.

optim_nn.py

@@ -1,13 +1,16 @@
 #!/usr/bin/env python3
 
 import numpy as np
+# ugly shorthand:
 nf = np.float32
 nfa = lambda x: np.array(x, dtype=nf)
 ni = np.int
 nia = lambda x: np.array(x, dtype=ni)
 
+# just for speed, not strictly essential:
 from scipy.special import expit as sigmoid
 
+# used for numbering layers like Keras:
 from collections import defaultdict
 
 # Initializations
@@ -47,7 +50,7 @@ class SquaredHalved(Loss):
         return r
 
 class SomethingElse(Loss):
-    # generalizes Absolute and SquaredHalved
+    # generalizes Absolute and SquaredHalved (|dx| = 1)
     # plot: https://www.desmos.com/calculator/fagjg9vuz7
     def __init__(self, a=4/3):
         assert 1 <= a <= 2, "parameter out of range"
@@ -99,7 +102,7 @@ class Momentum(Optimizer):
 
         V = self.mu * self.dWprev - self.alpha * (dW + W * self.lamb)
         self.dWprev[:] = V
-        if self.nesterov:
+        if self.nesterov: # TODO: is this correct? looks weird
             return self.mu * V - self.alpha * (dW + W * self.lamb)
         else:
             return V
@@ -528,17 +531,14 @@ class Model:
 
         f.close()
 
-class Ritual:
-    def __init__(self,
-        optim=None,
-        learn_rate=1e-3, learn_anneal=1, learn_advance=0,
-        loss=None, mloss=None):
-        self.optim = optim if optim is not None else SGD()
+class Ritual: # i'm just making up names at this point
+    def __init__(self, learner=None, loss=None, mloss=None):
+        self.learner = learner if learner is not None else Learner(Optimizer())
         self.loss = loss if loss is not None else Squared()
         self.mloss = mloss if mloss is not None else loss
-        self.learn_rate = nf(learn_rate)
-        self.learn_anneal = nf(learn_anneal)
-        self.learn_advance = nf(learn_advance)
+
+    def reset(self):
+        self.learner.reset(optim=True)
 
     def measure(self, residual):
         return self.mloss.mean(residual)
@@ -546,17 +546,6 @@ class Ritual:
     def derive(self, residual):
         return self.loss.dmean(residual)
 
-    def update(self, dW, W):
-        self.optim.update(dW, W)
-
-    def prepare(self, epoch):
-        self.optim.alpha = self.learn_rate * self.learn_anneal**epoch
-
-    def restart(self, optim=False):
-        self.learn_rate *= self.learn_anneal**self.learn_advance
-        if optim:
-            self.optim.reset()
-
     def train_batched(self, model, inputs, outputs, batch_size, return_losses=False):
         cumsum_loss = 0
         batch_count = inputs.shape[0] // batch_size
@@ -570,7 +559,7 @@ class Ritual:
             residual = predicted - batch_outputs
 
             model.backward(self.derive(residual))
-            self.update(model.dW, model.W)
+            self.learner.optim.update(model.dW, model.W)
 
             batch_loss = self.measure(residual)
             if np.isnan(batch_loss):
@@ -584,6 +573,89 @@ class Ritual:
         else:
             return avg_loss
 
+class Learner:
+    def __init__(self, optim, epochs=100, rate=None):
+        assert isinstance(optim, Optimizer)
+        self.optim = optim
+        self.start_rate = optim.alpha if rate is None else float(rate)
+        self.epochs = int(epochs)
+        self.reset()
+
+    def reset(self, optim=False):
+        self.started = False
+        self.epoch = 0
+        if optim:
+            self.optim.reset()
+
+    @property
+    def epoch(self):
+        return self._epoch
+
+    @epoch.setter
+    def epoch(self, new_epoch):
+        self._epoch = int(new_epoch)
+        self.rate = self.rate_at(self._epoch)
+
+    @property
+    def rate(self):
+        return self.optim.alpha
+
+    @rate.setter
+    def rate(self, new_rate):
+        self.optim.alpha = new_rate
+
+    def rate_at(self, epoch):
+        return self.start_rate
+
+    def next(self):
+        # returns whether or not to continue training. updates.
+        if self.epoch + 1 >= self.epochs:
+            return False
+        if self.started:
+            self.epoch += 1
+        else:
+            self.started = True
+            self.epoch = self.epoch # poke property setter just in case
+        return True
+
+    @property
+    def final_rate(self):
+        return self.rate_at(self.epochs - 1)
+
+class AnnealingLearner(Learner):
+    def __init__(self, optim, epochs=100, rate=None, halve_every=10):
+        self.halve_every = float(halve_every)
+        self.anneal = 0.5**(1/self.halve_every)
+        super().__init__(optim, epochs, rate)
+
+    def rate_at(self, epoch):
+        return super().rate_at(epoch) * self.anneal**epoch
+
+class DumbLearner(AnnealingLearner):
+    # this is my own awful contraption. it's not really "SGD with restarts".
+    def __init__(self, optim, epochs=100, rate=None, halve_every=10, restarts=0, restart_advance=20, callback=None):
+        self.restart_epochs = int(epochs)
+        self.restarts = int(restarts)
+        self.restart_advance = float(restart_advance)
+        self.restart_callback = callback
+        epochs = self.restart_epochs * (self.restarts + 1)
+        super().__init__(optim, epochs, rate, halve_every)
+
+    def rate_at(self, epoch):
+        sub_epoch = epoch % self.restart_epochs
+        restart = epoch // self.restart_epochs
+        return super().rate_at(sub_epoch) * (self.anneal**self.restart_advance)**restart
+
+    def next(self):
+        if not super().next():
+            return False
+        sub_epoch = self.epoch % self.restart_epochs
+        restart = self.epoch // self.restart_epochs
+        if restart > 0 and sub_epoch == 0:
+            if self.restart_callback is not None:
+                self.restart_callback(restart)
+        return True
+
 def multiresnet(x, width, depth, block=2, multi=1,
                 activation=Relu, style='batchless',
                 init=init_he_normal):
@@ -725,12 +797,16 @@ def run(program, args=[]):
                 print(str(node)+sep+('\n'+str(node)+sep).join(children))
     log('parameters', model.param_count)
 
+    #
+
     training = config.epochs > 0 and config.restarts >= 0
 
     if config.fn_load is not None:
         log('loading weights', config.fn_load)
         model.load_weights(config.fn_load)
 
+    #
+
     if config.optim == 'adam':
         assert not config.nesterov, "unimplemented"
         optim = Adam()
@@ -742,6 +818,22 @@ def run(program, args=[]):
     else:
         raise Exception('unknown optimizer', config.optim)
 
+    def rscb(restart):
+        measure_error() # declared later...
+        log("restarting", restart)
+        if config.restart_optim:
+            optim.reset()
+
+    #
+
+    learner = DumbLearner(optim, epochs=config.epochs, rate=config.learn,
+                          halve_every=config.learn_halve_every,
+                          restarts=config.restarts, restart_advance=config.learn_restart_advance,
+                          callback=rscb)
+    log("final learning rate", "{:10.8f}".format(learner.final_rate))
+
+    #
+
     def lookup_loss(maybe_name):
         if isinstance(maybe_name, Loss):
             return maybe_name
@@ -754,14 +846,7 @@ def run(program, args=[]):
     loss = lookup_loss(config.loss)
     mloss = lookup_loss(config.mloss) if config.mloss else loss
 
-    anneal = 0.5**(1/config.learn_halve_every)
-    ritual = Ritual(optim=optim,
-                    learn_rate=config.learn, learn_anneal=anneal,
-                    learn_advance=config.learn_restart_advance,
-                    loss=loss, mloss=mloss)
-
-    learn_end = config.learn * (anneal**config.learn_restart_advance)**config.restarts * anneal**(config.epochs - 1)
-    log("final learning rate", "{:10.8f}".format(learn_end))
+    ritual = Ritual(learner=learner, loss=loss, mloss=mloss)
 
     # Training
 
@@ -788,30 +873,26 @@ def run(program, args=[]):
         train_losses.append(train_err)
         valid_losses.append(valid_err)
 
-    for i in range(config.restarts + 1):
-        measure_error()
+    measure_error()
 
-        if i > 0:
-            log("restarting", i)
-            ritual.restart(optim=config.restart_optim)
+    assert inputs.shape[0] % config.batch_size == 0, \
+           "inputs is not evenly divisible by batch_size" # TODO: lift this restriction
+    while learner.next():
+        indices = np.arange(inputs.shape[0])
+        np.random.shuffle(indices)
+        shuffled_inputs = inputs[indices] / x_scale
+        shuffled_outputs = outputs[indices] / y_scale
 
-        assert inputs.shape[0] % config.batch_size == 0, \
-               "inputs is not evenly divisible by batch_size" # TODO: lift this restriction
-        for e in range(config.epochs):
-            indices = np.arange(inputs.shape[0])
-            np.random.shuffle(indices)
-            shuffled_inputs = inputs[indices] / x_scale
-            shuffled_outputs = outputs[indices] / y_scale
+        avg_loss, losses = ritual.train_batched(model,
+            shuffled_inputs, shuffled_outputs,
+            config.batch_size,
+            return_losses=True)
+        batch_losses += losses
 
-            ritual.prepare(e)
-            #log("learning rate", "{:10.8f}".format(ritual.optim.alpha))
-
-            avg_loss, losses = ritual.train_batched(model,
-                shuffled_inputs, shuffled_outputs,
-                config.batch_size,
-                return_losses=True)
-            log("average loss", "{:11.7f}".format(avg_loss))
-            batch_losses += losses
+        #log("learning rate", "{:10.8f}".format(learner.rate))
+        #log("average loss", "{:11.7f}".format(avg_loss))
+        fmt = "epoch {:4.0f}, rate {:10.8f}, loss {:11.7f}"
+        log("info", fmt.format(learner.epoch + 1, learner.rate, avg_loss))
 
     measure_error()
 
@@ -821,6 +902,8 @@ def run(program, args=[]):
 
     # Evaluation
 
+    # this is just an example/test of how to predict a single output;
+    # it doesn't measure the quality of the network or anything.
     a = (192, 128, 64)
     b = (64, 128, 192)
     X = np.expand_dims(np.hstack((a, b)), 0) / x_scale