new = [] for pred in preds: new.append( np.eye(args["num_classes"])[pred].tolist() ) return new else: if "expectation" in args: new = [] for pred in preds: label = int( round(expectation(pred)) ) new.append( np.eye(args["num_classes"])[label].tolist() ) 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")
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 b*bs >= len(filenames): break X_train_batch = get_batch(filenames, b*bs, (b+1)*bs) #print (X_train_batch.shape) #sys.stderr.write(" Batch #%i (%i-%i)\n" % ((b+1), (b*bs), ((b+1)*bs) )) loss, loss_flat = iter_train( X_train_batch ) batch_train_losses.append(loss) print (loss, loss_flat, sum(loss_flat > 0)) helper.plot_conv_activity( symbols.conv_layer, X_train_batch[1:2] ) sys.stderr.write( " train_loss = %f\n" % \ (np.mean(batch_train_losses)) ) current_weights = lasagne.layers.get_all_param_values(symbols.output_layer) return (print_network(symbols.output_layer), current_weights) if __name__ == '__main__': f = ArffToArgs() f.set_input("../mnist/mnist.meta.arff") args = f.get_args() f.close() args["lambda"] = 0 args["alpha"] = 0.1 args["epochs"] = 10 args["dir"] = "../mnist/data" weights = train(args)
from pyscript.pyscript import ArffToArgs # test saving the pkl to an output file f = ArffToArgs() f.set_input("../datasets/iris.arff") args = f.get_args() f.save("iris.pkl.gz") f.close() # test normal f = ArffToArgs() f.set_input("../datasets/iris.arff") args = f.get_args() print f.output f.close()
for pred in preds: new.append(np.eye(args["num_classes"])[pred].tolist()) return new else: if "expectation" in args: new = [] for pred in preds: label = int(round(expectation(pred))) new.append(np.eye(args["num_classes"])[label].tolist()) 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" )
def get_args(filename, sd, bn, im): x = ArffToArgs() x.set_input(filename) x.set_class_index("last") x.set_impute(im) x.set_binarize(bn) x.set_standardize(sd) args = x.get_args() x.close() return args