#MPI.Abort(MPI.COMM_WORLD) break comm.send(None, dest=0, tag=EXIT) if __name__ == "__main__": comm=MPI.COMM_WORLD size=comm.size rank=comm.rank status=MPI.Status() READY, START, DONE, EXIT = 0, 1, 2, 3 smile_old=zinc_data_with_bracket_original() val,smile=zinc_processed_with_bracket(smile_old) print val val2=['C', '(', ')', 'c', '1', '2', 'o', '=', 'O', 'N', '3', 'F', '[C@@H]', 'n', '-', '#', 'S', 'Cl', '[O-]', '[C@H]', '[NH+]', '[C@]', 's', 'Br', '/', '[nH]', '[NH3+]', '4', '[NH2+]', '[C@@]', '[N+]', '[nH+]', '\\', '[S@]', '5', '[N-]', '[n+]', '[S@@]', '[S-]', '6', '7', 'I', '[n-]', 'P', '[OH+]', '[NH-]', '[P@@H]', '[P@@]', '[PH2]', '[P@]', '[P+]', '[S+]', '[o+]', '[CH2-]', '[CH-]', '[SH+]', '[O+]', '[s+]', '[PH+]', '[PH]', '8', '[S@@+]'] logP_values = np.loadtxt('logP_values.txt') SA_scores = np.loadtxt('SA_scores.txt') cycle_scores = np.loadtxt('cycle_scores.txt') SA_mean = np.mean(SA_scores) #print len(SA_scores) SA_std=np.std(SA_scores) logP_mean = np.mean(logP_values) logP_std= np.std(logP_values) cycle_mean = np.mean(cycle_scores) cycle_std=np.std(cycle_scores)
#return new_sentence def save_model(model): # serialize model to JSON model_json = model.to_json() with open("model.json", "w") as json_file: json_file.write(model_json) # serialize weights to HDF5 model.save_weights("model.h5") print("Saved model to disk") if __name__ == "__main__": smile=zinc_data_with_bracket_original() valcabulary,all_smile=zinc_processed_with_bracket(smile) print(valcabulary) print(len(all_smile)) X_train,y_train=prepare_data(valcabulary,all_smile) maxlen=81 X= sequence.pad_sequences(X_train, maxlen=81, dtype='int32', padding='post', truncating='pre', value=0.) y = sequence.pad_sequences(y_train, maxlen=81, dtype='int32', padding='post', truncating='pre', value=0.) y_train_one_hot = np.array([to_categorical(sent_label, num_classes=len(valcabulary)) for sent_label in y]) print (y_train_one_hot.shape)