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/Constructing an Optimized Baseline Convolutional Network
Constructing an Optimized Baseline Convolutional Network
Learn how to build a baseline convolutional network with a strategic swap of pooling and activation.
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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:...