dlpy.applications.UNet¶
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dlpy.applications.UNet(conn, model_table='UNet', n_classes=2, n_channels=1, width=256, height=256, scale=0.00392156862745098, norm_stds=None, offsets=None, random_mutation=None, init=None, bn_after_convolutions=False, random_flip=None, random_crop=None, output_image_type=None, output_image_prob=False)¶ Generates a deep learning model with the U-Net architecture.
Parameters: - conn : CAS
Specifies the connection of the CAS connection.
- model_table : string, optional
Specifies the name of CAS table to store the model.
- n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will automatically detect the number of classes based on the training set.
Default: 2- n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3- width : int, optional
Specifies the width of the input layer.
Default: 256- height : int, optional
Specifies the height of the input layer.
Default: 256- scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1.0/255- norm_stds : double or iter-of-doubles, optional
Specifies a standard deviation for each channel in the input data. The final input data is normalized with specified means and standard deviations.
- offsets : double or iter-of-doubles, optional
Specifies an offset for each channel in the input data. The final input data is set after applying scaling and subtracting the specified offsets.
- random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: ‘none’, ‘random’- init : str
Specifies the initialization scheme for convolution layers.
Valid Values: XAVIER, UNIFORM, NORMAL, CAUCHY, XAVIER1, XAVIER2, MSRA, MSRA1, MSRA2
Default: None- bn_after_convolutions : Boolean
If set to True, a batch normalization layer is added after each convolution layer.
- random_flip : string, optional
Specifies how to flip the data in the input layer when image data is used. Approximately half of the input data is subject to flipping.
Valid Values: ‘h’, ‘hv’, ‘v’, ‘none’- random_crop : string, optional
Specifies how to crop the data in the input layer when image data is used. Images are cropped to the values that are specified in the width and height parameters. Only the images with one or both dimensions that are larger than those sizes are cropped.
Valid Values: ‘none’, ‘unique’, ‘randomresized’, ‘resizethencrop’- output_image_type: string, optional
Specifies the output image type of this layer. possible values: [ WIDE, PNG, BASE64 ]
Default: WIDE- output_image_prob: bool, options
Does not include probabilities if doing classification (default).
Returns: References