Figure QA1 is a small convolutional neural network that converts a 15x15 image into4 output values. The network has the following layers and operations from input tooutput: convolution with 3 filters, max pooling, ReLu, and finally a fully-connectedlayer. For this network we will not be using any bias to set parameters (b).Fig. QA1(a) How many weights in the convolutional layer do we need to learn
Question
Figure QA1 is a small convolutional neural network that converts a 15x15 image into4 output values. The network has the following layers and operations from input tooutput: convolution with 3 filters, max pooling, ReLu, and finally a fully-connectedlayer. For this network we will not be using any bias to set parameters (b).Fig. QA1(a) How many weights in the convolutional layer do we need to learn
Solution
In a convolutional neural network, the number of weights we need to learn in the convolutional layer depends on the size of the filters, the number of filters, and the depth of the input image.
In this case, the problem does not specify the size of the filters used in the convolutional layer. However, it does mention that there are 3 filters.
Assuming that the filters are of size 3x3 (a common size for convolutional neural networks), and the depth of the input image is 1 (since it's not specified), we can calculate the number of weights as follows:
- Each filter has a size of 3x3, so it has 3*3 = 9 weights.
- Since there are 3 filters, the total number of weights for the filters is 3*9 = 27.
- As the depth of the input image is 1, we multiply the total number of weights for the filters by the depth of the input image, so the total number of weights in the convolutional layer is 27*1 = 27.
So, we need to learn 27 weights in the convolutional layer.
Please note that this answer assumes that the filter size is 3x3 and the depth of the input image is 1, as these values are not specified in the problem. If these values are different, the number of weights to learn in the convolutional layer would be different.
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