**Layer-1:** Filter Size – 3 X 3, Number of Filters – 10, Stride – 1, Padding – 0

**Layer-2:** Filter Size – 5 X 5, Number of Filters – 20, Stride – 2, Padding – 0

**Layer-3:** Filter Size – 5 X5 , Number of Filters – 40, Stride – 2, Padding – 0

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**If we give the input a 3-D image to the network of dimension 39** **X** **39, then determine the dimension of the vector after passing through a fully connected layer in the architecture.**

Here we have the input image of dimension 39 X 39 X 3 convolves with 10 filters of size 3 X 3 and takes the Stride as 1 with no padding. After these operations, we will get an output of 37 X 37 X 10.

We then convolve this output further to the next convolution layer as an input and get an output of 7 X 7 X 40. Finally, by taking all these numbers (**7 X 7 X 40 = 1960**), and then unroll them into a large vector, and pass them to a classifier that will make predictions.