Update Adam optimizer documentation (apache#13754)

python/mxnet/optimizer/optimizer.py

@@ -1030,26 +1030,28 @@ class Adam(Optimizer):
     Stochastic Optimization*, available at http://arxiv.org/abs/1412.6980.
 
     If the storage types of grad is ``row_sparse``, and ``lazy_update`` is True, \
-    **lazy updates** are applied by::
+    **lazy updates** at step t are applied by::
 
         for row in grad.indices:
             rescaled_grad[row] = clip(grad[row] * rescale_grad + wd * weight[row], clip_gradient)
             m[row] = beta1 * m[row] + (1 - beta1) * rescaled_grad[row]
             v[row] = beta2 * v[row] + (1 - beta2) * (rescaled_grad[row]**2)
-            w[row] = w[row] - learning_rate * m[row] / (sqrt(v[row]) + epsilon)
+            lr = learning_rate * sqrt(1 - beta1**t) / (1 - beta2**t)
+            w[row] = w[row] - lr * m[row] / (sqrt(v[row]) + epsilon)
 
     The lazy update only updates the mean and var for the weights whose row_sparse
     gradient indices appear in the current batch, rather than updating it for all indices.
     Compared with the original update, it can provide large improvements in model training
     throughput for some applications. However, it provides slightly different semantics than
     the original update, and may lead to different empirical results.
 
-    Otherwise, **standard updates** are applied by::
+    Otherwise, **standard updates** at step t are applied by::
 
         rescaled_grad = clip(grad * rescale_grad + wd * weight, clip_gradient)
         m = beta1 * m + (1 - beta1) * rescaled_grad
         v = beta2 * v + (1 - beta2) * (rescaled_grad**2)
-        w = w - learning_rate * m / (sqrt(v) + epsilon)
+        lr = learning_rate * sqrt(1 - beta1**t) / (1 - beta2**t)
+        w = w - lr * m / (sqrt(v) + epsilon)
 
     This optimizer accepts the following parameters in addition to those accepted
     by :class:`.Optimizer`.