1 files changed, 51 insertions, 0 deletions
diff --git a/tensorflow/python/training/momentum.py b/tensorflow/python/training/momentum.py
new file mode 100644
index 0000000000..fdd434359f
--- /dev/null
+++ b/tensorflow/python/training/momentum.py
@@ -0,0 +1,51 @@
+"""Momentum for TensorFlow."""
+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 MomentumOptimizer(optimizer.Optimizer):
+  """Optimizer that implements the Momentum algorithm.
+
+  @@__init__
+  """
+
+  def __init__(self, learning_rate, momentum,
+               use_locking=False, name="Momentum"):
+    """Construct a new Momentum optimizer.
+
+    Args:
+      learning_rate: A `Tensor` or a floating point value.  The learning rate.
+      momentum: A `Tensor` or a floating point value.  The momentum.
+      use_locking: If `True` use locks for update operations.
+      name: Optional name prefix for the operations created when applying
+        gradients.  Defaults to "Momentum".
+    """
+    super(MomentumOptimizer, self).__init__(use_locking, name)
+    self._learning_rate = learning_rate
+    self._momentum = momentum
+
+  def _create_slots(self, var_list):
+    for v in var_list:
+      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._momentum_tensor = ops.convert_to_tensor(self._momentum,
+                                                  name="momentum")
+
+  def _apply_dense(self, grad, var):
+    mom = self.get_slot(var, "momentum")
+    return training_ops.apply_momentum(
+        var, mom,
+        self._learning_rate_tensor, grad, self._momentum_tensor,
+        use_locking=self._use_locking).op
+
+  def _apply_sparse(self, grad, var):
+    mom = self.get_slot(var, "momentum")
+    return training_ops.sparse_apply_momentum(
+        var, mom,
+        self._learning_rate_tensor, grad.values, grad.indices,
+        self._momentum_tensor, use_locking=self._use_locking).op