tfbox

a collection of models and tools for tensorflow

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TFBox's main utility lies within:

• tfbox.nn.encoder/decoder/encoder-decoder: an extremely flexible encoder/decoder python classes from which most modern architectures can be built using a simple (yaml) config file.

Additionally, TFBox contains a number of useful tools for TensorFlow, including:

• an extremely flexible sequence class
• tools for model scoring
• weighted metrics
• weighted loss function
• tensorboard callbacks

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INSTALL

pip install tfbox

ADDITIONAL REQUIREMENTS

• imagebox: https://github.com/brookisme/imagebox
• TF>2
• numpy
• pandas
• pyyaml

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MODELS

tfbox.nn.encoder/decoder/encoder-decoder use yaml files to combine keras-model-blocks in tfbox.nn.blocks to build neural-networks. The result is an flexible system from which you can build a large variety of models. Lets start with some examples.

Here is the config for the Xception Network:

xception:

    blocks_config:
        - conv:
            filters: 32
            strides: 2
        - 64
        - stack:
            name: entry_flow_blocks
            seperable: true
            depth: 3
            output_stride: 2
            layers: [128,256,728]
        - stack:
            name: middle_flow
            nb_repeats: 16
            depth: 3
            filters: 728
        - stack:
            name: exit_flow_block
            output_stride: 2
            filters_list: [728,1024,1024]
        - stack:
            name: exit_flow_convs
            seperable: true
            residual: false
            layers: [1536,1536,2048]
        - aspp

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DFSequence

tfbox.loaders.DFSequence builds instances of tf.keras.utils.Sequence for image segmentation models using pandas dataframes. In particular it does almost anything you can imagine - but also can be bit overwhelming.

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SCORING

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METRICS

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LOSS FUNCTIONS

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TENSORBOARD CALLBACKS