ValueError: The `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.

[asmagen]

Using 'None' as input for sm.Unet doesn't work:

# define network parameters
n_classes = 1 if len(SEGMENTATION_CLASSES) == 1 else (len(SEGMENTATION_CLASSES) + 1)  # case for binary and multiclass segmentation
activation = 'sigmoid' if n_classes == 1 else 'softmax'

#create model
model = sm.Unet(BACKBONE, classes=n_classes, activation=activation, encoder_weights='None',input_shape = (None, None, 3))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-31-a0481d301d20> in <module>()
      4 
      5 #create model
----> 6 model = sm.Unet(BACKBONE, classes=n_classes, activation=activation, encoder_weights='None',input_shape = (None, None, 3))

5 frames
/usr/local/lib/python3.6/dist-packages/keras_applications/densenet.py in DenseNet(blocks, include_top, weights, input_tensor, input_shape, pooling, classes, **kwargs)
    178 
    179     if not (weights in {'imagenet', None} or os.path.exists(weights)):
--> 180         raise ValueError('The `weights` argument should be either '
    181                          '`None` (random initialization), `imagenet` '
    182                          '(pre-training on ImageNet), '

ValueError: The `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.

[kenextra]

It should be encoder_weights=None not encoder_weights='None'