('weight',weight)self.register_buffer('pos_weight',pos_weight)self.size_average=size_averageself.reduce=reducedefforward(self,input,target):pos_weight=Variable(self.pos_weight)ifnotisinstance(self.pos_weight,Variable)elseself.pos_weightifself.weightisnotNone:weight=Variable(self.weight)ifnot...
seg_len=None, dim=None, **kwargs): super(rand_rotate_layer, self).__init__(**kwargs) self.signal_v = tf.Variable(tf.zeros((batchsize, seg_len, dim)), trainable=False) def get_config(self): config = super().get_config().copy() config.update({ 'signal_v': self.signal_v...
None Application BODY Model Number MSK-G5 Plus Item Type Massage & Relaxation Material ABS Size MediumView more DescriptionReport Item Specifications: Material: High-quality ABS plastic Performance: Up to 4000 rotations per minute Design: Stable five-star pedestal plate with adjustable vibration speed...
side:str="left",value=0,max_len:int|None=None):assertsidein("left","right")ifmax_lenisnotNone:sequences=[x[..., :max_len]forxinsequences]max_seq_len=max(seq.size(-1)forseqinsequences)else:max_len=max_seq_lenpadded
class MyCustomModel: # Define a static variable for now because I don't know how else to get around the callback being static. # I am sure that there is a more elegant way, but for now this works. beta_var = None # Create a new model by stringing together two models. def __ini...
ValueError: None values not supported. I have print the loss and self.model.trainable_weights. the loss is: -0.375 the self.model.trainable_weights: self.model.trainable_weights [<tf.Variable 'embedding_1/embeddings:0' shape=(39660, 300) dtype=float32_ref>, <tf.Variable 'embedding_2/embed...
Sign up for freeto join this conversation on GitHub.Already have an account?Sign in to comment Assignees No one assigned Labels staleThis issue has become stale Projects No projects Milestone No milestone Relationships None yet Development No branches or pull requests Participants Issue actions...