dlpy.applications.VGG16¶
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dlpy.applications.VGG16(conn, model_table='VGG16', n_classes=1000, n_channels=3, width=224, height=224, scale=1, random_flip=None, random_crop=None, offsets=(103.939, 116.779, 123.68), pre_trained_weights=False, pre_trained_weights_file=None, include_top=False, random_mutation=None, reshape_after_input=None)¶ Generates a deep learning model with the VGG16 architecture.
Parameters: - conn : CAS
Specifies the CAS connection object.
- 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: 1000- 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: 224- height : int, optional
Specifies the height of the input layer.
Default: 224- scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1- 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’- 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: (103.939, 116.779, 123.68)- pre_trained_weights : bool, optional
Specifies whether to use the pre-trained weights trained on the ImageNet data set.
Default: False- pre_trained_weights_file : string, optional
Specifies the file name for the pre-trained weights. Must be a fully qualified file name of SAS-compatible file (e.g., *.caffemodel.h5)
Note: Required when pre_trained_weights=True.- include_top : bool, optional
Specifies whether to include pre-trained weights of the top layers (i.e., the FC layers)
Default: False- random_mutation : string, optional
Specifies how to apply data augmentations/mutations to the data in the input layer.
Valid Values: ‘none’, ‘random’- reshape_after_input : Reshape, optional
Specifies whether to add a reshape layer after the input layer.
Returns: - Sequential
If pre_trained_weights is False
- Model
If pre_trained_weights is True
References