C4M1

• Detect image contrast

• Detect vertical edges

• Detect horizontal edges

• • Detect 45 degree edges

Suppose your input is a 300 by 300 color (RGB) image, and you are not using a convolutional network. If the first hidden layer has 100 neurons, each one fully connected to the input, how many parameters does this hidden layer have (including the bias parameters)?

• 9,000,001

• 9,000,100

• 27,000,001

• 27,000,100

Suppose your input is a 300 by 300 color (RGB) image, and you use a convolutional layer with 100 filters that are each 5x5. How many parameters does this hidden layer have (including the bias parameters)?

• 2501

• 2600

• 7500

• 7600

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?

• 29x29x16

• 16x16x32

• 16x16x16

• 29x29x32

You have an input volume that is 15x15x8, and pad it using “pad=2.” What is the dimension of the resulting volume (after padding)?

• 19x19x12

• 17x17x10

• 19x19x8

• 17x17x8

You have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, and stride of 1. You want to use a “same” convolution. What is the padding?

• 1

• 2

• 3

• 7

You have an input volume that is 32x32x16, and apply max pooling with a stride of 2 and a filter size of 2. What is the output volume?

• 32x32x8

• 15x15x16

• 16x16x16

• 16x16x8

Because pooling layers do not have parameters, they do not affect the backpropagation (derivatives) calculation

• TRUE

• FALSE

In lecture we talked about “parameter sharing” as a benefit of using convolutional networks. Which of the following statements about parameter sharing in ConvNets are true? (Check all that apply.)

• It allows gradient descent to set many of the parameters to zero, thus making the connections sparse.

• It reduces the total number of parameters, thus reducing overfitting.

• It allows parameters learned for one task to be shared even for a different task (transfer learning).

• It allows a feature detector to be used in multiple locations throughout the whole input image/input volume.

In lecture we talked about “sparsity of connections” as a benefit of using convolutional layers. What does this mean?

• Regularization causes gradient descent to set many of the parameters to zero.

• Each filter is connected to every channel in the previous layer.

• Each layer in a convolutional network is connected only to two other layers

• Each activation in the next layer depends on only a small number of activations from the previous layer.