Constructing an Optimized Baseline Convolutional Network
Explore how to build an optimized baseline convolutional network for rare event prediction. Learn to select appropriate Conv1D, Conv2D, and Conv3D layers, configure kernel sizes, filters, padding, and pooling strategies. Understand layer structuring including activation and dense layers for effective feature extraction and model performance.
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Baseline network
Construct a simple sequential baseline model with layer structure as shown below. Note to swap activation and pooling layers.
Convolution layer
Conv1Dvs.Conv2Dvs.Conv3D: AConv‘x’Dis chosen based on the number of spatial axes in the input. UseConv1D,Conv2D, andConv3Dfor inputs with 1, 2, and 3 spatial axes, respectively. Follow the table below for details.
Axes and Channels in Grid-Like Inputs to Convolutional Networks
Time Series | Image | Video | |
Axis-1 (Spatial dim1) | Time | Height | Height |
Axis-2 (Spatial dim2) | - | Width | Width |
Axis-3 (Spatial dim3) | - | - | Time |
Channels | Features (one in univariate time series) | Colors | Colors |
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Input Shape | (samples, time, features) | (samples, height, width, colors) | (samples, height, width, time, colors) |
| An integer, t, specifying the time window | An integer tuple (h, w) specifying the height and width window | An integer tuple, (h, w, t), specifying the height, width, and time window |
Kernel shape | (t, features) | (h, w, colors) | (h, w, t, colors) |
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Kernel: The
kernel_sizeargument is a tuple in which each element represents the size of the kernel along a spatial axis. The size can be taken as:...