Merge pull request #1075 from NLGithubWP/update_train_mlp

Add SGD optimizer for model selection

examples/model_selection_psql/ms_mlp/train_mlp.py

@@ -34,6 +34,102 @@
 
 singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
 
+class MSSGD(MSOptimizer):
+    """Implements stochastic gradient descent (optionally with momentum).
+
+    Nesterov momentum is based on the formula from `On the importance of initialization and momentum in deep learning`__.
+
+    Args:
+        lr(float): learning rate
+        momentum(float, optional): momentum factor(default: 0)
+        weight_decay(float, optional): weight decay(L2 penalty)(default: 0)
+        dampening(float, optional): dampening for momentum(default: 0)
+        nesterov(bool, optional): enables Nesterov momentum(default: False)
+
+    Typical usage example:
+        >> > from singa import opt
+        >> > optimizer = opt.SGD(lr=0.1, momentum=0.9)
+        >> > optimizer.update()
+
+    __ http: // www.cs.toronto.edu / %7Ehinton / absps / momentum.pdf
+
+    .. note::
+        The implementation of SGD with Momentum / Nesterov subtly differs from
+        Sutskever et. al. and implementations in some other frameworks.
+
+        Considering the specific case of Momentum, the update can be written as
+
+        .. math::
+                  v = \rho * v + g \\
+                  p = p - lr * v
+
+        where p, g, v and: math: `\rho` denote the parameters, gradient,
+        velocity, and momentum respectively.
+
+        This is in contrast to Sutskever et. al. and
+        other frameworks which employ an update of the form
+
+        .. math::
+             v = \rho * v + lr * g \\
+             p = p - v
+
+        The Nesterov version is analogously modified.
+    """
+
+    def __init__(self,
+                 lr=0.1,
+                 momentum=0,
+                 dampening=0,
+                 weight_decay=0,
+                 nesterov=False,
+                 dtype=tensor.float32):
+        super(MSSGD, self).__init__(lr, dtype)
+
+        # init momentum
+        if type(momentum) == float or type(momentum) == int:
+            if momentum < 0.0:
+                raise ValueError("Invalid momentum value: {}".format(momentum))
+            self.momentum = Constant(momentum)
+        elif isinstance(momentum, DecayScheduler):
+            self.momentum = momentum
+            momentum = momentum.init_value
+        else:
+            raise TypeError("Wrong momentum type")
+        self.mom_value = self.momentum(self.step_counter).as_type(self.dtype)
+
+        # init dampening
+        if type(dampening) == float or type(dampening) == int:
+            self.dampening = Constant(dampening)
+        elif isinstance(dampening, DecayScheduler):
+            self.dampening = dampening
+            dampening = dampening.init_value
+        else:
+            raise TypeError("Wrong dampening type")
+        self.dam_value = self.dampening(self.step_counter).as_type(self.dtype)
+
+        # init weight_decay
+        if type(weight_decay) == float or type(weight_decay) == int:
+            if weight_decay < 0.0:
+                raise ValueError(
+                    "Invalid weight_decay value: {}".format(weight_decay))
+            self.weight_decay = Constant(weight_decay)
+        elif isinstance(weight_decay, DecayScheduler):
+            self.weight_decay = weight_decay
+        else:
+            raise TypeError("Wrong weight_decay type")
+        self.decay_value = self.weight_decay(self.step_counter).as_type(
+            self.dtype)
+
+        # init other params
+        self.nesterov = nesterov
+        self.moments = dict()
+
+        # check value
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError(
+                "Nesterov momentum requires a momentum and zero dampening")
+
+
 # Data augmentation
 def augmentation(x, batch_size):
     xpad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'symmetric')