If a convolutional layer has an input size of 28x28 and applies a filter of size 3x3 with a stride of 1 and no padding, what is the size of the output feature map?

Consider a convolutional neural network model that has three convolution layers. The first layer has 50 filters, the second layer has 100 filters, and the third layer has 200 filters. All convolution layers have stride=2, and the same padding. The input images are 300x400 pixels with RGB channels.What is the feature map size after the third convolution layer?4 points150x200x5075x100x10038x50x200

You have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, using a stride of 2 and no padding. What is the output volume?Question 9Answera.29x29x32b.16x16x16c.16x16x32d.29x29x16

Suppose that in a MobileNet v2 Bottleneck block the input volume has shape 64×64×1664×64×16. If we use 3232 filters for the expansion and 1616 filters for the projection. What is the size of the input and output volume of the depthwise convolution, assuming a pad='same'?1 point

In a certain modified GoogLenet the in input to the 1x1 convolution layer which has 16 filters is 28x28x92. The number of operations with 1x1 convolutions will be*1 point240844810035200124436481154048

In a CNN, if we have a 32x32x3 input volume followed by four layers of 3x3x3filters with stride = 1 and pad = 0, how many parameters must be learned?(a) 4(b) 27(c) 28(d) 108(e) 112