Merge d0341fc into 2f8acfe

keras/optimizers.py

@@ -415,13 +415,90 @@ def get_config(self):
         return dict(list(base_config.items()) + list(config.items()))
 
 
+class Nadam(Optimizer):
+    '''
+    Nesterov Adam optimizer: Adam ~ RMSProp + momentum, Nadam ~ RMSProp + NAG
+
+    Default parameters follow those provided in the paper.
+
+    # Arguments
+        lr: float >= 0. Learning rate.
+        beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1.
+        epsilon: float >= 0. Fuzz factor.
+
+    # References
+    [1] Nadam report - http://cs229.stanford.edu/proj2015/054_report.pdf
+    [2] On the importance of initialization and momentum in deep learning -
+        http://www.cs.toronto.edu/~fritz/absps/momentum.pdf
+    '''
+    def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999,
+                 epsilon=1e-8, **kwargs):
+        super(Nadam, self).__init__(**kwargs)
+        self.__dict__.update(locals())
+        self.iterations = K.variable(0.)
+        self.m_schedule = K.variable(1.)
+        self.lr = K.variable(lr)
+        self.beta_1 = K.variable(beta_1)
+        self.beta_2 = K.variable(beta_2)
+
+    def get_updates(self, params, constraints, loss):
+        grads = self.get_gradients(loss, params)
+        self.updates = [(self.iterations, self.iterations + 1)]
+
+        t = self.iterations + 1
+
+        # Due to the recommendations in [2], i.e. warming momentum schedule
+        schedule_decay = 0.004 # Exactly given in [1] and [2]
+        momentum_cache_t = self.beta_1 * (1. - 0.5 * (K.pow(0.96, t * schedule_decay)))
+        momentum_cache_t_1 = self.beta_1 * (1. - 0.5 * (K.pow(0.96, (t + 1) * schedule_decay)))
+        m_schedule_new = self.m_schedule * momentum_cache_t
+        m_schedule_next = self.m_schedule * momentum_cache_t * momentum_cache_t_1
+        self.updates.append((self.m_schedule, m_schedule_new))
+
+        ms = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
+        vs = [K.variable(np.zeros(K.get_value(p).shape)) for p in params]
+
+        self.weights = ms + vs
+
+        for p, g, m, v in zip(params, grads, ms, vs):
+            # the following equations given in [1]
+            g_prime = g / (1. - m_schedule_new)
+            m_t = self.beta_1 * m + (1. - self.beta_1) * g
+            m_t_prime = m_t / (1. - m_schedule_next)
+            v_t = self.beta_2 * v + (1. - self.beta_2) * K.square(g)
+            v_t_prime = v_t / (1. - K.pow(self.beta_2, t))
+            m_t_bar = (1. - momentum_cache_t) * g_prime + momentum_cache_t_1 * m_t_prime
+
+            self.updates.append((m, m_t))
+            self.updates.append((v, v_t))
+
+            p_t = p - self.lr * m_t_bar / (K.sqrt(v_t_prime) + self.epsilon)
+            new_p = p_t
+
+            # apply constraints
+            if p in constraints:
+                c = constraints[p]
+                new_p = c(new_p)
+            self.updates.append((p, new_p))
+        return self.updates
+
+    def get_config(self):
+        config = {'lr': float(K.get_value(self.lr)),
+                  'beta_1': float(K.get_value(self.beta_1)),
+                  'beta_2': float(K.get_value(self.beta_2)),
+                  'epsilon': self.epsilon}
+        base_config = super(Nadam, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
 # aliases
 sgd = SGD
 rmsprop = RMSprop
 adagrad = Adagrad
 adadelta = Adadelta
 adam = Adam
 adamax = Adamax
+nadam = Nadam
 
 
 def get(identifier, kwargs=None):

tests/keras/test_optimizers.py

@@ -2,7 +2,7 @@
 import pytest
 
 from keras.utils.test_utils import get_test_data
-from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax
+from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam
 from keras.models import Sequential
 from keras.layers.core import Dense, Activation
 from keras.utils.np_utils import to_categorical
@@ -63,5 +63,9 @@ def test_adamax():
     _test_optimizer(Adamax())
 
 
+def test_nadam():
+    _test_optimizer(Nadam())
+
+
 if __name__ == '__main__':
     pytest.main([__file__])