Reformat the doc strings
@@ -2261,8 +2261,7 @@ def DenseNet_Cifar(conn, model_name=None, n_classes=None, conv_channel = 16, gro
model_name : string
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.
+ Specifies the number of classes. If None is assigned, the model will automatically detect the number of classes based on the training set.
Default: None
conv_channel: int, optional.
Specifies the number of filters of first convolutional layer.
@@ -2289,21 +2288,18 @@ def DenseNet_Cifar(conn, model_name=None, n_classes=None, conv_channel = 16, gro
Specifies a scaling factor to apply to each image..
Default : 1.
random_flip : string, "H" | "HV" | "NONE" | "V"
- 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.
+ 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.
Default : "HV"
random_crop : string, "NONE" or "UNIQUE"
- 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.
+ 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.
Default : "UNIQUE"
offsets=(double-1 <, double-2, ...>), 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.
+ 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 : (85, 111, 139)
Returns
-------
+ :class:`Sequential`
A model object using DenseNet_Cifar architecture.
'''
