.

optim_nn.py

@@ -1,7 +1,5 @@
 #!/usr/bin/env python3
 
-# imports
-
 import numpy as np
 nf = np.float32
 nfa = lambda x: np.array(x, dtype=nf)
@@ -31,7 +29,7 @@ class SquaredHalved(Loss):
 
 class Optimizer:
     def __init__(self, alpha=0.1):
-        self.alpha = nfa(alpha)
+        self.alpha = nf(alpha)
         self.reset()
 
     def reset(self):
@@ -43,9 +41,33 @@ class Optimizer:
     def update(self, dW, W):
         W += self.compute(dW, W)
 
-# the following optimizers are blatantly ripped from tiny-dnn:
+# the following optimizers are blatantly lifted from tiny-dnn:
 # https://github.com/tiny-dnn/tiny-dnn/blob/master/tiny_dnn/optimizers/optimizer.h
 
+class Momentum(Optimizer):
+    def __init__(self, alpha=0.01, lamb=0, mu=0.9, nesterov=False):
+        self.alpha = np.asfarray(alpha) # learning rate
+        self.lamb = np.asfarray(lamb) # weight decay
+        self.mu = np.asfarray(mu) # momentum
+        self.nesterov = bool(nesterov)
+
+        self.reset()
+
+    def reset(self):
+        self.dWprev = None
+
+    def compute(self, dW, W):
+        if self.dWprev is None:
+            #self.dWprev = np.zeros_like(dW)
+            self.dWprev = np.copy(dW)
+
+        V = self.mu * self.dWprev - self.alpha * (dW + W * self.lamb)
+        self.dWprev = V
+        if self.nesterov:
+            return self.mu * V - self.alpha * (dW + W * self.lamb)
+        else:
+            return V
+
 class Adam(Optimizer):
     def __init__(self, alpha=0.001, b1=0.9, b2=0.999, b1_t=0.9, b2_t=0.999, eps=1e-8):
         self.alpha = nf(alpha) # learning rate
@@ -107,7 +129,7 @@ class Layer:
         raise NotImplementedError("unimplemented", self)
 
     def do_feed(self, child):
-        pass
+        self.children.append(child)
 
     def be_fed(self, parent):
         self.parents.append(parent)
@@ -154,7 +176,6 @@ class Layer:
         if not child.compatible(self):
             fmt = "{} is incompatible with {}: shape mismatch: {} vs. {}"
             raise Exception(fmt.format(self, child, self.output_shape, child.input_shape))
-        self.children.append(child)
         self.do_feed(child)
         child.be_fed(self)
         return child
@@ -375,14 +396,14 @@ class Model:
         #return lut[input_node] # meaningless value
         return self.dW
 
-    def load_model(self, fn):
+    def load_weights(self, fn):
         # seemingly compatible with keras models at the moment
         import h5py
         f = h5py.File(fn)
-        loadweights = {}
+        weights = {}
         def visitor(name, obj):
             if isinstance(obj, h5py.Dataset):
-                loadweights[name.split('/')[-1]] = nfa(obj[:])
+                weights[name.split('/')[-1]] = nfa(obj[:])
         f.visititems(visitor)
         f.close()
 
@@ -390,10 +411,10 @@ class Model:
         for i in range(len(denses)):
             a, b = i, i + 1
             b_name = "dense_{}".format(b)
-            denses[a].coeffs = loadweights[b_name+'_W'].T
+            denses[a].coeffs = weights[b_name+'_W'].T
-            denses[a].biases = np.expand_dims(loadweights[b_name+'_b'], -1)
+            denses[a].biases = np.expand_dims(weights[b_name+'_b'], -1)
 
-    def save_model(self, fn, overwrite=False):
+    def save_weights(self, fn, overwrite=False):
         raise NotImplementedError("unimplemented", self)
 
 if __name__ == '__main__':
@@ -488,13 +509,18 @@ if __name__ == '__main__':
     if not training:
         model.load_weights(config.fn)
 
-    assert config.optim == 'adam'
+    if config.optim == 'adam':
-    if config.nesterov:
+        assert not config.nesterov, "unimplemented"
-        assert False, "unimplemented"
-    else:
         optim = Adam()
+    elif config.optim == 'sgd':
+        if config.momentum != 0:
+            optim = Momentum(mu=config.momentum, nesterov=config.nesterov)
+        else:
+            optim = Optimizer()
+    else:
+        raise Exception('unknown optimizer', config.optim)
 
-    assert config.loss == 'mse'
+    assert config.loss == 'mse', 'unknown loss function'
     loss = SquaredHalved()
 
     LR = config.LR