add cosine decay op, test=develop

paddle/fluid/API.spec

@@ -334,6 +334,7 @@ paddle.fluid.layers.natural_exp_decay ArgSpec(args=['learning_rate', 'decay_step
 paddle.fluid.layers.inverse_time_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
 paddle.fluid.layers.polynomial_decay ArgSpec(args=['learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False))
 paddle.fluid.layers.piecewise_decay ArgSpec(args=['boundaries', 'values'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.cosine_decay ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.noam_decay ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.append_LARS ArgSpec(args=['params_grads', 'learning_rate', 'weight_decay'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.InitState.__init__ ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32'))

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -28,10 +28,12 @@ from . import ops
 from . import tensor
 from ..initializer import init_on_cpu
 from ..framework import default_main_program, Parameter, unique_name, name_scope
+import math
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
-    'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS'
+    'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS',
+    'cosine_decay'
 ]
 
 
@@ -307,6 +309,39 @@ def piecewise_decay(boundaries, values):
     return lr
 
 
+def cosine_decay(learning_rate, step_each_epoch, epochs):
+    """
+    Applies cosine decay to the learning rate.
+
+    when training a model, it is oftem recommended to lower the learning rate as the
+    training progresses. By using this function, the learning rate will be decayed by
+    following cosine decay strategy.
+    
+    Args:
+        learning_rate(Variable|float): The initial learning rate.
+        step_each_epoch(int): the number of steps in an epoch.
+        epochs(int): the number of epochs.
+
+     Returns:
+        Variable: The decayed learning rate.
+
+     Examples:
+
+    ..code-block:: python
+
+  	base_lr = 0.1
+	lr = fluid.layers.cosine_decay(
+	learning_rate = base_lr, step_each_epoch=10000, epochs=120)
+    """
+    with default_main_program()._lr_schedule_guard():
+        global_step = _decay_step_counter()
+
+        cur_epoch = ops.floor(global_step / step_each_epoch)
+        decayed_lr = learning_rate * 0.5 * (
+            ops.cos(cur_epoch * math.pi / epochs) + 1)
+        return decayed_lr
+
+
 def append_LARS(params_grads, learning_rate, weight_decay):
     """
     Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for

python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py

@@ -82,6 +82,13 @@ def piecewise_decay(global_step, boundaries, values):
     return values[len(values) - 1]
 
 
+def cosine_decay(global_step, learning_rate, step_each_epoch, epochs):
+    cur_epoch = math.floor(global_step / step_each_epoch)
+    decayed_lr = learning_rate * 0.5 * (
+        math.cos(cur_epoch * math.pi / epochs) + 1)
+    return decayed_lr
+
+
 class TestLearningRateDecay(unittest.TestCase):
     def check_decay(self, python_decay_fn, fluid_decay_fn, kwargs):
         places = [fluid.CPUPlace()]
@@ -149,6 +156,11 @@ class TestLearningRateDecay(unittest.TestCase):
                 "boundaries": [3, 6, 9],
                 "values": [0.1, 0.2, 0.3, 0.4]
             }),
+            (cosine_decay, layers.cosine_decay, {
+                "learning_rate": 0.1,
+                "step_each_epoch": 100,
+                "epochs": 120
+            }),
         ]
 
         for py_decay_fn, fluid_decay_fn, kwargs in decay_fns: