Convolution layer (CONV) The convolution layer (CONV) uses filters that perform convolution functions as it is actually scanning the enter $I$ with respect to its Proportions. Its hyperparameters incorporate the filter size $F$ and stride $S$. The ensuing output $O$ is called aspect map or activation map.
Knowing the complexity from the model In order to assess the complexity of a model, it is often handy to determine the number of parameters that its architecture will have. Inside a offered layer of a convolutional neural network, it is finished as follows:
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This architecture has Inception blocks that comprise 1x1, 3x3, 5x5 convolution layers accompanied by 3x3 max pooling with padding (to make the output of the same condition because the enter) about the preceding layer and concatenates their output.
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Zero-padding Zero-padding denotes the process of including $P$ zeroes to either cash to bitcoin side from the boundaries from the enter. This value can possibly be manually specified or instantly set as a result of among the three modes detailed underneath:
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