begin support for multiple input/output layers

onn_core.py

@@ -24,6 +24,44 @@ _pi = _f(np.pi)
 class LayerIncompatibility(Exception):
     pass
 
+# Node Traversal {{{1
+
+class DummyNode:
+    def __init__(self, children=None, parents=None):
+        self.children = children if children is not None else []
+        self.parents  = parents  if parents  is not None else []
+
+def levelorder(field, node_in, nodes=None):
+    # relatively inefficient. this function can be optimized.
+    nodes = nodes if nodes is not None else []
+    q = [node_in]
+    while len(q) > 0:
+        node = q.pop(0)
+        nodes.append(node)
+        for child in getattr(node, field):
+            q.append(child)
+    return nodes
+
+def traverse(node_in, node_out, nodes):
+    nodes = nodes if nodes is not None else []
+    down = levelorder('children', node_in)
+    up   = levelorder('parents', node_out)
+    seen = {}
+    for node in up:
+        seen[node] = seen.get(node, 0) | 1
+    for node in down:
+        seen[node] = seen.get(node, 0) | 2
+        if seen[node] == 3:
+            nodes.append(node)
+    return nodes
+
+def traverse_all(nodes_in, nodes_out, nodes=None):
+    all_in = DummyNode()
+    all_out = DummyNode()
+    for node in nodes_in:  all_in.children.append(node)
+    for node in nodes_out: all_out.parents.append(node)
+    return traverse(all_in, all_out, nodes)
+
 # Initializations {{{1
 
 # note: these are currently only implemented for 2D shapes.
@@ -716,23 +754,30 @@ class Dense(Layer):
 # Models {{{1
 
 class Model:
-    def __init__(self, x, y, unsafe=False):
+    def __init__(self, nodes_in, nodes_out, unsafe=False):
-        assert isinstance(x, Layer), x
+        nodes_in  = [nodes_in]  if isinstance(nodes_in,  Layer) else nodes_in
-        assert isinstance(y, Layer), y
+        nodes_out = [nodes_out] if isinstance(nodes_out, Layer) else nodes_out
-        self.x = x
+        assert type(nodes_in)  == list, type(nodes_in)
-        self.y = y
+        assert type(nodes_out) == list, type(nodes_out)
-        self.ordered_nodes = self.traverse([], self.y)
+        self.nodes_in = nodes_in
+        self.nodes_out = nodes_out
+        self.nodes = traverse_all(self.nodes_in, self.nodes_out)
         self.make_weights()
-        for node in self.ordered_nodes:
+        for node in self.nodes:
             node.unsafe = unsafe
 
+    @property
+    def ordered_nodes(self):
+        # deprecated? we don't guarantee an order like we did before.
+        return self.nodes
+
     def make_weights(self):
-        self.param_count = sum((node.size for node in self.ordered_nodes))
+        self.param_count = sum((node.size for node in self.nodes))
         self.W  = np.zeros(self.param_count, dtype=_f)
         self.dW = np.zeros(self.param_count, dtype=_f)
 
         offset = 0
-        for node in self.ordered_nodes:
+        for node in self.nodes:
             if node.size > 0:
                 inner_offset = 0
 
@@ -752,39 +797,26 @@ class Model:
                 assert inner_offset >= node.size, "Layer {} allocated less weights than it said it would".format(node)
                 offset += node.size
 
-    def traverse(self, nodes, node):
-        if node == self.x:
-            return [node]
-        for parent in node.parents:
-            if parent not in nodes:
-                new_nodes = self.traverse(nodes, parent)
-                for new_node in new_nodes:
-                    if new_node not in nodes:
-                        nodes.append(new_node)
-        if nodes:
-            nodes.append(node)
-        return nodes
-
     def forward(self, X, deterministic=False):
         values = dict()
-        input_node = self.ordered_nodes[0]
+        input_node = self.nodes[0]
-        output_node = self.ordered_nodes[-1]
+        output_node = self.nodes[-1]
         values[input_node] = input_node._propagate(np.expand_dims(X, 0), deterministic)
-        for node in self.ordered_nodes[1:]:
+        for node in self.nodes[1:]:
             values[node] = node.propagate(values, deterministic)
         return values[output_node]
 
     def backward(self, error):
         values = dict()
-        output_node = self.ordered_nodes[-1]
+        output_node = self.nodes[-1]
         values[output_node] = output_node._backpropagate(np.expand_dims(error, 0))
-        for node in reversed(self.ordered_nodes[:-1]):
+        for node in reversed(self.nodes[:-1]):
             values[node] = node.backpropagate(values)
         return self.dW
 
     def regulate_forward(self):
         loss = _0
-        for node in self.ordered_nodes:
+        for node in self.nodes:
             if node.loss is not None:
                 loss += node.loss
             for k, w in node.weights.items():
@@ -792,7 +824,7 @@ class Model:
         return loss
 
     def regulate(self):
-        for node in self.ordered_nodes:
+        for node in self.nodes:
             for k, w in node.weights.items():
                 w.update()
 
@@ -812,7 +844,7 @@ class Model:
         for k in weights.keys():
             used[k] = False
 
-        nodes = [node for node in self.ordered_nodes if node.size > 0]
+        nodes = [node for node in self.nodes if node.size > 0]
         for node in nodes:
             full_name = str(node).lower()
             for s_name, o_name in node.serialized.items():
@@ -833,7 +865,7 @@ class Model:
 
         counts = defaultdict(lambda: 0)
 
-        nodes = [node for node in self.ordered_nodes if node.size > 0]
+        nodes = [node for node in self.nodes if node.size > 0]
         for node in nodes:
             full_name = str(node).lower()
             grp = f.create_group(full_name)