Consider a convolutional neural network whose inputs are an RGB color image of size 39 x 39 (i.e., the input volume size is 39 x 39 x 3 ). The network has three convolutional layers and the parameters of each convolutional layer are given in Figure 1. Compute the output volume size of the feature maps at each convolutional layer. Conv1 Conv2 Conv3 Filter size = 3 x 3 Filter size 5 x 5 Filter size 5 x 5 # filters = 40 Stride - 2 # filters = 20 # filters = 10 Stride = 1 Stride = 2 Input: 39 x 39 x 3 Padding = 0 Padding = 0 Padding = 0 Figure 1. CNN model.

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Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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Consider a convolutional neural network whose inputs are an RGB color image of
size 39 x 39 (i.e., the input volume size is 39 x 39 x 3 ). The network has three
convolutional layers and the parameters of each convolutional layer are given in Figure 1.
Compute the output volume size of the feature maps at each convolutional layer.
Conv1
Conv2
Conv3
Filter size = 3 x 3 Filter size 5 x 5 Filter size = 5 x 5
# filters = 10
# filters = 20
# filters = 40
Stride = 1
Stride = 2
Stride = 2
Input: 39 x 39 x 3
Padding = 0
Padding = 0
Padding = 0
Figure 1. CNN model.
Transcribed Image Text:Consider a convolutional neural network whose inputs are an RGB color image of size 39 x 39 (i.e., the input volume size is 39 x 39 x 3 ). The network has three convolutional layers and the parameters of each convolutional layer are given in Figure 1. Compute the output volume size of the feature maps at each convolutional layer. Conv1 Conv2 Conv3 Filter size = 3 x 3 Filter size 5 x 5 Filter size = 5 x 5 # filters = 10 # filters = 20 # filters = 40 Stride = 1 Stride = 2 Stride = 2 Input: 39 x 39 x 3 Padding = 0 Padding = 0 Padding = 0 Figure 1. CNN model.
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