def trainprocess(): sym = txt.get() if sym != "": TR.process(sym, 4) tm.showinfo("Input", "Training Successfully Finished") else: tm.showinfo("Input error", "Select Dataset")
def get_sql(): df = pd.read_sql('SELECT * FROM stock_table', con=db_connection) df.drop(columns=['index'], inplace=True) print(df.columns) l = process(df, db_connection) # print("ppppppppppppppppppp") return l
nparams["training"]["loss_func"] = 'cosine' nparams["training"]["optimizer"] = 'adam' nparams["training"]["normalize_y"] = True nparams["cnn"]["architecture"] = '33' nparams["cnn"]["n_dense"] = 0 nparams["cnn"]["dropout_factor"] = 0.7 nparams["cnn"]["final_activation"] = 'linear' nparams["dataset"]["nsamples"] = 'all' nparams["dataset"]["dataset"] = 'MuMu-albums' nparams["dataset"]["meta-suffix"] = meta_suffix nparams["dataset"]["meta-suffix2"] = meta_suffix2 nparams["dataset"]["meta-suffix3"] = meta_suffix3 add_extra_params(nparams, extra_params) params['cosine_multilabel_tri'] = copy.deepcopy(nparams) return params[suffix] if __name__ == '__main__': parser = argparse.ArgumentParser( description='Run experiment', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('suffix', default="class_bow", help='Suffix of experiment params') parser.add_argument('meta_suffix', nargs='?', default="", help='Suffix of input matrix for experiment') parser.add_argument('meta_suffix2', nargs='?', default="", help='Suffix of input matrix for experiment') parser.add_argument('meta_suffix3', nargs='?', default="", help='Suffix of input matrix for experiment') parser.add_argument('extra_params', nargs='?', default="", help='Specific extra parameters') args = parser.parse_args() print args.extra_params params = get_configuration(args.suffix,args.meta_suffix,args.meta_suffix2,args.meta_suffix3,args.extra_params) process(params)
model = models.model_from_json(f.read()) model.load_weights(wfile) return model if __name__ == '__main__': model = load_model() words, embeddings = load_data() word_len = model.input_shape[1] word = raw_input('Enter a word [enter nothing to exit]: ') while word: try: vector = model.predict( process(word, word_len).reshape(1, word_len))[0] nn_words, nn_dists = rank_nearest_neighbors( vector, words, embeddings) print('Closest words to "%s":' % word) s = len(words) - 4 for i, (word, dist) in enumerate(zip(nn_words[:5], nn_dists[:5]), 1): print(' %d. "%s" [%.3f]' % (i, word, dist)) print(' ...') for i, (word, dist) in enumerate(zip(nn_words[-5:], nn_dists[-5:]), s): print(' %d. "%s" [%.3f]' % (i, word, dist)) except ValueError, e: print(e) word = raw_input('Enter another word [enter nothing to exit]: ')
# optimizer and criterion optimizer = optimizer_dict[optimizer_name](model.parameters(), **optimizer_options) criterion = nn.CrossEntropyLoss() criterion = criterion.to(device) # initialize data ds_trn, ds_val = imagenet_1k() dl_trn = DataLoader(ds_trn, batch_size, shuffle=True, num_workers=4, pin_memory=True) dl_val = DataLoader(ds_val, batch_size, num_workers=4, pin_memory=True) # release unused objects del ds_trn, ds_val del parser, args, optimizer_name, optimizer_options, optimizer_dict # obtain best accuracy from checkpoint _, best_acc1, _ = process(dl_val, model, criterion, None, mode='eval', device=device, progress=True) print(f" *** Starting Acc@1 {best_acc1:.4f}") set_all_rng_seed(2019) for epoch in range(epochs): l, t1, t5 = process(dl_trn, model, criterion, optimizer, mode='train') print(f" * Train Loss {l:.4f} Acc@1 {t1:.4f} Acc@5 {t5:.4f}") l, t1, t5 = process(dl_trn, model, criterion, None, mode='eval') print(f" ** Test Loss {l:.4f} Acc@1 {t1:.4f} Acc@5 {t5:.4f}") is_best = t1 > best_acc1 best_acc1 = max(t1, best_acc1) checkpoint_filename = f'checkpoint-epoch-{epoch}.pt' torch.save(model.state_dict(), checkpoint_filename) if is_best: shutil.copyfile(checkpoint_filename, 'model_best.pt')