Available architectures

class koogu.model.architectures.ConvNet(filters_per_layer, **kwargs)

Boilerplate ConvNet network-building logic that can be used, with appropriate customization of parameters, to build networks like LeNet, AlexNet, etc.

Parameters:

• filters_per_layer – (list/tuple of ints) The length of the list/tuple defines the depth of the network and each value in it specifies the number of filters at the corresponding level.

• pool_sizes – (optional) Must be a list of 2-element tuples (of ints) specifying the factors by which to downscale (vertical, horizontal) following each convolution. The length of the list must be the same as that of filters_per_layer. By default, a pool size of (2, 2) is considered throughout.

• pool_strides – (optional; defaults to whatever pool_sizes is) Must be of similar structure as pool_sizes, and will define the strides that the pooling operation takes along the horizontal and vertical directions.

Other helpful customizations

Parameters:

• add_batchnorm – (bool; default: False) If True, batch normalization layers will be added following each convolution layer.

• pooling_type – (optional) By default, average pooling is performed. Set to ‘max’ to use max pooling instead.

Koogu-style model customizations

Parameters:

• preproc –

  (optional) Use this to add pre-convolution operations to the model. If specified, must be a list of 2-element tuples, with each tuple containing -

  • the name of the operation (either a compatible Keras layer or a transformation from koogu.data.tf_transformations.

  • a Python dictionary specifying parameters to the operation.

• dense_layers – (optional) Use this to add fully-connected (dense) layers to the end of the model network. Can specify a single integer (the added layer will have as many nodes) or a list of integers to add multiple (connected in sequence) dense layers.

class koogu.model.architectures.DenseNet(layers_per_block, **kwargs)

DenseNet (Huang et. al., 2016). This implementation supports both with and without bottleneck.

Parameters:

• layers_per_block – (list/tuple of ints) The length of the list/tuple defines the number of dense-blocks in the network and each value in the list specifies the number of composite function layers (made up of BatchNorm-Conv-ReLU) in the corresponding dense-block.

• growth_rate – (optional; default: 12) Number of composite function layers per dense-block.

• compression – (optional; default: 1.0) Specifies the rate of compression applied in transition blocks. A value of 1.0 means no compression; specify value < 1.0 to bring about compression.

• with_bottleneck – (bool; default: False) Weather to include bottleneck layers.

Other helpful customizations

Parameters:

• quasi_dense – (bool; default: False) If True, feed-forward connections within a dense-block will be reduced, as described in Madhusudhana et. al. 2021.

• pooling_type – (optional) By default, average pooling is performed. Set to ‘max’ to use max pooling instead.

• pool_sizes – (optional) Must be a list of 2-element tuples (of ints) specifying the factors by which to downscale (vertical, horizontal) in each transition block. The length of the list must be one less than that of layers_per_block. By default, a pool size of (3, 3) is considered throughout.

• pool_strides – (optional; defaults to whatever pool_sizes is) Must be of similar structure as pool_sizes, and will define the strides that the pooling operation takes along the horizontal and vertical directions.

Koogu-style model customizations

Parameters:

• preproc –

  (optional) Use this to add pre-convolution operations to the model. If specified, must be a list of 2-element tuples, with each tuple containing -

  • the name of the operation (either a compatible Keras layer or a transformation from koogu.data.tf_transformations.

  • a Python dictionary specifying parameters to the operation.

• dense_layers – (optional) Use this to add fully-connected (dense) layers to the end of the model network. Can specify a single integer (the added layer will have as many nodes) or a list of integers to add multiple (connected in sequence) dense layers.