Depthwise separable convolution
A depthwise separable convolution factorizes convolution into per-channel spatial filtering followed by pointwise channel mixing, reducing parameters and computation.
Deep Learning with R, Third Edition
A depthwise separable convolution factorizes convolution into independent spatial convolutions over input channels followed by a pointwise convolution that mixes channels.
- How it is used
- Replaces standard convolutions when image convnets need a more parameter-efficient architecture.
- How deep it goes
- Explains the two convolution stages and their architectural assumption without a mathematical derivation.
- Examples
- Uses an R/Keras mini Xception-like dogs-versus-cats model with repeated separable-convolution blocks and reported test accuracy.
- What you can do after
- Apply it via Keras SeparableConv2D or R/Keras layer_separable_conv_2d when convolutional efficiency matters.
Read how Deep Learning with R, Third Edition explains depthwise separable convolution →
Deep Learning with Python, Third Edition
A convolution variant that applies per-channel spatial convolution followed by pointwise channel mixing to reduce parameters and computation.
- How it is used
- Replaces regular convolution in ConvNets to reduce model size and computation while preserving useful representational power.
- How deep it goes
- Explains the depthwise-then-pointwise operation, its independence prior, efficiency benefits, and Keras application, without deriving its mathematics.
- Examples
- Uses Keras SeparableConv2D blocks in a mini Xception-like dogs-versus-cats classifier and compares its parameters and test accuracy.
- What you can do after
- Apply it via Keras SeparableConv2D when constructing smaller, more parameter-efficient ConvNet blocks.
Read how Deep Learning with Python, Third Edition explains depthwise separable convolution →
Related concepts
- convolutional layeralternative to · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
- pointwise convolutionhas part · A pointwise convolution is a convolution with a 1×1 spatial kernel that mixes or transforms channels independently at each spatial position.
- convolutionkind of · Convolution is an operation that applies a kernel across an input, combining values from overlapping local regions with shared weights to produce outputs.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.