Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride- 2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max-...

Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride-<br>2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max-<br>pooling,stride-2 --->3X3-filter (128-filters),Stride-2,padding-0 --> 2X2 max-pooling,stride-2 ---> Fully<br>Connected with 512 neurons ---> Fully Connected with 512 neurons ---> Fully Connected with 10 neurons.<br>How many parameters do the Gradient-descent algorithm need to learn for this network? You may omit<br>the bias values from your calculations.<br>

Extracted text: Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride- 2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max- pooling,stride-2 --->3X3-filter (128-filters),Stride-2,padding-0 --> 2X2 max-pooling,stride-2 ---> Fully Connected with 512 neurons ---> Fully Connected with 512 neurons ---> Fully Connected with 10 neurons. How many parameters do the Gradient-descent algorithm need to learn for this network? You may omit the bias values from your calculations.

Jun 07, 2022

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Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride- 2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max-...

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