bs = args["batch_size"] else: bs = 128 X_test = args["X_test"] preds = iter_test(X_test).tolist() new = [] for pred in preds: new.append(np.eye(args["num_classes"])[pred].tolist()) return new if __name__ == '__main__': x = ArffToArgs() x.set_input("data/cpu_act.arff") x.set_class_index("last") x.set_impute(True) x.set_binarize(True) x.set_standardize(True) x.set_arguments( "adaptive=True;alpha=0.01;lambda=0;epochs=500;rmsprop=True") args = x.get_args() #args["debug"] = True args["X_test"] = np.asarray(args["X_train"], dtype="float32") model = train(args) test(args, model)
return new else: return preds if __name__ == '__main__': x = ArffToArgs() #x.set_input("data/auto_price.arff") if len(sys.argv) != 3: sys.argv.append("data/2dplanes.arff") sys.argv.append("kappa") x.set_input( sys.argv[1] ) print "Training on: %s" % sys.argv[1] x.set_class_index("last") x.set_impute(True) x.set_binarize(True) x.set_standardize(True) if sys.argv[2] == "kappa": #x.set_arguments("expectation=True;a=1;b=0;logistic=True;alpha=0.1;rmsprop=True;epochs=5000") x.set_arguments("expectation=True;a=1;b=0;logistic=True;alpha=0.1;schedule=500;epochs=5000") elif sys.argv[2] == "regression": #x.set_arguments("regression=True;alpha=0.1;rmsprop=True;epochs=5000") x.set_arguments("regression=True;alpha=0.1;schedule=500;epochs=5000") else: print "error!" args = x.get_args() args["debug"] = False args["X_test"] = np.asarray(args["X_train"], dtype="float32") model = train(args)
if "batch_size" in args: bs = args["batch_size"] else: bs = 128 X_test = args["X_test"] preds = iter_test(X_test).tolist() new = [] for pred in preds: new.append( np.eye(args["num_classes"])[pred].tolist() ) return new if __name__ == '__main__': x = ArffToArgs() x.set_input("data/cpu_act.arff") x.set_class_index("last") x.set_impute(True) x.set_binarize(True) x.set_standardize(True) x.set_arguments("adaptive=True;alpha=0.01;lambda=0;epochs=500;rmsprop=True") args = x.get_args() #args["debug"] = True args["X_test"] = np.asarray(args["X_train"], dtype="float32") model = train(args) test(args, model)
if __name__ == '__main__': x = ArffToArgs() #x.set_input("data/auto_price.arff") if len(sys.argv) != 3: sys.argv.append("data/2dplanes.arff") sys.argv.append("kappa") x.set_input(sys.argv[1]) print "Training on: %s" % sys.argv[1] x.set_class_index("last") x.set_impute(True) x.set_binarize(True) x.set_standardize(True) if sys.argv[2] == "kappa": #x.set_arguments("expectation=True;a=1;b=0;logistic=True;alpha=0.1;rmsprop=True;epochs=5000") x.set_arguments( "expectation=True;a=1;b=0;logistic=True;alpha=0.1;schedule=500;epochs=5000" ) elif sys.argv[2] == "regression": #x.set_arguments("regression=True;alpha=0.1;rmsprop=True;epochs=5000") x.set_arguments("regression=True;alpha=0.1;schedule=500;epochs=5000") else: print "error!" args = x.get_args() args["debug"] = False args["X_test"] = np.asarray(args["X_train"], dtype="float32") model = train(args)