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Visualizing Tensorflow networks.

Project description

nn_visualizer

Visualizing Tensorflow neural networks.

Please see the examples.ipynb notebook for an example on how to use the package.

example_output1.png

Currently supporting:

  • Input layers:
    • For dense networks with the shape (x_size)
    • For convolution with the shape (x_size, y_size, n_channels)
  • Convolutional Network Layers:
    • Conv2D
    • MaxPooling2D
    • AveragePooling2D
  • Dense Layers:
    • Dense (also as output)
    • Flatten

Changes

0.7

  • Fixed cases, in which the layers descriptions have overlapped.

0.6

  • Supporting dense layers as input.

0.5

  • Changed build tool to flit, now available as package from PyPi under the name nnvisualizertf.

0.4

  • Changed build configuration.

0.3

  • Changed build tool to hachtling.

0.2

  • Renaming to comply with the python naming conventions.

0.1

  • Initial version.

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