dlpy.applications.ResNet50_Caffe¶
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dlpy.applications.ResNet50_Caffe(conn, model_table='RESNET50_CAFFE', n_classes=1000, n_channels=3, width=224, height=224, scale=1, batch_norm_first=False, 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 ResNet50 architecture with convolution shortcut.
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- batch_norm_first : bool, optional
Specifies whether to have batch normalization layer before the convolution layer in the residual block. For a detailed discussion about this, please refer to this paper: He, Kaiming, et al. “Identity mappings in deep residual networks.” European Conference on Computer Vision. Springer International Publishing, 2016.
Default: False- 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. This option is required when pre_trained_weights=True. Must be a fully qualified file name of SAS-compatible file (e.g., *.caffemodel.h5)
- include_top : bool, optional
Specifies whether to include pre-trained weights of the top layers (i.e., the last layer for classification).
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