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Я пытаюсь применить momentum и decay к мини-пакетной SGD: как правильно обновить мои веса, я получаю странные результаты, как только устанавливается decay..

   import numpy as np
  def _mini_batch(self,X,y,batch_size):
      # sack data for shuffle - mini batch
      rows = len(X)
      X_full = np.hstack(( np.ones((rows,1)), X , np.array(y).reshape(rows,-1) ))
      np.random.shuffle(X_full)
      # Performing minibatch
      num_batches = rows // batch_size  
      for rng in range(num_batches): 
        start_rng, end_rng = rng*batch_size , (rng 1)*batch_size
        yield X_full[start_rng:end_rng, :-1], X_full[start_rng:end_rng, -1]  # X_batch, y_batch
      if not rows % batch_size == 0: 
          yield X_full[end_rng:rows, :-1], X_full[end_rng:rows, -1] # X_batch, y_batch      
decay_rate = 0.2
alpha = 0.1 #learning rate
weights =  np.random.normal(size=X.shape[-1]) #np.zeros(X.shape[-1])   
rows = len(X)
for i in range(epochs): 
     # init mini-batch to update gradients for each batch
     for X_batch, y_batch in _mini_batch(X,y,batch_size):    
          
          train_predictions = np.dot(X,weights)  #y_hat
          errors =   np.subtract(train_predictions, y)
          
          self.weights = (1. - decay_rate) * weights - alpha * np.dot(X.T,errors) / rows