diff --git a/onn/optimizer.py b/onn/optimizer.py
index 0350b2f..b432e02 100644
--- a/onn/optimizer.py
+++ b/onn/optimizer.py
@@ -4,8 +4,23 @@ from .float import _f, _0, _1
 from .optimizer_base import *
 from .utility import *
 
-# some of the the following optimizers are blatantly lifted from tiny-dnn:
-# https://github.com/tiny-dnn/tiny-dnn/blob/master/tiny_dnn/optimizers/optimizer.h
+
+def filter_gradients(accum, grads, param):
+    # NOTE: this modifies accum in-place.
+    # param > 0 acts as a simple one-pole low-pass filter, unity at DC.
+    # param < 0 acts as an accumulator with a decay of -param, nonunity at DC.
+    # param == 0 simply copies grads into accum.
+    if param == 0:
+        accum[:] = grads
+    if param < 0:
+        if param != -1:
+            accum *= -param
+        accum += grads
+    elif param == 1:
+        pass
+    else:
+        accum += (1 - param) * (grads - accum)
+    return accum
 
 
 class Momentum(Optimizer):
@@ -598,3 +613,144 @@ class AMSgrad(Optimizer):
         self.b2_t *= self.b2
 
         return ret
+
+
+class Adamlike(Optimizer):
+    # this generalizes a lot of algorithms that are
+    # either subsets or supersets of the Adam optimizer.
+    # refer to the subclasses for details.
+
+    # the arguments to init default to Adam's.
+    def __init__(self, lr=0.001, b1=0.9, b2=0.999,
+                 power=1/2, debias=True, runmax=False, eps=1e-8):
+        self.b1 = _f(b1)  # decay term
+        self.b2 = _f(b2)  # decay term
+        self.b1_t_default = _f(b1)  # decay term power t
+        self.b2_t_default = _f(b2)  # decay term power t
+        self.power = _f(power)
+        self.debias = bool(debias)
+        self.runmax = bool(runmax)
+        self.eps = _f(eps)
+
+        super().__init__(lr)
+
+    def reset(self):
+        self.mt = None
+        self.vt = None
+        self.vtmax = None
+        self.b1_t = self.b1_t_default
+        self.b2_t = self.b2_t_default
+
+    def compute(self, dW, W):
+        if self.mt is None:
+            self.mt = np.zeros_like(dW)
+        if self.vt is None:
+            self.vt = np.zeros_like(dW)
+        if self.vtmax is None and self.runmax:
+            self.vtmax = np.zeros_like(dW)
+
+        # keep local references of mt and vt to simplify
+        # implementing all the variations of Adam later.
+        mt = filter_gradients(self.mt, dW, self.b1)
+        vt = filter_gradients(self.vt, np.square(dW), self.b2)
+
+        if self.runmax:
+            self.vtmax[:] = np.maximum(vt, self.vtmax)
+            vt = self.vtmax
+
+        if self.debias:
+            if self.b1_t != 1:
+                mt = mt / (1 - self.b1_t)
+            if self.b2_t != 1:
+                vt = vt / (1 - self.b2_t)
+
+        if self.power == 0:
+            delta = mt
+        elif self.power == 1:
+            delta = mt / (vt + self.eps)
+        elif self.power == 1/2:  # TODO: is this actually faster?
+            delta = mt / (np.sqrt(vt) + self.eps)
+        elif self.power == 1/3:  # TODO: is this actually faster?
+            delta = mt / (np.cbrt(vt) + self.eps)
+        else:
+            delta = mt / (vt**self.power + self.eps)
+
+        if self.debias:
+            # decay gain.
+            self.b1_t *= self.b1
+            self.b2_t *= self.b2
+
+        return -self.lr * delta
+
+
+class Adagrad(Adamlike):
+    # paper: https://web.stanford.edu/~jduchi/projects/DuchiHaSi11.pdf
+
+    def __init__(self, lr=0.01, eps=1e-8):
+        super().__init__(lr=lr, b1=0.0, b2=-1.0,
+                         power=1/2, debias=False, runmax=False, eps=eps)
+
+    @property
+    def g(self):
+        return self.vt
+
+    @g.setter
+    def g(self, value):
+        self.vt = value
+
+
+class RMSprop(Adamlike):
+    # slides: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
+
+    def __init__(self, lr=0.001, mu=0.99, eps=1e-8):
+        super().__init__(lr=lr, b1=0.0, b2=mu,
+                         power=1/2, debias=False, runmax=False, eps=eps)
+
+    @property
+    def mu(self):
+        return self.b2
+
+    @mu.setter
+    def mu(self, value):
+        self.b2 = value
+
+    @property
+    def g(self):
+        return self.vt
+
+    @g.setter
+    def g(self, value):
+        self.vt = value
+
+
+class Adam(Adamlike):
+    # paper: https://arxiv.org/abs/1412.6980
+    # Adam generalizes RMSprop, and
+    # adds a decay term to the regular (non-squared) delta, and performs
+    # debiasing to compensate for the filtered deltas starting from zero.
+
+    def __init__(self, lr=0.001, b1=0.9, b2=0.999,
+                 debias=True, eps=1e-8):
+        super().__init__(lr=lr, b1=b1, b2=b2,
+                         power=1/2, debias=debias, runmax=False, eps=eps)
+
+
+class AMSgrad(Adamlike):
+    # paper: https://openreview.net/forum?id=ryQu7f-RZ
+    # based on Adam. this simply adds a running element-wise maximum to vt.
+
+    def __init__(self, lr=0.001, b1=0.9, b2=0.999,
+                 debias=True, eps=1e-8):
+        super().__init__(lr=lr, b1=b1, b2=b2,
+                         power=1/2, debias=debias, runmax=True, eps=eps)
+
+
+class Padam(Adamlike):
+    # paper: https://arxiv.org/abs/1806.06763
+    # paper: https://arxiv.org/abs/1808.05671
+    # based on AMSgrad. this configures the power of vt to be closer to zero.
+
+    def __init__(self, lr=0.1, b1=0.9, b2=0.999,
+                 power=1/8, debias=True, eps=1e-8):
+        super().__init__(lr=lr, b1=b1, b2=b2,
+                         power=power, debias=debias, runmax=True, eps=eps)