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)