Update model_resnet50.py
@@ -42,8 +42,8 @@ def ResNet50_Model(s, model_name='RESNET50', n_channels=3, width=224, height=224
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' or 'UNIQUE'
- Default : 'UNIQUE'
+ Valid Values: 'none' or 'unique'
+ Default : 'unique'
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
@@ -57,9 +57,9 @@ def ResNet50_Model(s, model_name='RESNET50', n_channels=3, width=224, height=224
# TODO: quick error-checking and default setting
if (random_crop == None):
- random_crop = "NONE"
- elif random_crop.upper() not in ["NONE", "UNIQUE"]:
- raise ValueError('random_crop can only be "NONE" or "UNIQUE"')
+ random_crop = "none"
+ elif random_crop.lower() not in ["none", "unique"]:
+ raise ValueError('random_crop can only be "none" or "unique"')
if (offsets == None):
offsets = [103.939, 116.779, 123.68]
