In the formula to calculate output shape of tensor after convolution operation
W_2 = (W_1-F+2P)/S + 1,
$W_2$ is the output shape of the tensor
$W_1$ is the input shape
$F$ is the filter ...
Let's suppose I have an image with 16 channels that goes to a convolutional layer, which has 3 trainable $7 \times 7$ filters, so the output of this layer has depth 3.
How does the convolutional layer ...
Below is a quote from CS231n:
Prefer a stack of small filter CONV to one large receptive field CONV layer. Suppose that you stack three 3x3 CONV layers on top of each other (with non-linearities in ...