From 5e06190acc9eb9e1e992c805d70290fd35cbd8e2 Mon Sep 17 00:00:00 2001
From: Connor Olding <cloningdonor@gmail.com>
Date: Tue, 10 Jan 2017 19:06:42 -0800
Subject: [PATCH] .

---
 optim_nn.py | 35 +++++++++++++++++++++++------------
 1 file changed, 23 insertions(+), 12 deletions(-)

diff --git a/optim_nn.py b/optim_nn.py
index 0ac409a..edc42dd 100644
--- a/optim_nn.py
+++ b/optim_nn.py
@@ -123,6 +123,7 @@ class Layer:
         _layer_counters[kind] += 1
         self.name = "{}_{}".format(kind, _layer_counters[kind])
         self.size = None # total weight count (if any)
+        self.unsafe = False # aka gotta go fast mode
 
     def __str__(self):
         return self.name
@@ -142,7 +143,8 @@ class Layer:
         self.parents.append(parent)
 
     def make_shape(self, shape):
-        assert shape is not None
+        if not self.unsafe:
+            assert shape is not None
         if self.output_shape is None:
             self.output_shape = shape
         return shape
@@ -150,7 +152,8 @@ class Layer:
     # TODO: rename this multi and B crap to something actually relevant.
 
     def multi(self, B):
-        assert len(B) == 1, self
+        if not self.unsafe:
+            assert len(B) == 1, self
         return self.F(B[0])
 
     def dmulti(self, dB):
@@ -194,27 +197,33 @@ class Layer:
         assert Y.shape[1:] == self.output_shape, (str(self), Y.shape[1:], self.output_shape)
 
     def forward(self, lut):
-        assert len(self.parents) > 0, self
+        if not self.unsafe:
+            assert len(self.parents) > 0, self
         B = []
         for parent in self.parents:
             # TODO: skip over irrelevant nodes (if any)
             X = lut[parent]
-            self.validate_input(X)
+            if not self.unsafe:
+                self.validate_input(X)
             B.append(X)
         Y = self.multi(B)
-        self.validate_output(Y)
+        if not self.unsafe:
+            self.validate_output(Y)
         return Y
 
     def backward(self, lut):
-        assert len(self.children) > 0, self
+        if not self.unsafe:
+            assert len(self.children) > 0, self
         dB = []
         for child in self.children:
             # TODO: skip over irrelevant nodes (if any)
             dY = lut[child]
-            self.validate_output(dY)
+            if not self.unsafe:
+                self.validate_output(dY)
             dB.append(dY)
         dX = self.dmulti(dB)
-        self.validate_input(dX)
+        if not self.unsafe:
+            self.validate_input(dX)
         return dX
 
 # Final Layers
@@ -337,13 +346,15 @@ class Dense(Layer):
 # Model
 
 class Model:
-    def __init__(self, x, y):
+    def __init__(self, x, y, unsafe=False):
         assert isinstance(x, Layer), x
         assert isinstance(y, Layer), y
         self.x = x
         self.y = y
         self.ordered_nodes = self.traverse([], self.y)
         self.make_weights()
+        for node in self.ordered_nodes:
+            node.unsafe = unsafe
 
     def make_weights(self):
         self.param_count = 0
@@ -502,7 +513,7 @@ if __name__ == '__main__':
     if last_size != output_samples:
         y = y.feed(Dense(output_samples))
 
-    model = Model(x, y)
+    model = Model(x, y, unsafe=False)
 
     node_names = ' '.join([str(node) for node in model.ordered_nodes])
     log('{} nodes'.format(len(model.ordered_nodes)), node_names)
@@ -558,13 +569,13 @@ if __name__ == '__main__':
         if i > 0:
             log("restarting", i)
             LR *= LRprod**config.LR_restart_advance
-            #optim.reset()
+            optim.reset()
 
         assert inputs.shape[0] % config.batch_size == 0, \
                "inputs is not evenly divisible by batch_size" # TODO: lift this restriction
         batch_count = inputs.shape[0] // config.batch_size
         for e in range(config.epochs):
-            indices = np.arange(len(inputs))
+            indices = np.arange(inputs.shape[0])
             np.random.shuffle(indices)
             shuffled_inputs = inputs[indices] / x_scale
             shuffled_outputs = outputs[indices] / y_scale