fast.ai 2020 — Lesson 7

loss_with_wd = loss + wd * (parameters**2).sum()
weight.grad += wd * 2 * weight
def create_params(size):
return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))
class DotProductBias(Module):
def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):
self.user_factors = create_params([n_users, n_factors])
self.user_bias = create_params([n_users])
self.movie_factors = create_params([n_movies, n_factors])
self.movie_bias = create_params([n_movies])
self.y_range = y_range

def forward(self, x):
users = self.user_factors[x[:,0]]
movies = self.movie_factors[x[:,1]]
res = (users*movies).sum(dim=1)
res += self.user_bias[x[:,0]] + self.movie_bias[x[:,1]]
return sigmoid_range(res, *self.y_range)
model = DotProductBias(n_users, n_movies, 50)
learn = Learner(dls, model, loss_func=MSELossFlat())
learn.fit_one_cycle(5, 5e-3, wd=0.1)
cont,cat = cont_cat_split(df, 1, dep_var=dep_var)
procs = [Categorify, FillMissing]
splits = (list(train_idx),list(valid_idx))
dep_var = 'SalePrice'
to = TabularPandas(df, procs, cat, cont, y_names=dep_var, splits=splits)

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