#-*- coding:utf-8 -*-
from generate_poetry import Poetry
from poetry_model import poetryModel
import tensorflow as tf
import numpy as np
if __name__ == '__main__':
batch_size = 50
epoch = 20
rnn_size = 128
num_layers = 2
poetrys = Poetry()
words_size = len(poetrys.word_to_id)
inputs = tf.placeholder(tf.int32, [batch_size, None])
targets = tf.placeholder(tf.int32, [batch_size, None])
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
model = poetryModel()
logits, probs, initial_state, last_state = model.create_model(
inputs, batch_size, rnn_size, words_size, num_layers, True, keep_prob)
loss = model.loss_model(words_size, targets, logits)
learning_rate = tf.Variable(0.0, trainable=False)
optimizer = model.optimizer_model(loss, learning_rate)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.assign(learning_rate, 0.002 * 0.97))
next_state = sess.run(initial_state)
step = 0
while True:
x_batch, y_batch = poetrys.next_batch(batch_size)
feed = {
#-*- coding:utf-8 -*-
from generate_poetry import Poetry
from poetry_model import poetryModel
from operator import itemgetter
import tensorflow as tf
import numpy as np
import random
if __name__ == '__main__':
batch_size = 1
rnn_size = 128
num_layers = 2
poetrys = Poetry()
words_size = len(poetrys.word_to_id)
def to_word(prob):
prob = prob[0]
indexs, _ = zip(*sorted(enumerate(prob), key=itemgetter(1)))
rand_num = int(np.random.rand(1) * 10)
index_sum = len(indexs)
max_rate = prob[indexs[(index_sum - 1)]]
if max_rate > 0.9:
sample = indexs[(index_sum - 1)]
else:
sample = indexs[(index_sum - 1 - rand_num)]
return poetrys.id_to_word[sample]
inputs = tf.placeholder(tf.int32, [batch_size, None])
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
model = poetryModel()
logits, probs, initial_state, last_state = model.create_model(