micro_dl.networks module
Submodules
Submodules
micro_dl.networks.base_conv_net module
Base class for all networks
micro_dl.networks.base_image_to_vector_net module
Network for regressing a vector from a set of images
- class micro_dl.networks.base_image_to_vector_net.BaseImageToVectorNet(network_config, predict=False)
Bases:
BaseConvNetNetwork for regression/classification from a set of images
- build_net()
Assemble the network
micro_dl.networks.base_unet module
Base class for U-net
- class micro_dl.networks.base_unet.BaseUNet(network_config, predict=False)
Bases:
BaseConvNetBase U-net implementation
2) residual Unet: https://arxiv.org/pdf/1711.10684.pdf border_mode=’same’ preferred over ‘valid’. Else have to interpolate the last block to match the input image size.
- build_net()
Assemble the network
micro_dl.networks.conv_blocks module
Collection of different conv blocks typically used in conv nets
- micro_dl.networks.conv_blocks.conv_block(layer, network_config, block_idx)
Convolution block
- Allowed block-seq: [conv-BN-activation, conv-activation-BN,
BN-activation-conv]
- To accommodate params of advanced activations, activation is a dict with
keys ‘type’ and ‘params’.
For a complete list of keys in network_config, refer to BaseConvNet.__init__() in base_conv_net.py
- Parameters:
layer (keras.layers) – current input layer
network_config (dict) – dict with network related keys
block_idx (int) – block index in the network
- Returns:
keras.layers after performing operations in block-sequence repeated for num_convs_per_block times
TODO: data_format from network_config won’t work for full 3D models in predict if depth is set to None
- micro_dl.networks.conv_blocks.downsample_conv_block(layer, network_config, block_idx, downsample_shape=None)
Conv-BN-activation block
- Parameters:
layer (keras.layers) – current input layer
network_config (dict) – please check conv_block()
block_idx (int) – block index in the network
downsample_shape (tuple) – anisotropic downsampling kernel shape
- Returns:
keras.layers after downsampling and conv_block
- micro_dl.networks.conv_blocks.pad_channels(input_layer, final_layer, channel_axis)
Zero pad along channels before residual/skip add
- Parameters:
input_layer (keras.layers) – input layer to be padded with zeros / 1x1
to match shape of final layer :param keras.layers final_layer: layer whose shape has to be matched :param int channel_axis: dimension along which to pad :return: keras.layer layer_padded - layer with the same shape as final
layer
- micro_dl.networks.conv_blocks.residual_conv_block(layer, network_config, block_idx)
Convolution block where the last layer is merged (+) with input layer
- Parameters:
layer (keras.layers) – current input layer
network_config (dict) – please check conv_block()
block_idx (int) – block index in the network
- Returns:
keras.layers after conv-block and residual merge
- micro_dl.networks.conv_blocks.residual_downsample_conv_block(layer, network_config, block_idx, downsample_shape=None)
Convolution block where the last layer is merged (+) with input layer
- Parameters:
layer (keras.layers) – current input layer
network_config (dict) – please check conv_block()
block_idx (int) – block index in the network
downsample_shape (tuple) – anisotropic downsampling kernel shape
- Returns:
keras.layers after conv-block and residual merge
- micro_dl.networks.conv_blocks.skip_merge(skip_layers, upsampled_layers, skip_merge_type, data_format, num_dims, padding)
Skip connection concatenate/add to upsampled layer :param keras.layer skip_layers: as named :param keras.layer upsampled_layers: as named :param str skip_merge_type: [add, concat] :param str data_format: [channels_first, channels_last] :param int num_dims: as named :param str padding: same or valid :return: keras.layer skip merged layer
micro_dl.networks.image2D_to_vector_net module
Image 2D to vector / scalar conv net
- class micro_dl.networks.image2D_to_vector_net.Image2DToVectorNet(network_config, predict=False)
Bases:
BaseImageToVectorNetUses 2D images as input
micro_dl.networks.image3D_to_vector_net module
Image 3D to vector / scalar conv net
- class micro_dl.networks.image3D_to_vector_net.Image3DToVectorNet(network_config, predict=False)
Bases:
BaseImageToVectorNetUses 3D images as input
micro_dl.networks.unet2D module
Unet 2D
micro_dl.networks.unet3D module
Unet 3D
micro_dl.networks.unet_stack_2D module
Predict the center slice from a stack of 3-5 slices
- class micro_dl.networks.unet_stack_2D.UNetStackTo2D(network_config, predict=False)
Bases:
BaseUNetImplements a U-net that takes a stack and predicts the center slice
- build_net()
Assemble the network
Treat the downsampling blocks as 3D and the upsampling blocks as 2D. All blocks use 3D filters: either 3x3x3 or 3x3x1. Another variant is: if the stack could be treated as channels similar to RGB and 2D convolutions are sufficient to extract features. This could be done by using UNet2D with num_input_channels = 3 (N) and num_output_channels = 1
micro_dl.networks.unet_stack_stack module
Unet for 3D volumes with anisotropic shape
micro_dl.networks.vqnet module
- class micro_dl.networks.vqnet.VQVAE(network_config, predict=False)
Bases:
BaseConvNet- build_net()
Assemble/build the network from layers
- decoder_pass(inputs)
- encoder_pass(inputs)
- class micro_dl.networks.vqnet.VectorQuantizer(k, **kwargs)
Bases:
Layer- build(input_shape)
Creates the layer weights.
Must be implemented on all layers that have weights.
- # Arguments
- input_shape: Keras tensor (future input to layer)
or list/tuple of Keras tensors to reference for weight shape computations.
- call(inputs)
This is where the layer’s logic lives.
- # Arguments
inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.
- # Returns
A tensor or list/tuple of tensors.
- sample(k_index)
Module contents
Classes related to different NN architectures