files

srush · srush · commit 23f347cb6a37 · 2021-09-10T12:51:28.000-04:00
diff --git a/minitorch/autodiff.py b/minitorch/autodiff.py
@@ -70,12 +70,10 @@ def accumulate_derivative(self, val):
         Args:
             val (number): value to be accumulated
         """
-        # print("accumulating", val, self._derivative, self.name)
         assert self.is_leaf(), "Only leaf variables can have derivatives."
         if self._derivative is None:
             self._derivative = self.zeros()
         self._derivative += val
-        # print("accumulated", val, self._derivative)
 
     def zero_derivative_(self):  # pragma: no cover
         """
diff --git a/project/run_scalar.py b/project/run_scalar.py
@@ -72,8 +72,6 @@ def train(self, data, learning_rate, max_epochs=500, log_fn=default_log_fn):
             # Forward
             loss = 0
             for i in range(data.N):
-                # print("before", self.model.layer1.bias[0].value._derivative)
-                # print("before", self.model.layer3.bias[0].value._derivative)
                 x_1, x_2 = data.X[i]
                 y = data.y[i]
                 x_1 = minitorch.Scalar(x_1)
@@ -86,13 +84,9 @@ def train(self, data, learning_rate, max_epochs=500, log_fn=default_log_fn):
                 else:
                     prob = -out + 1.0
                     correct += 1 if out.data < 0.5 else 0
-                # print(prob, out, y)
                 loss = -prob.log()
                 (loss / data.N).backward()
-                # print("after", self.model.layer1.bias[0].value._derivative)
-                # print("after", self.model.layer3.bias[0].value._derivative)
                 total_loss += loss.data
-                # print(loss)
 
             losses.append(total_loss)
 
