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GeezerVison is showing github.com — DeepLearningTutorials/code/test.py at master · dchichkov/DeepLearningTutorials Expand file tree/ Copy pathtest.pyMore file actions 152 lines (119 loc) · 6.08 KB / Copy pathtest.pyFile metadata and controls152 lines (119 loc) · 6.08 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 import convolutional_mlp, logistic_cg, logistic_sgd, mlp, SdA, dA, rbm , DBN from nose.plugins.skip import SkipTest import numpy, theano import time, sys def test_logistic_sgd(): logistic_sgd.sgd_optimization_mnist(n_epochs=10) def test_logistic_cg(): logistic_cg.cg_optimization_mnist(n_epochs=10) def test_mlp(): mlp.test_mlp(n_epochs=5) def test_convolutional_mlp(): convolutional_mlp.evaluate_lenet5(n_epochs=5,nkerns=[5,5]) def test_dA(): dA.test_dA(training_epochs = 3, output_folder = 'tmp_dA_plots') def test_SdA(): SdA.test_SdA(pretraining_epochs = 2, training_epochs = 3, batch_size = 300) def test_dbn(): DBN.test_DBN(pretraining_epochs = 1, training_epochs = 2, batch_size =300) def test_rbm(): rbm.test_rbm(training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1, output_folder = 'tmp_rbm_plots') def speed(): """ This fonction modify the configuration theano and don't restore it! I want it to be compatible with python2.4 so using try: finaly: is not an option. """ import theano algo=['logistic_sgd','logistic_cg','mlp','convolutional_mlp','dA','SdA','DBN','rbm'] to_exec=[True]*len(algo) # to_exec=[False]*len(algo) # to_exec[-1]=True expected_times_64=numpy.asarray([ 12.42313051, 28.09523582, 106.35365391, 116.79225969, 153.12310314, 425.09175086, 642.72824597, 652.52828193]) expected_times_32=numpy.asarray([ 13.29699826, 32.42813158, 68.03559947, 105.54640913, 107.00527334, 242.41721797, 490.40798998, 528.88854146]) expected_times_gpu=numpy.asarray([ 3.07663488, 7.55523491, 18.99226785, 9.58915591, 24.13007045, 24.77524018, 92.66246653, 322.34032917]) def time_test(m,l,idx,f,**kwargs): if not to_exec[idx]: l[idx]=float('nan') return print algo[idx] ts=m.call_time try: f(**kwargs) except Exception, e: print >> sys.stderr, 'test', algo[idx], 'FAILED', e l[idx]=float('nan') return te=m.call_time l[idx]=te-ts def do_tests(): m=theano.compile.mode.get_default_mode() l=numpy.zeros(len(algo)) time_test(m,l,0, logistic_sgd.sgd_optimization_mnist,n_epochs=30) time_test(m,l,1, logistic_cg.cg_optimization_mnist,n_epochs=30) time_test(m,l,2, mlp.test_mlp, n_epochs=5) time_test(m,l,3, convolutional_mlp.evaluate_lenet5, n_epochs=5,nkerns=[5,5]) time_test(m,l,4, dA.test_dA, training_epochs = 2, output_folder = 'tmp_dA_plots') time_test(m,l,5, SdA.test_SdA, pretraining_epochs = 1, training_epochs = 2, batch_size = 300) time_test(m,l,6, DBN.test_DBN, pretraining_epochs = 1, training_epochs = 2, batch_size = 300) time_test(m,l,7, rbm.test_rbm, training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1, output_folder = 'tmp_rbm_plots') return l #test in float64 in FAST_RUN mode on the cpu print >> sys.stderr, algo theano.config.floatX='float64' theano.config.mode='FAST_RUN' float64_times=numpy.zeros(len(algo)) float64_times=do_tests() print >> sys.stderr, 'float64 times',float64_times print >> sys.stderr, 'float64 expected',expected_times_64 print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times #test in float32 in FAST_RUN mode on the cpu theano.config.floatX='float32' float32_times=numpy.zeros(len(algo)) float32_times=do_tests() print >> sys.stderr, 'float32 times',float32_times print >> sys.stderr, 'float32 expected',expected_times_32 print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times print >> sys.stderr, 'float64/float32',float64_times/float32_times print >> sys.stderr print >> sys.stderr, 'Duplicate the timing to have everything in one place' print >> sys.stderr, algo print >> sys.stderr, 'float64 times',float64_times print >> sys.stderr, 'float64 expected',expected_times_64 print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times print >> sys.stderr, 'float32 times',float32_times print >> sys.stderr, 'float32 expected',expected_times_32 print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times print >> sys.stderr, 'float64/float32',float64_times/float32_times print >> sys.stderr, 'expected float64/float32',expected_times_64/float32_times #test in float64 in FAST_RUN mode on the gpu theano.config.device='gpu0' import theano.sandbox.cuda theano.sandbox.cuda.use('gpu') gpu_times=do_tests() print >> sys.stderr, 'gpu times',gpu_times print >> sys.stderr, 'gpu expected',expected_times_gpu print >> sys.stderr, 'gpu % expected/get',expected_times_gpu/gpu_times print >> sys.stderr, 'float64/gpu',float64_times/gpu_times print >> sys.stderr print >> sys.stderr, 'Duplicate the timing to have everything in one place' print >> sys.stderr, algo print >> sys.stderr, 'float64 times',float64_times print >> sys.stderr, 'float64 expected',expected_times_64 print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times print >> sys.stderr, 'float32 times',float32_times print >> sys.stderr, 'float32 expected',expected_times_32 print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times print >> sys.stderr, 'gpu times',gpu_times print >> sys.stderr, 'gpu expected',expected_times_gpu print >> sys.stderr, 'gpu % expected/get',expected_times_gpu/gpu_times print >> sys.stderr, 'float64/float32',float64_times/float32_times print >> sys.stderr, 'float64/gpu',float64_times/gpu_times print >> sys.stderr, 'float32/gpu',float32_times/gpu_times print >> sys.stderr, 'expected float64/float32',expected_times_64/float32_times print >> sys.stderr, 'expected float64/gpu',expected_times_64/gpu_times print >> sys.stderr, 'expected float32/gpu',expected_times_32/gpu_times assert not numpy.isnan(gpu_times).any() |
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