1 files changed, 81 insertions, 0 deletions
diff --git a/tensorflow/python/training/rmsprop.py b/tensorflow/python/training/rmsprop.py
new file mode 100644
index 0000000000..6dc0ce11ea
--- /dev/null
+++ b/tensorflow/python/training/rmsprop.py
@@ -0,0 +1,81 @@
+"""One-line documentation for rmsprop module.
+
+rmsprop algorithm [tieleman2012rmsprop]
+
+A detailed description of rmsprop.
+
+- maintain a moving (discounted) average of the square of gradients
+- divide gradient by the root of this average
+
+mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
+mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square + epsilon)
+delta = - mom
+
+"""
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import constant_op
+from tensorflow.python.training import optimizer
+from tensorflow.python.training import training_ops
+
+
+class RMSPropOptimizer(optimizer.Optimizer):
+  """Optimizer that implements the RMSProp algorithm.
+
+  @@__init__
+  """
+
+  def __init__(self, learning_rate, decay, momentum=0.0, epsilon=1e-10,
+               use_locking=False, name="RMSProp"):
+    """Construct a new RMSProp optimizer.
+
+    Args:
+      learning_rate: A Tensor or a floating point value.  The learning rate.
+      decay: discounting factor for the history/coming gradient
+      momentum: a scalar tensor.
+      epsilon: small value to avoid zero denominator.
+      use_locking: If True use locks for update operation.
+      name: Optional name prefic for the operations created when applying
+        gradients. Defaults to "RMSProp".
+    """
+    super(RMSPropOptimizer, self).__init__(use_locking, name)
+    self._learning_rate = learning_rate
+    self._decay = decay
+    self._momentum = momentum
+    self._epsilon = epsilon
+
+    # Tensors for learning rate and momentum.  Created in _prepare.
+    self._learning_rate_tensor = None
+    self._decay_tensor = None
+    self._momentum_tensor = None
+    self._epsilon_tensor = None
+
+  def _create_slots(self, var_list):
+    for v in var_list:
+      self._get_or_make_slot(
+          v, constant_op.constant(1.0, dtype=v.dtype, shape=v.get_shape()),
+          "rms", self._name)
+      self._zeros_slot(v, "momentum", self._name)
+
+  def _prepare(self):
+    self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
+                                                       name="learning_rate")
+    self._decay_tensor = ops.convert_to_tensor(self._decay, name="decay")
+    self._momentum_tensor = ops.convert_to_tensor(self._momentum,
+                                                  name="momentum")
+    self._epsilon_tensor = ops.convert_to_tensor(self._epsilon,
+                                                 name="epsilon")
+
+  def _apply_dense(self, grad, var):
+    rms = self.get_slot(var, "rms")
+    mom = self.get_slot(var, "momentum")
+    return training_ops.apply_rms_prop(
+        var, rms, mom,
+        self._learning_rate_tensor,
+        self._decay_tensor,
+        self._momentum_tensor,
+        self._epsilon_tensor,
+        grad, use_locking=self._use_locking).op
+
+  def _apply_sparse(self, grad, var):
+    raise NotImplementedError()