During my attempt to implement a gradient descent algorithm, I encountered an intriguing issue related to the ineffective use of **kwargs. The function in question is as follows:
def gradient_descent(g,x,y,alpha,max_its,w,**kwargs):
# switch for verbose
verbose = True
if 'verbose' in kwargs:
verbose = kwargs['verbose']
# determine num train and batch size
num_train = y.size()[1]
batch_size = num_train
if 'batch_size' in kwargs:
batch_size = kwargs['batch_size']
........
This resulted in the following error:
TypeError Traceback (most recent call last)
<ipython-input-12-f71adb8a241b> in <module>()
3 w_train = Variable(torch.Tensor(w_init), requires_grad=True)
4 g = softmax; alpha_choice = 10**(-1); max_its = 100; num_pts = y.size;
batch_size = 10;
----> 5 weight_hist_2,train_hist_2 = gradient_descent(g,x_train,y_train,alpha_choice,max_its,w_train,num_pts,batch_size,verbose = False)
TypeError: gradient_descent() takes 6 positional arguments but 8 were given.
Are there any oversights in my approach to developing this function?