test_xor_cuda.py

#!/usr/bin/env python
#Copyright (c) 2014, Ben Goodrich
#All rights reserved.
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#Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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#1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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#2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 
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import time
import numpy as np;

#from nnet_toolkit import nnet_old as nnet
from nnet_toolkit import nnet_cuda as nnet
#from nnet_toolkit import nnet

layers = [nnet.layer(2),nnet.layer(128,'sigmoid'),nnet.layer(1,'sigmoid')];
#layers = [nnet_toolkit.layer(2),nnet_toolkit.layer(256,'linear_rectifier'),nnet_toolkit.layer(128,'linear_rectifier'),nnet_toolkit.layer(64,'linear_rectifier'),nnet_toolkit.layer(32,'linear_rectifier'),nnet_toolkit.layer(1,'squash')];

training_data = np.array([[0,0,1,1],[0,1,0,1]]);
training_out = np.array([0,1,1,0]);

net = nnet.net_cuda(layers,step_size=.1);
#net = nnet.net(layers,step_size=.1);

net.input = training_data;
t = time.time();
for i in range(100000):
	net.feed_forward();
	net.error = net.output - training_out;
	net.back_propagate();
	net.update_weights();
	if(i%1000 == 0):
		#print("iteration: " + str(i) + " " + str(net.error));
		t_delta = time.time() - t;
		print("iteration: " + str(i) + " " + str(np.sum(net.error**2)) + ' ' + str(t_delta));
		t = time.time();