diff --git a/keras_contrib/optimizers/__init__.py b/keras_contrib/optimizers/__init__.py
index bdf6a7bf4..f0b4dce91 100644
--- a/keras_contrib/optimizers/__init__.py
+++ b/keras_contrib/optimizers/__init__.py
@@ -2,7 +2,9 @@
 from .padam import Padam
 from .yogi import Yogi
 from .lars import LARS
+from .adamod import Adamod
 
 # aliases
 ftml = FTML
 lars = LARS
+adamod = Adamod
diff --git a/keras_contrib/optimizers/adamod.py b/keras_contrib/optimizers/adamod.py
new file mode 100644
index 000000000..38b3e85aa
--- /dev/null
+++ b/keras_contrib/optimizers/adamod.py
@@ -0,0 +1,91 @@
+from keras import backend as K
+from keras.optimizers import Optimizer
+
+
+class Adamod(Optimizer):
+    """Adamod optimizer.
+
+    Default parameters follow those provided in the original paper.
+
+    # Arguments
+        learning_rate: float >= 0. Learning rate.
+        beta_1: float, 0 < beta < 1. Generally close to 1.
+        beta_2: float, 0 < beta < 1. Generally close to 1.
+        beta_3: float, 0 < beta < 1. Generally close to 1.
+
+    # References
+        - [An Adaptive and Momental Bound Method for Stochastic Learning](
+           https://arxiv.org/abs/1910.12249)
+    """
+
+    def __init__(self, learning_rate=0.001, beta_1=0.9, beta_2=0.999, beta_3=0.999,
+                 **kwargs):
+        self.initial_decay = kwargs.pop('decay', 0.0)
+        self.epsilon = kwargs.pop('epsilon', K.epsilon())
+        learning_rate = kwargs.pop('lr', learning_rate)
+        super(Adamod, self).__init__(**kwargs)
+        with K.name_scope(self.__class__.__name__):
+            self.iterations = K.variable(0, dtype='int64', name='iterations')
+            self.learning_rate = K.variable(learning_rate, name='learning_rate')
+            self.beta_1 = K.variable(beta_1, name='beta_1')
+            self.beta_2 = K.variable(beta_2, name='beta_2')
+            self.beta_3 = K.variable(beta_3, name='beta_3')
+            self.decay = K.variable(self.initial_decay, name='decay')
+
+    def get_updates(self, loss, params):
+        grads = self.get_gradients(loss, params)
+        self.updates = [K.update_add(self.iterations, 1)]
+
+        lr = self.learning_rate
+        if self.initial_decay > 0:
+            lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
+                                                      K.dtype(self.decay))))
+
+        t = K.cast(self.iterations, K.floatx()) + 1
+        lr_t = lr * K.sqrt(1. - K.pow(self.beta_2, t))
+        m_bias_correction = 1.0 / (1. - K.pow(self.beta_1, t))
+
+        ms = [K.zeros(K.int_shape(p),
+              dtype=K.dtype(p),
+              name='m_' + str(i))
+              for (i, p) in enumerate(params)]
+        vs = [K.zeros(K.int_shape(p),
+              dtype=K.dtype(p),
+              name='v_' + str(i))
+              for (i, p) in enumerate(params)]
+        ss = [K.zeros(K.int_shape(p),
+              dtype=K.dtype(p),
+              name='s_' + str(i))
+              for (i, p) in enumerate(params)]
+
+        self.weights = [self.iterations] + ms + vs + ss
+
+        for p, g, m, v, s in zip(params, grads, ms, vs, ss):
+            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
+            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
+            n_t = lr_t / (K.sqrt(v_t) + self.epsilon)
+            s_t = (self.beta_3 * s) + (1. - self.beta_3) * n_t
+            nhat_t = K.minimum(n_t, s_t)
+            p_t = p - nhat_t * m_t * m_bias_correction
+
+            self.updates.append(K.update(m, m_t))
+            self.updates.append(K.update(v, v_t))
+            self.updates.append(K.update(s, s_t))
+            new_p = p_t
+
+            # Apply constraints.
+            if getattr(p, 'constraint', None) is not None:
+                new_p = p.constraint(new_p)
+
+            self.updates.append(K.update(p, new_p))
+        return self.updates
+
+    def get_config(self):
+        config = {'learning_rate': float(K.get_value(self.learning_rate)),
+                  'beta_1': float(K.get_value(self.beta_1)),
+                  'beta_2': float(K.get_value(self.beta_2)),
+                  'beta_3': float(K.get_value(self.beta_3)),
+                  'decay': float(K.get_value(self.decay)),
+                  'epsilon': self.epsilon}
+        base_config = super(Adamod, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
diff --git a/keras_contrib/tests/optimizers.py b/keras_contrib/tests/optimizers.py
index 2ef7d88b8..7449bb2f5 100644
--- a/keras_contrib/tests/optimizers.py
+++ b/keras_contrib/tests/optimizers.py
@@ -33,7 +33,7 @@ def _test_optimizer(optimizer, target=0.75):
     model = get_model(x_train.shape[1], 10, y_train.shape[1])
     model.compile(loss='categorical_crossentropy',
                   optimizer=optimizer,
-                  metrics=['accuracy'])
+                  metrics=['acc'])
     history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0)
     assert history.history['acc'][-1] >= target
     config = optimizers.serialize(optimizer)
diff --git a/tests/keras_contrib/optimizers/adamod_test.py b/tests/keras_contrib/optimizers/adamod_test.py
new file mode 100644
index 000000000..2288c4484
--- /dev/null
+++ b/tests/keras_contrib/optimizers/adamod_test.py
@@ -0,0 +1,8 @@
+from __future__ import print_function
+from keras_contrib.tests import optimizers
+from keras_contrib.optimizers import Adamod
+
+
+def test_adamod():
+    optimizers._test_optimizer(Adamod())
+    optimizers._test_optimizer(Adamod(beta_3=0.9999))