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Convolutional Neural Networks (CNNs) Dimension Calculations

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Convolutional Neural Networks (CNNs) Dimension Calculations

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Questions and Answers

What is the purpose of down-sampling in a convolutional neural network?

To reduce the network parameters and prevent overfitting (correct)

To increase the number of network parameters

To apply non-linearity to the convolution layer output

To increase the size of the feature maps

What is the equation for the output of a convolution layer?

hk = f(Wk ∗ x - bk)

hk = f(Wk ∗ x + bk) (correct)

hk = Wk ∗ x - bk

hk = Wk ∗ x + bk

What is the default stride in a CNN?

1 (correct)

What is the effect of using a stride of 1 in a convolutional layer?

The output feature map will have the same dimensions as the input (C) Signup and view all the answers

What is the primary purpose of padding in a CNN?

To preserve the spatial dimensions of the input image (D) Signup and view all the answers

How does the number of filters used in a convolutional layer affect the output feature map?

The output feature map will have more channels (A) Signup and view all the answers

What is the purpose of the pooling function in a convolutional neural network?

To down-sample the feature maps and reduce the number of parameters (D) Signup and view all the answers

What determines the step size at which the network updates its parameters during training?

Learning rate (D) Signup and view all the answers

What is the effect of a large learning rate?

Rapid convergence but unstable training (B) Signup and view all the answers

What is the final output of a convolutional neural network?

A classification score (D) Signup and view all the answers

What determines the number of samples processed by the network in each training iteration?

Batch size (A) Signup and view all the answers

What is the trade-off when choosing a stride in a CNN?

Between information loss and computational efficiency (C) Signup and view all the answers

What is the main purpose of batch normalization in CNN architectures?

To reduce the internal covariance shift of the activation layers (B) Signup and view all the answers

What is the internal covariance shift phenomenon?

A variation in the activation distribution in each layer (C) Signup and view all the answers

What is the effect of batch normalization on the vanishing gradient problem?

It prevents the vanishing gradient problem from arising (C) Signup and view all the answers

What is the benefit of batch normalization in terms of network convergence?

It reduces the time required for network convergence (C) Signup and view all the answers

What is the relationship between batch normalization and weight initialization?

Batch normalization can effectively control the poor weight initialization (D) Signup and view all the answers

Where is the batch normalization layer typically applied in a CNN architecture?

After the convolutional layers (A) Signup and view all the answers