this question may have multiple correct answers

A.     We should use valid padding if we know that information at edges is not that much useful.

B.     There is no reduction in dimension when we use zero padding.

C.      In valid padding, we drop the part of the image where the filter does not fit.

Ans: A,B,C

The correct answers are:

1. We should use valid padding if we know that information at edges is not that much useful.
2. There is no reduction in dimension when we use zero padding.
3. In valid padding, we drop the part of the image where the filter does not fit.

Reason:

• The output size of the convolutional layer shrinks depending on the input size & kernel size.
• In zero padding, we pad zeros around the image's border to save most of the information, whereas, in valid padding, we lose out on the information that doesn't fit in filters.
• There is no reduction in dimension when we use zero padding.
• To sum up, Valid padding means no padding. The output size of the convolutional layer shrinks depending on the input size & kernel size. On the contrary, 'zero' padding means using padding.

Q8. CNN with benefits

What are the benefits of using Convolutional Neural Network(CNN) instead of Artificial Neural Network(ANN)?

Choose the correct answer from below, please note that this question may have multiple correct answers

A.     Reduce the number of units in the network, which means fewer parameters to learn and decreased computational power is required

B.     Increase the number of units in the network, which means more parameters to learn and increase chance of overfitting.

C.      They consider the context information in the small neighborhoods.

D.     CNN uses weight sharing technique

Ans: A, C,D

Correct options:

• Reduce the number of units in the network, which means fewer parameters to learn and decreased computational power is required
• They consider the context information in the small neighborhoods
• CNN uses weight sharing technique.

Explanation :

• CNNs usually have a lesser no of parameters compared to ANNs, which means

• CNNs consider the context information and pixel dependencies in the small neighborhood and due to this feature, they achieve a better prediction in data like images

• Weight sharing decreases the number of parameters and also makes feature search insensitive to feature location in the image. This results in a more generalized model and thus also works as a regularization technique .

Q9. Appyling Max pooling

If we pass a 2×2 max-pooling filter over the given input with a stride of 2, find the value of W, X, Y, Z?

Choose the correct answer from below:

A.     W = 8, X = 6, Y= 9, Z=6​

B.     W = 9, X = 8, Y= 8, Z=6​

C.      W = 6, X = 9, Y= 8, Z=8​

D.     W = 9, X = 8, Y= 8, Z=9​

 

Ans: B

The correct answer is W = 9, X = 8, Y= 8, Z=6

• Our first 2 × 2 region is highlighted in yellow, and we can see the max value of this region is 6.
• Next 2 × 2 region is highlighted in blue, and we can see the max value of this region is 9.
• Similarly, we will do this for all the 2×2 sub-matrices highlighted in different colors.

Q10. Difference in output size

What is the difference between the output size of the given two models with input image of size 100×100. Given, number of filter, filter size, strides respectively in the figure ? (Take padding = 0)

Note: The Answer is the difference of final convolution of Model1 and Model2.

Example: Say the final convolution of Model1 is 10 x 10 x 30 = 3000 and Model2 is 20 x 20 x 14 = 5600
Answer = 5600 - 3000 = 2600

Choose the correct answer from below:

A.     1392

B.     1024

C.      6876

D.     500

Ans: B

The correct answer is 1024

The result size of a convolution after 1 layer will be (W – F + 2P) /S + 1.

For model 1,

Step1 - Input = 100 x 100, filter = 15, filter size = 3 x 3, strides = 1

Answer = (100 - 3 + (2x0))/1 + 1 = 98

Step1_output =  98 x 98 x 15

Step2 - Input = 98 x 98, filter = 42, filter size = 6 x 6, strides = 4

Answer = (98 - 6 + (2x0))/4 + 1 = 24

Step2_output =  24 x 24 x 42

Step3 - Input = 24 x 24, filter = 30, filter size = 3 x 3, strides = 3

Answer = (24 - 3 + (2x0))/3 + 1 = 8

Step3_output =  8 x 8 x 30

final_model1_ output = 1920

——————————————————————————

For model 2,

Step1 - Input = 100 x 100, filter = 5, filter size = 6 x 6, strides = 1

Answer = (100 - 6 + (2x0))/1 + 1 = 95

Step1_output =  95 x 95 x 5

Step2 - Input = 95 x 95, filter = 11, filter size = 3 x 3, strides = 4

Answer = (95 - 3 + (2x0))/4 + 1 = 24

Step2_output =  24 x 24 x 11

Step3 - Input = 24 x 24, filter = 14, filter size = 3 x 3, strides = 3

Answer = (24 - 3 + (2x0))/3 + 1 = 8

Step3_output =  8 x 8 x 14

final_model2_ output = 896

Therefore, difference in output size will be 1920 – 896 = 1024.

Q11. Horizontal Edges

Perform a default Horizontal edge detection on the given image and choose the correct option?

Note : Here Stride = 1, Padding = Valid

Choose the correct answer from below:

A.     A

B.     B

C.      C

D.     D

Ans: A

Therefore, correct option is A

Q12. Dimensionality Reduction

Jay is working on an image resizing algorithm. He wants to reduce the dimensions of an image, he takes inspiration from the course he took on Scaler related to Data Science where he was taught about CNN's. Which of these options might be useful in the dimensionality reduction of an image?

hoose the correct answer from below, please note that this question may have multiple correct answers

A.     Convolution Layer

B.     ReLU Layer

C.      Sigmoid

D.     Pooling Layer

Ans: A,D

Correct options:

• Convolution Layer
• Pooling Layer

Explanation :

• Convolution Layer helps in dimensionality reduction as convolution layer can decrease the size of input depending upon size of kernel, stride etc.
• Pooling layer also decreases size, like if we use Max Pooling, then it takes maximum value present in size of kernel matrix.
• ReLU and sigmoid are just activations, they don't affect the shape of an image.