Convolutional Neural Networks (CNNs) Dimension Calculations
18 Questions
2 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

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)
  • 3
  • 2
  • 4

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

    <p>The output feature map will have the same dimensions as the input</p> Signup and view all the answers

    What is the primary purpose of padding in a CNN?

    <p>To preserve the spatial dimensions of the input image</p> Signup and view all the answers

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

    <p>The output feature map will have more channels</p> Signup and view all the answers

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

    <p>To down-sample the feature maps and reduce the number of parameters</p> Signup and view all the answers

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

    <p>Learning rate</p> Signup and view all the answers

    What is the effect of a large learning rate?

    <p>Rapid convergence but unstable training</p> Signup and view all the answers

    What is the final output of a convolutional neural network?

    <p>A classification score</p> Signup and view all the answers

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

    <p>Batch size</p> Signup and view all the answers

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

    <p>Between information loss and computational efficiency</p> Signup and view all the answers

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

    <p>To reduce the internal covariance shift of the activation layers</p> Signup and view all the answers

    What is the internal covariance shift phenomenon?

    <p>A variation in the activation distribution in each layer</p> Signup and view all the answers

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

    <p>It prevents the vanishing gradient problem from arising</p> Signup and view all the answers

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

    <p>It reduces the time required for network convergence</p> Signup and view all the answers

    What is the relationship between batch normalization and weight initialization?

    <p>Batch normalization can effectively control the poor weight initialization</p> Signup and view all the answers

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

    <p>After the convolutional layers</p> Signup and view all the answers

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser