diff --git a/scheduler/tanh_lr.py b/scheduler/tanh_lr.py
new file mode 100644
index 00000000..a8a67777
--- /dev/null
+++ b/scheduler/tanh_lr.py
@@ -0,0 +1,81 @@
+import logging
+import math
+import numpy as np
+import torch
+
+from .scheduler import Scheduler
+
+
+logger = logging.getLogger(__name__)
+
+
+class TanhLRScheduler(Scheduler):
+    """
+    Cosine annealing with restarts.
+    This is described in the paper https://arxiv.org/abs/1608.03983.
+    """
+
+    def __init__(self,
+                 optimizer: torch.optim.Optimizer,
+                 t_initial: int,
+                 lb: float = -6.,
+                 ub: float = 4.,
+                 t_mul: float = 1.,
+                 lr_min: float = 0.,
+                 decay_rate: float = 1.,
+                 warmup_updates=0,
+                 warmup_lr_init=0,
+                 initialize=True) -> None:
+        super().__init__(optimizer, param_group_field="lr", initialize=initialize)
+
+        assert t_initial > 0
+        assert lr_min >= 0
+        self.lb = lb
+        self.ub = ub
+        self.t_initial = t_initial
+        self.t_mul = t_mul
+        self.lr_min = lr_min
+        self.decay_rate = decay_rate
+        self.cycle_limit = 0
+        self.warmup_updates = warmup_updates
+        self.warmup_lr_init = warmup_lr_init
+        if self.warmup_updates:
+            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates for v in self.base_values]
+        else:
+            self.warmup_steps = [1 for _ in self.base_values]
+        if self.warmup_lr_init:
+            super().update_groups(self.warmup_lr_init)
+
+    def get_epoch_values(self, epoch: int):
+        # this scheduler doesn't update on epoch
+        return None
+
+    def get_update_values(self, num_updates: int):
+        if num_updates < self.warmup_updates:
+            lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
+        else:
+            curr_updates = num_updates - self.warmup_updates
+
+            if self.t_mul != 1:
+                i = math.floor(math.log(1 - curr_updates / self.t_initial * (1 - self.t_mul), self.t_mul))
+                t_i = self.t_mul ** i * self.t_initial
+                t_curr = curr_updates - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial
+            else:
+                i = curr_updates // self.t_initial
+                t_i = self.t_initial
+                t_curr = curr_updates - (self.t_initial * i)
+
+            if self.cycle_limit == 0 or i <= self.cycle_limit:
+                gamma = self.decay_rate ** i
+                lr_min = self.lr_min * gamma
+                lr_max_values = [v * gamma for v in self.base_values]
+
+                tr = t_curr / t_i
+                lrs = [
+                    lr_min + 0.5 * (lr_max - lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr))
+                    for lr_max in lr_max_values
+                ]
+            else:
+                lrs = [self.lr_min * (self.decay_rate ** self.cycle_limit) for _ in self.base_values]
+
+        return lrs