Transcription of Convolutional Neural Networks (CNNs / ConvNets)
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Convolutional Neural Networks (CNNs / ConvNets)
Table of Contents:Architecture OverviewConvNet LayersConvolutional LayerPooling LayerNormalization LayerFully-Connected LayerConverting Fully-Connected Layers to Convolutional LayersConvNet ArchitecturesLayer PatternsLayer Sizing PatternsCase Studies (LeNet / AlexNet / ZFNet / GoogLeNet / VGGNet)Computational ConsiderationsAdditional ReferencesConvolutional Neural Networks (CNNs / ConvNets) Convolutional Neural Networks are very similar to ordinary Neural Networks from the previouschapter: they are made up of neurons that have learnable weights and biases. Each neuronreceives some inputs, performs a dot product and optionally follows it with a non-linearity. Thewhole network still expresses a single differentiable score function: from the raw image pixels onone end to class scores at the other.
thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]). POOL layer will per form a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12]. FC (i.e. fully-connected) la yer will compute the class scores, resulting in volume of size
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