W-Net¶
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class
src.pipelines.tensorflow_v2.models.wnet.ConvBridgeBlock(channels, activation, initializer)[source]¶ A Convolutional Bridge Block to be used in a W-Net
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class
src.pipelines.tensorflow_v2.models.wnet.MiniUnet(output_channels, activation='relu', initializer='he_normal', filters=0)[source]¶ A mini U-Net, two of these are joined to make a W-Net model
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call(inputs)[source]¶ Applies a mini U-Net to the input.
- Parameters
inputs (
Tensor) – an input image- Return type
Tensor- Returns
the output of a mini U-Net
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model(shape=(512, 512, 1))[source]¶ Returns a U-Net model as tf.keras.Model. This is a workaround to use the functional api, which allows the model to be viewed.
- Parameters
shape (
Tuple[int,int,int]) – the shape of the input- Return type
Model- Returns
the tf.keras.Model instantiated using the functional api
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class
src.pipelines.tensorflow_v2.models.wnet.WNet(output_channels, activation='relu', initializer='he_normal', filters=0)[source]¶ A W-Net model. This model combines two Mini U-Nets where the prediction of the first Mini U-Net is concatenated to the first
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call(input_tensor, training=True)[source]¶ Applies a W-Net model to an input image, which is a call of two sequent mini U-Nets. If the W-Net is not training then only the output of the second mini U-Net is returned.
- Parameters
input_tensor (
Tensor) – an input imagetraining (
bool) – whether the W-Net is being applied to a training sample
- Return type
Tuple[Tensor,Tensor]- Returns
either the ouput of both mini U-Nets or the output of the second mini U-Net
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model(shape=(512, 512, 1))[source]¶ Returns a U-Net model as tf.keras.Model. This is a workaround to use the functional api, which allows the model to be viewed.
- Parameters
shape (
Tuple[int,int,int]) – the shape of the input- Return type
Model- Returns
the tf.keras.Model instantiated using the functional api
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