epochs=N_EPOCHS, verbose=1, shuffle=False, callbacks=[checkpoint]) model.save('models/' + MODEL_NAME) model.save('models/' + MODEL_NAME + '.bckup') else: model = load_model('models/' + MODEL_NAME, custom_objects={'MYLSTM': MYLSTM}) # # Evaluate # In[12]: model.reset_states() score = model.evaluate(X_test, y_test, batch_size=BATCH_SIZE, verbose=1) print('Test score:', score[0]) print('Test accuracy:', score[1]) # In[13]: # Generate samples import generator as gen reload(gen) print gen.complete_sentence_stateful('if ', model, 256, char2int, int2char, BATCH_SIZE) print '*****' print gen.complete_sentence_stateful('else', model, 256, char2int, int2char, BATCH_SIZE)
from keras.models import load_model from controllers.mylstm_legacy import MYLSTM if not CACHED: for i in range(N_EPOCHS): model.reset_states() history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=1, verbose=1, shuffle=False) model.save('models/parentheses_stateful_reg_mylstm') else: model = load_model('models/parentheses_stateful_reg_mylstm', custom_objects={'MYLSTM':MYLSTM}) # 12 -------------------------------------------------------------------------- model.reset_states() score = model.evaluate(X_test, y_test, batch_size=BATCH_SIZE, verbose=1) print('Test score:', score[0]) print('Test accuracy:', score[1]) # 13 -------------------------------------------------------------------------- # Generate samples import generator as gen print gen.complete_sentence_stateful('(1(2(3(', model, 64, char2int, int2char,BATCH_SIZE) print gen.complete_sentence_stateful('((((4))', model, 64, char2int, int2char, BATCH_SIZE)