util/convlr.rb: generate directly applyable learning rates from update and gradient histories learned with AdaDelta

1 files changed, 28 insertions, 0 deletions

diff --git a/util/convlr.rb b/util/convlr.rb
new file mode 100755
index 0000000..864f397
--- /dev/null
+++ b/util/convlr.rb
@@ -0,0 +1,28 @@
+#!/usr/bin/env ruby
+
+require 'zipf'
+
+updates = SparseVector.new
+ReadFile.readlines_strip(ARGV[0]).each { |line|
+  k,v = line.split
+  updates[k] = v.to_f
+}
+grads = SparseVector.new
+ReadFile.readlines_strip(ARGV[1]).each { |line|
+  k,v = line.split
+  grads[k] = v.to_f
+}
+
+smooth = 0.000001
+
+ks = updates.keys + grads.keys
+
+rates = SparseVector.new
+ks.each { |k|
+  rates[k] = -1*(Math.sqrt(updates[k]+smooth)/Math.sqrt(grads[k]+smooth))
+}
+
+rates.each { |k,v|
+  puts "#{k} #{v}"
+}
+