fix nitpicks

lucid/misc/redirected_relu_grad.py

@@ -15,18 +15,23 @@
 
 """Redirected ReLu Gradient Overrides
 
-When visualizing models we often[0] have to optimize through ReLu activation
-functions. Where accessing pre-relu tensors is too hard, we use these
-overrides to allow gradient to flow back through the ReLu—even if it didn't
-activate ("dead neuron") and thus its derivative is 0.
+When we visualize ReLU networks, the initial random input we give the model may
+not cause the neuron we're visualizing to fire at all. For a ReLU neuron, this
+means that no gradient flow backwards and the visualization never takes off.
+One solution would be to find the pre-ReLU tensor, but that can be tedious.
+
+These functions provide a more convenient solution: temporarily override the
+gradient of ReLUs to allow gradient to flow back through the ReLU -- even if it
+didn't activate and had a derivative of zero -- allowing the visualization
+process to get started.
 
 Usage:
 ```python
 from lucid.misc.gradient_override import gradient_override_map
 from lucid.misc.redirected_relu_grad import redirected_relu_grad
 
 with gradient_override_map({'Relu': redirected_relu_grad}):
-  model.import_graph(…)
+  model.import_graph(...)
 ```
 
 Discussion:
@@ -35,6 +40,9 @@
 change this behavior to allow gradient pushing the input into a desired regime
 between these points.
 
+(This override first checks if the entire gradient would be blocked, and only
+changes it in that case. It does this check independently for each batch entry.)
+
 In effect, this replaces the relu gradient with the following:
 
 Regime       | Effect