dlpy.applications.EfficientNetB5¶
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dlpy.applications.EfficientNetB5(conn, model_table='EfficientNetB5', n_classes=1000, drop_connect_rate=0, depth_divisor=8, activation_fn='relu', blocks_args=[(1, 16, 1, 3, 1, 0.25), (6, 24, 2, 3, 2, 0.25), (6, 40, 2, 5, 2, 0.25), (6, 80, 3, 3, 2, 0.25), (6, 112, 3, 5, 1, 0.25), (6, 192, 4, 5, 2, 0.25), (6, 320, 1, 3, 1, 0.25)], offsets=(103.53, 116.28, 123.675), norm_stds=(57.375, 57.120000000000005, 58.395), random_flip=None, random_crop=None, random_mutation=None)¶ Generates the EfficientNetB5 architecture.
Parameters: - conn : CAS
Specifies the CAS connection object.
- model_table : string or dict or CAS table, optional
Specifies the CAS table to store the deep learning 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: 1000- drop_connect_rate: double, optional
Specifies the dropout rate at skip connections.
Default: 0.0- depth_divisor: integer, optional
Specifies the unit of network width.
Default: 8- activation_fn: string, optional
Specifies the activation function
- blocks_args: list of dicts
Specifies parameters to construct blocks for the efficientnet model.
- 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.
Default: (255*0.406, 255*0.456, 255*0.485)- 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.
Default: (255*0.225, 255*0.224, 255*0.229)- 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’- random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: ‘none’, ‘random’
References