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Created October 20, 2016 15:22
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UNET to KNIME
Raw
README.md

Goal: Keras to KNIME

Model Conversion

JSON -> KNIME Workflow

The model file is below as a json output.

Weight Importing

Original (better)

Original weights are stored in HDF5

Keras-JS (alternative)

  • Reads the weights from a single binary file and a metadata file.

Needed Operations

Conv2D

2D convolution operation taking an X, Y, Ca image and making a X, Y, Cb sized image by applying Cb convolutions with a window size Xw, Yw, Ca. For UNET specifically this is (3, 3, 64) in the first layer. For UNET the X, Y dimensions are also reduced to the valid area.

MaxPool2D

2D max pooling operation that takes a X, Y, C image and makes a X//n, Y//n, C sized image where only the maximum value is kept (for UNET, n = 2)

UpSample2D

2D upsampling takes a X, Y, C image and makes a Xn, Yn, C image with the nearest (or zeros) interpolated.

Merge

Merge layers take a X, Y, Ca image and combine them along the channel dimension with another image X, Y, Cb to make a X, Y, Ca+Cb image

References

Raw
unet_model.json
{
"class_name": "Model",
"keras_version": "1.1.0",
"config": {
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{
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"config": {
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"sparse": false,
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},
{
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"activation": "relu",
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"name": "convolution2d_58",
"b_regularizer": null,
"trainable": true,
"bias": true,
"border_mode": "same",
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"dim_ordering": "th",
"init": "glorot_uniform"
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{
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{
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],
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{
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"pool_size": [
2,
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],
"strides": [
2,
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],
"dim_ordering": "th"
}
},
{
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"name": "convolution2d_62",
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0,
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]
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],
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2
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{
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{
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{
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{
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2
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"trainable": true
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{
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0,
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{
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{
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[
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0,
0
]
]
],
"config": {
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"size": [
2,
2
],
"trainable": true
}
},
{
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[
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],
[
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},
{
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],
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]
],
"config": {
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],
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}
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}
},
{
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"name": "convolution2d_74",
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],
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},
{
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},
{
"class_name": "Convolution2D",
"name": "convolution2d_76",
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[
[
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0,
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]
]
],
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],
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}
}
],
"name": "model_3",
"output_layers": [
[
"convolution2d_76",
0,
0
]
],
"input_layers": [
[
"input_3",
0,
0
]
]
}
}
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