From 09cfcf97e0e938a93867c7d445f1c9b4dcfea023 Mon Sep 17 00:00:00 2001
From: Samuel Fadel <samuelfadel@gmail.com>
Date: Tue, 16 Apr 2019 15:33:58 -0300
Subject: Initial commit.

---
 README.md | 47 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 README.md

(limited to 'README.md')

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..aeffbfe
--- /dev/null
+++ b/README.md
@@ -0,0 +1,47 @@
+# pytorch_ema
+
+A very small library for computing exponential moving averages of model
+parameters.
+
+This library was written for personal use. Nevertheless, if you run into issues
+or have suggestions for improvement, feel free to open either a new issue or
+pull request.
+
+## Example
+
+```python
+import torch
+import torch.nn.functional as F
+
+from torch_ema import ExponentialMovingAverage
+
+
+x_train = torch.rand((100, 10))
+y_train = torch.rand(100).round().long()
+model = torch.nn.Linear(10, 2)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
+ema = ExponentialMovingAverage(model.parameters(), decay=0.995)
+
+# Train for a few epochs
+model.train()
+for _ in range(10):
+    logits = model(x_train)
+    loss = F.cross_entropy(logits, y_train)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    ema.update(model.parameters())
+
+# Compare losses:
+# Original
+model.eval()
+logits = model(x_train)
+loss = F.cross_entropy(logits, y_train)
+print(loss.item())
+
+# With EMA
+ema.copy_to(model.parameters())
+logits = model(x_train)
+loss = F.cross_entropy(logits, y_train)
+print(loss.item())
+```
-- 
cgit v1.2.3