if __name__ == '__main__': modelpath = '' #### .pkl file ########## parameter_path = '' #### .para file ############# parameterlist = cPickle.load(open(parameter_path, 'rb')) traindatapath = './data' print 'loading dataset.. ', if not os.path.isfile(traindatapath + '/test.p'): import dataset dataset.data2pickle(traindatapath + '/test_filtered.data', traindatapath + '/test.p') if not os.path.isfile(traindatapath + '/train.p'): import dataset dataset.data2pickle(traindatapath + '/train_filtered.data', traindatapath + '/train.p') testData = cPickle.load(open(traindatapath + '/test.p')) trainData = cPickle.load(open(traindatapath + '/train.p')) # testData = testData[1:5] # trainData = trainData[1:15] tmp = traindatapath.split('_') test = data2cv.make_idx_data_cv(testData, parameterlist['filter_size'], int(parameterlist['max_sentence_word'])) train = data2cv.make_idx_data_cv(trainData, parameterlist['filter_size'], int(parameterlist['max_sentence_word']))
modelpath = runpath + 'PCNN_C2SA.pkl' traindatapath = './data' print 'model save in: ', modelpath # save_log = modelpath + '1.pkl\n' # log_file = open(log_path, 'a') # log_file.write(save_log) # log_file.close() print 'loading dataset.. ' if not os.path.isfile(traindatapath + '/test_57w.p'): import dataset dataset.data2pickle(traindatapath + '/test_data.txt', traindatapath + '/test_57w.p') if not os.path.isfile(traindatapath + '/train_57w.p'): import dataset dataset.data2pickle(traindatapath + '/train_data.txt', traindatapath + '/train_57w.p') if not os.path.isfile(traindatapath + '/wv.p'): import dataset dataset.wv2pickle('./data/wv.txt', 50, './data/wv.p') testData = cPickle.load(open(traindatapath + '/test_57w.p')) trainData = cPickle.load(open(traindatapath + '/train_57w.p')) # testData = testData[1:5] # trainData = trainData[1:15] tmp = traindatapath.split('_')
if not os.path.isfile(inputdir+'/'+str(dimension)+'/Wv.p'): import dataset dataset.wv2pickle(inputdir+'/'+str(dimension)+'/wv.txt', dimension, inputdir+'/'+str(dimension)+'/Wv.p') resultdir = './e_'+str(epochs)+'_s_'+str(static)+'_u_'+\ hidden_units_str+'_b_'+str(batch_size)+'_w_'+\ str(window_size)+'_c_'+conv_non_linear+'_d_'+\ str(dimension)+'_i_'+inputdir+'_n_'+str(norm)+'/' print 'result dir='+resultdir if not os.path.exists(resultdir): os.mkdir(resultdir) if not os.path.isfile(inputdir+'/test.p'): import dataset dataset.data2pickle(inputdir+'/test_filtered.data', inputdir+'/test.p') if not os.path.isfile(inputdir+'/train.p'): import dataset dataset.data2pickle(inputdir+'/train_filtered.data', inputdir+'/train.p') testData = cPickle.load(open(inputdir+'/test.p')) trainData = cPickle.load(open(inputdir+'/train.p')) # testData = testData[1:5] # trainData = trainData[1:15] tmp = inputdir.split('_') test = data2cv.make_idx_data_cv(testData, window_size, int(tmp[3])) train = data2cv.make_idx_data_cv(trainData, window_size, int(tmp[3])) print 'load Wv ...' Wv = cPickle.load(open(inputdir+'/'+str(dimension)+'/Wv.p'))