class evaluator:
def __init__(self, name, vocabulary, init, extract, layer):
self.opt = pickle.load(open('{}.pkl'.format(name), "rb"))
self.opt.batch_size = 1
self.opt.seq_length = 1
self.reader = Vocabulary(self.opt.tokens, vocabulary, max_word_l=self.opt.max_word_l)
if extract:
self.model = LSTMCNN_print(self.opt, extract, layer)
print self.model.summary()
self.model.load_weights('{}.h5'.format(name), by_name=True)
else:
self.model = LSTMCNN(self.opt)
self.model.load_weights('{}.h5'.format(name))
print self.model.summary()
if init:
self.state_mean = np.load(init)
else:
self.state_mean = None
def logprob(self, line):
x, y = self.reader.get_input(line)
nwords = len(y)
if self.state_mean is not None:
self.model.set_states_value(self.state_mean)
return self.model.evaluate(x, y, batch_size=1, verbose=0), nwords
def get_embedding(self, line):
x, y = self.reader.get_input(line)
if self.state_mean is not None:
self.model.set_states_value(self.state_mean)
return self.model.predict(x, batch_size=1, verbose=0)
class evaluator:
def __init__(self, name, vocabulary, init):
self.opt = pickle.load(open('{}.pkl'.format(name), "rb"))
self.opt.batch_size = 1
self.opt.seq_length = 1
self.reader = Vocabulary(self.opt.tokens, vocabulary, max_word_l=self.opt.max_word_l)
self.model = LSTMCNN(self.opt)
self.model.load_weights('{}.h5'.format(name))
if init:
self.state_mean = np.load(init)
else:
self.state_mean = None
def logprob(self, line):
x, y = self.reader.get_input(line)
nwords = len(y)
if self.state_mean is not None:
self.model.set_states_value(self.state_mean)
return self.model.evaluate(x, y, batch_size=1, verbose=0), nwords
class evaluator:
def __init__(self, name, vocabulary, init):
self.opt = pickle.load(open('{}.pkl'.format(name), "rb"))
self.opt.batch_size = 1
self.opt.seq_length = 1
self.reader = Vocabulary(self.opt.tokens,
vocabulary,
max_word_l=self.opt.max_word_l)
self.model = LSTMCNN(self.opt)
self.model.load_weights('{}.h5'.format(name))
if init:
self.state_mean = np.load(init)
else:
self.state_mean = None
def logprob(self, line):
x, y = self.reader.get_input(line)
nwords = len(y)
if self.state_mean is not None:
self.model.set_states_value(self.state_mean)
return self.model.evaluate(x, y, batch_size=1, verbose=0), nwords