def train(self, learning_rate=1e-5, steps=200, epoch=10):
benchmarks = []
progress = ProgressBar('training', steps)
for step in range(steps):
if step % epoch == 0:
loss, rmse = self.loss()
benchmarks.append((step + 1, loss, rmse))
progress.report(step, loss)
grad_u, grad_m = self.gradients()
self.U = self.U - (learning_rate * grad_u)
self.M = self.M - (learning_rate * grad_m)
loss, rmse = self.loss()
benchmarks.append((step + 1, loss, rmse))
progress.complete()
return benchmarks
