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Questions and Answers
What is the size of the input before being passed to a fully connected layer?
What is the size of the input before being passed to a fully connected layer?
- 512
- 8×64
- 64
- 8×8×64 (correct)
If the input is flattened before being passed to a fully connected layer, what will be the size of the flattened output?
If the input is flattened before being passed to a fully connected layer, what will be the size of the flattened output?
- 512
- 128
- 1024
- 4096 (correct)
What type of layer typically follows a flattened input?
What type of layer typically follows a flattened input?
- Pooling layer
- Convolutional layer
- Fully connected layer (correct)
- Normalization layer
What is the purpose of flattening the input before passing it to a fully connected layer?
What is the purpose of flattening the input before passing it to a fully connected layer?
What would be the size of the flattened output if the input size was 4×4×64?
What would be the size of the flattened output if the input size was 4×4×64?
What is the significance of the number of filters in a CNN filter?
What is the significance of the number of filters in a CNN filter?
What is the purpose of the channels (depth) dimension in a CNN filter?
What is the purpose of the channels (depth) dimension in a CNN filter?
What is the size of the receptive field in a CNN filter with a size of 5×5×3×10?
What is the size of the receptive field in a CNN filter with a size of 5×5×3×10?
What does the height and width of a CNN filter specify?
What does the height and width of a CNN filter specify?
How many activation maps will each filter produce in a CNN filter with a size of 5×5×3×10?
How many activation maps will each filter produce in a CNN filter with a size of 5×5×3×10?
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Study Notes
Flattening Input for Fully Connected Layer
- Given an input with size 8×8×64, when flattened, the output size will be 4096.
- This is because the input dimensions are multiplied together to get the total number of elements.
CNN Filter Characteristics
- A CNN filter with a size of 5×5×3×10 produces 10 activation maps.
- The filter size is broken down into height (5), width (5), channels (3), and number of filters (10).
- Each filter generates a separate activation map, resulting in 10 maps.
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