added AdaBound

nnlib/nnlib.py

@@ -76,7 +76,7 @@ ZeroPadding2D = keras.layers.ZeroPadding2D
 RandomNormal = keras.initializers.RandomNormal
 Model = keras.models.Model
 
-#Adam = keras.optimizers.Adam
+AdaBound = nnlib.AdaBound
 Adam = nnlib.Adam
 Padam = nnlib.Padam
 
@@ -539,6 +539,144 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 return dict(list(base_config.items()) + list(config.items()))
         nnlib.Adam = Adam
         
+        class AdaBound(keras.optimizers.Optimizer):
+            """AdaBound optimizer.
+            Default parameters follow those provided in the original paper.
+            # Arguments
+                lr: float >= 0. Learning rate.
+                final_lr: float >= 0. Final learning rate.
+                beta_1: float, 0 < beta < 1. Generally close to 1.
+                beta_2: float, 0 < beta < 1. Generally close to 1.
+                gamma: float >= 0. Convergence speed of the bound function.
+                epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
+                decay: float >= 0. Learning rate decay over each update.
+                weight_decay: Weight decay weight.
+                amsbound: boolean. Whether to apply the AMSBound variant of this
+                    algorithm.
+                tf_cpu_mode: only for tensorflow backend
+                      0 - default, no changes.
+                      1 - allows to train x2 bigger network on same VRAM consuming RAM
+                      2 - allows to train x3 bigger network on same VRAM consuming RAM*2
+                          and CPU power.
+            # References
+                - [Adaptive Gradient Methods with Dynamic Bound of Learning Rate]
+                  (https://openreview.net/forum?id=Bkg3g2R9FX)
+                - [Adam - A Method for Stochastic Optimization]
+                  (https://arxiv.org/abs/1412.6980v8)
+                - [On the Convergence of Adam and Beyond]
+                  (https://openreview.net/forum?id=ryQu7f-RZ)
+            """
+
+            def __init__(self, lr=0.001, final_lr=0.1, beta_1=0.9, beta_2=0.999, gamma=1e-3,
+                         epsilon=None, decay=0., amsbound=False, weight_decay=0.0, tf_cpu_mode=0, **kwargs):
+                super(AdaBound, self).__init__(**kwargs)
+
+                if not 0. <= gamma <= 1.:
+                    raise ValueError("Invalid `gamma` parameter. Must lie in [0, 1] range.")
+
+                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')
+
+                self.final_lr = final_lr
+                self.gamma = gamma
+
+                if epsilon is None:
+                    epsilon = K.epsilon()
+                self.epsilon = epsilon
+                self.initial_decay = decay
+                self.amsbound = amsbound
+
+                self.weight_decay = float(weight_decay)
+                self.base_lr = float(lr)
+                self.tf_cpu_mode = tf_cpu_mode
+
+            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 = lr * (1. / (1. + self.decay * K.cast(self.iterations,
+                                                              K.dtype(self.decay))))
+
+                t = K.cast(self.iterations, K.floatx()) + 1
+
+                # Applies bounds on actual learning rate
+                step_size = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
+                                  (1. - K.pow(self.beta_1, t)))
+
+                final_lr = self.final_lr * lr / self.base_lr
+                lower_bound = final_lr * (1. - 1. / (self.gamma * t + 1.))
+                upper_bound = final_lr * (1. + 1. / (self.gamma * t))
+
+                e = K.tf.device("/cpu:0") if self.tf_cpu_mode > 0 else None
+                if e: e.__enter__()
+                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.amsbound:
+                    vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
+                else:
+                    vhats = [K.zeros(1) for _ in params]
+                if e: e.__exit__(None, None, None)
+                
+                self.weights = [self.iterations] + ms + vs + vhats
+
+                for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
+                    # apply weight decay
+                    if self.weight_decay != 0.:
+                        g += self.weight_decay * K.stop_gradient(p)
+
+                    e = K.tf.device("/cpu:0") if self.tf_cpu_mode == 2 else None
+                    if e: e.__enter__()                    
+                    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.amsbound:
+                        vhat_t = K.maximum(vhat, v_t)
+                        self.updates.append(K.update(vhat, vhat_t))
+                    if e: e.__exit__(None, None, None)
+                    
+                    if self.amsbound:
+                        denom = (K.sqrt(vhat_t) + self.epsilon)
+                    else:
+                        denom = (K.sqrt(v_t) + self.epsilon)                        
+
+                    # Compute the bounds
+                    step_size_p = step_size * K.ones_like(denom)
+                    step_size_p_bound = step_size_p / denom
+                    bounded_lr_t = m_t * K.minimum(K.maximum(step_size_p_bound,
+                                                             lower_bound), upper_bound)
+
+                    p_t = p - bounded_lr_t
+
+                    self.updates.append(K.update(m, m_t))
+                    self.updates.append(K.update(v, v_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 = {'lr': float(K.get_value(self.lr)),
+                          'final_lr': float(self.final_lr),
+                          'beta_1': float(K.get_value(self.beta_1)),
+                          'beta_2': float(K.get_value(self.beta_2)),
+                          'gamma': float(self.gamma),
+                          'decay': float(K.get_value(self.decay)),
+                          'epsilon': self.epsilon,
+                          'weight_decay': self.weight_decay,
+                          'amsbound': self.amsbound}
+                base_config = super(AdaBound, self).get_config()
+                return dict(list(base_config.items()) + list(config.items()))
+        nnlib.AdaBound = AdaBound
+        
         class Padam(keras.optimizers.Optimizer):
             """Partially adaptive momentum estimation optimizer.
             # Arguments