PrognosAIs.Model.Architectures package¶
Submodules¶
PrognosAIs.Model.Architectures.AlexNet module¶
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class
PrognosAIs.Model.Architectures.AlexNet.
AlexNet_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
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padding_type
= 'valid'¶
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class
PrognosAIs.Model.Architectures.AlexNet.
AlexNet_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
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padding_type
= 'valid'¶
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PrognosAIs.Model.Architectures.Architecture module¶
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class
PrognosAIs.Model.Architectures.Architecture.
ClassificationNetworkArchitecture
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.NetworkArchitecture
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class
PrognosAIs.Model.Architectures.Architecture.
NetworkArchitecture
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config: dict = {})[source]¶ Bases:
abc.ABC
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static
check_minimum_input_size
(input_layer: tensorflow.python.keras.engine.input_layer.Input, minimum_input_size: numpy.ndarray)[source]¶
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static
get_corrected_stride_size
(layer: <module 'tensorflow.keras.layers' from '/home/docs/checkouts/readthedocs.org/user_builds/prognosais/envs/stable/lib/python3.7/site-packages/tensorflow/keras/layers/__init__.py'>, stride_size: list, conv_size: list)[source]¶ Ensure that the stride is never bigger than the actual input In this way any network can keep working, indepedent of size
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static
PrognosAIs.Model.Architectures.DDSNet module¶
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class
PrognosAIs.Model.Architectures.DDSNet.
DDSNet
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
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class
PrognosAIs.Model.Architectures.DDSNet.
DDSNet_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DDSNet.DDSNet
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.DDSNet.
DDSNet_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DDSNet.DDSNet
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dims
= 3¶
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PrognosAIs.Model.Architectures.DenseNet module¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_121_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_121_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 3¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_169_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_169_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 3¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_201_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_201_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 3¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_264_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.DenseNet.
DenseNet_264_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.DenseNet.DenseNet
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GROWTH_RATE
= 32¶
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INITIAL_FILTERS
= 64¶
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THETA
= 0.5¶
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dims
= 3¶
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PrognosAIs.Model.Architectures.InceptionNet module¶
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class
PrognosAIs.Model.Architectures.InceptionNet.
InceptionNet_InceptionResNetV2_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.InceptionNet.InceptionResNet
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class
PrognosAIs.Model.Architectures.InceptionNet.
InceptionNet_InceptionResNetV2_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.InceptionNet.InceptionResNet
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class
PrognosAIs.Model.Architectures.InceptionNet.
InceptionResNet
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
PrognosAIs.Model.Architectures.ResNet module¶
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class
PrognosAIs.Model.Architectures.ResNet.
ResNet
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
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class
PrognosAIs.Model.Architectures.ResNet.
ResNet_18_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶
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class
PrognosAIs.Model.Architectures.ResNet.
ResNet_18_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶
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class
PrognosAIs.Model.Architectures.ResNet.
ResNet_18_multioutput_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶
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class
PrognosAIs.Model.Architectures.ResNet.
ResNet_34_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶
PrognosAIs.Model.Architectures.UNet module¶
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class
PrognosAIs.Model.Architectures.UNet.
UNet_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config: dict = {})[source]¶ Bases:
PrognosAIs.Model.Architectures.UNet.Unet
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.UNet.
UNet_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config: dict = {})[source]¶ Bases:
PrognosAIs.Model.Architectures.UNet.Unet
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dims
= 3¶
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class
PrognosAIs.Model.Architectures.UNet.
Unet
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config: dict = {})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.NetworkArchitecture
PrognosAIs.Model.Architectures.VGG module¶
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class
PrognosAIs.Model.Architectures.VGG.
VGG
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.Architecture.ClassificationNetworkArchitecture
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class
PrognosAIs.Model.Architectures.VGG.
VGG_16_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.VGG.VGG
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dims
= 2¶
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class
PrognosAIs.Model.Architectures.VGG.
VGG_16_3D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.VGG.VGG
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dims
= 3¶
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class
PrognosAIs.Model.Architectures.VGG.
VGG_19_2D
(input_shapes: dict, output_info: dict, input_data_type='float32', output_data_type='float32', model_config={})[source]¶ Bases:
PrognosAIs.Model.Architectures.VGG.VGG
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dims
= 2¶
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