mlair.model_modules.residual_networks¶
Module Contents¶
Classes¶
A convolutional neural network with residual blocks (skip connections). |
Attributes¶
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mlair.model_modules.residual_networks.__date__= 2022-08-23¶
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class
mlair.model_modules.residual_networks.ResNet(input_shape: list, output_shape: list, layer_configuration: list, optimizer='adam', **kwargs)¶ Bases:
mlair.model_modules.convolutional_networks.CNNfromConfigA convolutional neural network with residual blocks (skip connections).
```python input_shape = [(65,1,9)] output_shape = [(4, )]
# model layer_configuration=[
{“type”: “Conv2D”, “activation”: “relu”, “kernel_size”: (7, 1), “filters”: 32, “padding”: “same”}, {“type”: “MaxPooling2D”, “pool_size”: (2, 1), “strides”: (2, 1)}, {“type”: “residual_block”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 32, “strides”: (1, 1), “kernel_regularizer”: “l2”}, {“type”: “residual_block”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 32, “strides”: (1, 1), “kernel_regularizer”: “l2”}, {“type”: “residual_block”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 64, “strides”: (1, 1), “kernel_regularizer”: “l2”, “use_1x1conv”: True}, {“type”: “residual_block”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 64, “strides”: (1, 1), “kernel_regularizer”: “l2”}, {“type”: “residual_block”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 128, “strides”: (1, 1), “kernel_regularizer”: “l2”, “use_1x1conv”: True}, {“type”: “residual_block”, “activation”: “relu”, “kernel_size”: (3, 1), “filters”: 128, “strides”: (1, 1), “kernel_regularizer”: “l2”}, {“type”: “MaxPooling2D”, “pool_size”: (2, 1), “strides”: (2, 1)}, {“type”: “Dropout”, “rate”: 0.25}, {“type”: “Flatten”}, {“type”: “Dense”, “units”: 128, “activation”: “relu”}
]
model = ResNet(input_shape, output_shape, layer_configuration) ```
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static
residual_block(**layer_kwargs)¶
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_extract_layer_conf(self, layer_opts)¶
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static