Consider a convolutional neural network which takes as input a 42-by-54 color image (i.e. with three channels R, G, B). The first convolutional layer has 16 filters that are 6-by-6, with stride 2 and no zero-padding. Compute the number of: * weights per filter in this layer (including bias): * neurons in this layer: * connections into the neurons in this layer: * independent parameters in this layer:
Consider a convolutional neural network which takes as input a 42-by-54 color image (i.e. with three channels R, G, B). The first convolutional layer has 16 filters that are 6-by-6, with stride 2 and no zero-padding. Compute the number of: * weights per filter in this layer (including bias): * neurons in this layer: * connections into the neurons in this layer: * independent parameters in this layer:
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![Consider a convolutional neural network which takes as input a 42-by-54 color image (i.e. with three channels R, G, B). The first convolutional layer has 16 filters that are 6-by-6, with stride 2 and no zero-padding.
Compute the number of:
* weights per filter in this layer (including bias):
* neurons in this layer:
* connections into the neurons in this layer:
* independent parameters in this layer:](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9b1a67bc-b8d7-44c1-987c-7cc2a46b517b%2F8325e780-50da-4e9e-8547-c9099591d4a1%2Fg3zqa4s_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Consider a convolutional neural network which takes as input a 42-by-54 color image (i.e. with three channels R, G, B). The first convolutional layer has 16 filters that are 6-by-6, with stride 2 and no zero-padding.
Compute the number of:
* weights per filter in this layer (including bias):
* neurons in this layer:
* connections into the neurons in this layer:
* independent parameters in this layer:
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