1.在最后一行添加以下代码PTBModel:__init__()功能：
self._output_probs = tf.nn.softmax(logits)
2.添加如下功能PTBModel:
@property
def output_probs(self):
return self._output_probs
3.尝试运行以下代码：
raw_data = reader.ptb_raw_data(FLAGS.data_path)
train_data, valid_data, test_data, vocabulary, word_to_id, id_to_word = raw_data
eval_config = get_config()
eval_config.batch_size = 1
eval_config.num_steps = 1
sess = tf.Session()
initializer = tf.random_uniform_initializer(-eval_config.init_scale,
eval_config.init_scale)
with tf.variable_scope("model", reuse=None, initializer=initializer):
mtest = PTBModel(is_training=False, config=eval_config)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('/home/medium') # __YOUR__MODEL__SAVE__PATH__
if ckpt and gfile.Exists(ckpt.model_checkpoint_path):
msg = 'Reading model parameters from %s' % ckpt.model_checkpoint_path
print(msg)
saver.restore(sess, ckpt.model_checkpoint_path)
def pick_from_weight(weight, pows=1.0):
weight = weight**pows
t = np.cumsum(weight)
s = np.sum(weight)
return int(np.searchsorted(t, np.random.rand(1) * s))
while True:
number_of_sentences = 10 # generate 10 sentences one time
sentence_cnt = 0
text = '\n'
end_of_sentence_char = word_to_id['<eos>']
input_char = np.array([[end_of_sentence_char]])
state = sess.run(mtest.initial_state)
while sentence_cnt < number_of_sentences:
feed_dict = {mtest.input_data: input_char,
mtest.initial_state: state}
probs, state = sess.run([mtest.output_probs, mtest.final_state],
feed_dict=feed_dict)
sampled_char = pick_from_weight(probs[0])
if sampled_char == end_of_sentence_char:
text += '.\n'
sentence_cnt += 1
else:
text += ' ' + id_to_word[sampled_char]
input_char = np.array([[sampled_char]])
print(text)
raw_input('press any key to continue ...')
