add a super basic test comparing our grad backward pass to that of py…

…torch

README.md

@@ -24,7 +24,7 @@ print(x.grad) # prints 62.0 - i.e. the numerical value of dy / dx
 
 ### Tracing / visualization
 
-Have a look at the jupyter notebook `trace_graph.py` to also produce graphviz visualizations. E.g. this one is of a simple 2D neuron, arrived at by calling `draw_dot` on the code below, and it shows both the data (top number in each node) and the gradient (bottom number in each node).
+Have a look at the jupyter notebook `trace_graph.ipynb` to also produce graphviz visualizations. E.g. this one is of a simple 2D neuron, arrived at by calling `draw_dot` on the code below, and it shows both the data (top number in each node) and the gradient (bottom number in each node).
 
 ```python
 from micrograd import nn

test/test_basic.py

@@ -0,0 +1,27 @@
+import torch
+from micrograd.engine import Value
+from micrograd import nn
+
+def test_sanity_check():
+
+    x = Value(-4.0)
+    z = 2 * x + 2 + x
+    q = z.relu() + z * x
+    h = (z * z).relu()
+    y = h + q + q * x
+    y.backward()
+    xmg, ymg = x, y
+
+    x = torch.Tensor([-4.0])
+    x.requires_grad = True
+    z = 2 * x + 2 + x
+    q = z.relu() + z * x
+    h = (z * z).relu()
+    y = h + q + q * x
+    y.backward()
+    xpt, ypt = x, y
+
+    # forward pass went well
+    assert ymg.data == ypt.data.item()
+    # backward pass went well
+    assert xmg.grad == xpt.grad.item()