mlair.model_modules.u_networks¶
Module Contents¶
Classes¶
A U-net neural network. |
Attributes¶
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mlair.model_modules.u_networks.__date__= 2022-08-29¶
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class
mlair.model_modules.u_networks.UNet(input_shape: list, output_shape: list, layer_configuration: list, optimizer='adam', **kwargs)¶ Bases:
mlair.model_modules.convolutional_networks.CNNfromConfigA U-net neural network.
```python input_shape = [(65,1,9)] output_shape = [(4, )]
# model layer_configuration=[
# 1st block (down) {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 16, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 16, “padding”: “same”}, {“type”: “MaxPooling2D”, “pool_size”: (2, 1), “strides”: (2, 1)}, {“type”: “blocksave”},
# 2nd block (down) {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 32, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 32, “padding”: “same”}, {“type”: “MaxPooling2D”, “pool_size”: (2, 1), “strides”: (2, 1)}, {“type”: “blocksave”},
# 3rd block (down) {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 64, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 64, “padding”: “same”}, {“type”: “MaxPooling2D”, “pool_size”: (2, 1), “strides”: (2, 1)}, {“type”: “blocksave”},
# 4th block (down) {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 128, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 128, “padding”: “same”}, {“type”: “MaxPooling2D”, “pool_size”: (2, 1), “strides”: (2, 1)}, {“type”: “blocksave”},
# 5th block (final down) {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 256, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 256, “padding”: “same”},
# 6th block (up) {“type”: “Conv2DTranspose”, “activation”: “relu”, “kernel_size”: (2, 1), “filters”: 128, “strides”: (2, 1),
“padding”: “same”},
{“type”: “ConcatenateUNet”}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 128, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 128, “padding”: “same”},
# 7th block (up) {“type”: “Conv2DTranspose”, “activation”: “relu”, “kernel_size”: (2, 1), “filters”: 64, “strides”: (2, 1),
“padding”: “same”},
{“type”: “ConcatenateUNet”}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 64, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 64, “padding”: “same”},
# 8th block (up) {“type”: “Conv2DTranspose”, “activation”: “relu”, “kernel_size”: (2, 1), “filters”: 32, “strides”: (2, 1),
“padding”: “same”},
{“type”: “ConcatenateUNet”}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 32, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 32, “padding”: “same”},
# 8th block (up) {“type”: “Conv2DTranspose”, “activation”: “relu”, “kernel_size”: (2, 1), “filters”: 16, “strides”: (2, 1),
“padding”: “same”},
{“type”: “ConcatenateUNet”}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 16, “padding”: “same”}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 16, “padding”: “same”},
# Tail {“type”: “Flatten”}, {“type”: “Dense”, “units”: 128, “activation”: “relu”}
]
model = UNet(input_shape, output_shape, layer_configuration) ```
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_extract_layer_conf(self, layer_opts)¶
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set_model(self)¶ Abstract method to set model.
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