update makefile

Makefile

@@ -36,13 +36,15 @@ endif
 all: $(EXAMPLE) $(TEST)
 
 $(EXAMPLE): $(BINDIR)/%: $(BUILDDIR)/example/%.o $(OBJ)
+	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) $(LIB) $< $(OBJ) -o $@
 
 $(EXAMPLEOBJ): $(BUILDDIR)/example/%.o: $(EXAMPLEDIR)/%.cc
 	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) $(INC) -c $< -o $@
 
 $(TEST): $(BINDIR)/%: $(BUILDDIR)/test/%.o $(OBJ)
+	@mkdir -p $(@D)
 	$(CXX) $(CFLAGS) $(LIB) $< $(OBJ) -o $@
 
 $(TESTOBJ): $(BUILDDIR)/test/%.o: $(TESTDIR)/%.cc

README.md

@@ -0,0 +1,12 @@
+#Neural Network Library#
+
+This library is a research of implementing ultimately generic neural network without performance overhead.
+
+##Interesting Features:
+  * Network inside network: every network is considered as a filter and could be used to contruct more complicated networks;
+  * Network sharing and cloning: sub-networks could share paramenters and are clonable;
+  * In place memory optimization by default: one neuron could accept signals from several other neurons with just one copy of n-dim array memory;
+  * Dynamic traning: it's able to train only part of the whole network (e.g. RNN with varied input lenght); it's able to fix part of the whole network;
+  * Dynamic network [WIP]: fast dynamic network construction, optimization with cache.
+
+The guiding principles of design includes both efficiency and convenience. For user guide, please have a look at the example fold. The library uses extensively new features of C++11 to make the code simple and clear. Using Galois is just as simple as drawing dataflow graphs. Galois is also efficient. For the same mnist_mlp model (from torch demos) on Mac Pro 2013, the consumed time of each epoch is: Torch ~ 40s; Keras ~ 60s; Galois ~ 30s. Only implemented for CPU for the moment.