MFreidank · September 4, 2018 12:00 · Sep 4, 2018
diff --git a/Padam.py b/Padam.py
@@ -0,0 +1,93 @@
+from keras import backend as K
+from keras.optimizers import Optimizer
+
+
+class Padam(Optimizer):
+    def __init__(self, lr=1e-1, beta_1=0.9, beta_2=0.999,
+                 epsilon=1e-8, decay=0., amsgrad=False, partial=1. / 4., **kwargs):
+        """ Partially adaptive momentum estimation optimizer.
+
+        # Arguments
+            lr: float >= 0. Learning rate.
+            beta_1: float, 0 < beta < 1. Generally close to 1.
+            beta_2: float, 0 < beta < 1. Generally close to 1.
+            epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
+            decay: float >= 0. Learning rate decay over each update.
+            amsgrad: boolean. Whether to apply the AMSGrad variant of this
+                algorithm from the paper "On the Convergence of Adam and
+                Beyond".
+            partial: float >=0. Parameter controlling partial momentum adaption.
+
+        # References
+            - [Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks](https://arxiv.org/pdf/1806.06763.pdf)
+
+        """
+        super(Padam, self).__init__(**kwargs)
+        with K.name_scope(self.__class__.__name__):
+            self.iterations = K.variable(0, dtype='int64', name='iterations')
+            self.lr = K.variable(lr, name='lr')
+            self.beta_1 = K.variable(beta_1, name='beta_1')
+            self.beta_2 = K.variable(beta_2, name='beta_2')
+            self.decay = K.variable(decay, name='decay')
+        if epsilon is None:
+            epsilon = K.epsilon()
+        self.epsilon = epsilon
+        self.partial = partial
+        self.initial_decay = decay
+        self.amsgrad = amsgrad
+
+    def get_updates(self, loss, params):
+        grads = self.get_gradients(loss, params)
+        self.updates = [K.update_add(self.iterations, 1)]
+
+        lr = self.lr
+        if self.initial_decay > 0:
+            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)) /
+                     (1. - K.pow(self.beta_1, t)))
+
+        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
+        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
+        if self.amsgrad:
+            vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
+        else:
+            vhats = [K.zeros(1) for _ in params]
+        self.weights = [self.iterations] + ms + vs + vhats
+
+        for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
+            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
+            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
+            if self.amsgrad:
+                vhat_t = K.maximum(vhat, v_t)
+                denom = (K.sqrt(vhat_t) + self.epsilon)
+                self.updates.append(K.update(vhat, vhat_t))
+            else:
+                denom = (K.sqrt(v_t) + self.epsilon)
+
+            self.updates.append(K.update(m, m_t))
+            self.updates.append(K.update(v, v_t))
+
+            # Partial momentum adaption.
+            p_t = p - (lr_t * (m_t / (denom ** (self.partial * 2))))
+
+            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 = {'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)),
+                  'decay': float(K.get_value(self.decay)),
+                  'epsilon': self.epsilon,
+                  'amsgrad': self.amsgrad}
+        base_config = super(Padam, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
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