零：训练模型从上下文判断下一个单词的选择范围（perplextity），选择范围越小，模型越好
一：训练，验证，测试数据下载
从Tomas Mikolov 网站上的PTB数据下载
二：此次所用的文件在simple-examples.tgz解压后的data文件夹里：ptb.test.text,train.txt,valid三个文件
如图
三：代码
# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
import reader
DATA_PATH = 'F:/PycharmProjects/tmp/model/data'
HIDDEN_SIZE = 200 #隐藏层规模
NUM_LAYERS = 2
VOCAB_SIZE = 10000 #词典规模
LEARNING_RATE = 1.0
TRAIN_BATCH_SIZE = 20
TRAIN_NUM_STEP = 35 #训练数据截断长度
EVAL_BATCH_SIZE = 1
EVAL_NUM_STEP = 1
NUM_EPOCH = 2 #使用训练数据的轮数
KEEP_PROB = 0.5 #节点不被dropout的概率
MAX_GARD_NORM = 5 #用于控制梯度膨胀的参数
class PTBModel(object):
def __init__(self, is_training, batch_size, num_steps):
self.batch_size = batch_size
self.num_steps = num_steps
self.input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
self.targets = tf.placeholder(tf.int32, [batch_size, num_steps])
#定义使用LSTM结构为循环体结构且使用dropout的深层循环神经网络
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE)
if is_training:
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(
lstm_cell, output_keep_prob=KEEP_PROB
)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * NUM_LAYERS)
self.initial_state = cell.zero_state(batch_size, tf.float32)
embedding = tf.get_variable('embedding', [VOCAB_SIZE, HIDDEN_SIZE])
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
if is_training:
inputs = tf.nn.dropout(inputs, KEEP_PROB)
outputs = []
state = self.initial_state
with tf.variable_scope('RNN'):
for time_step in range(num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
cell_output, state = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)
output = tf.reshape(tf.concat(outputs, 1), [-1, HIDDEN_SIZE])
weight = tf.get_variable('weight', [HIDDEN_SIZE, VOCAB_SIZE])
bias = tf.get_variable('bias', [VOCAB_SIZE])
logits = tf.matmul(output, weight) + bias
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([batch_size * num_steps], dtype=tf.float32)]
)
self.cost = tf.reduce_sum(loss) / batch_size
self.final_state = state
if not is_training: return #只在训练模型时进行反向传播操作
trainable_variables = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, trainable_variables), MAX_GARD_NORM)
optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE)
self.train_op = optimizer.apply_gradients(zip(grads, trainable_variables))
def run_epoch(session, model, data, train_op, output_log):
total_costs = 0.0
iters = 0
state = session.run(model.initial_state)
for step, (x, y) in enumerate(reader.ptb_iterator(data, model.batch_size, model.num_steps)):
cost, state, _ = session.run(
[model.cost, model.final_state, train_op],
{model.input_data: x, model.targets: y,
model.initial_state: state}
)
total_costs += cost
iters += model.num_steps
if output_log and step % 100 == 0:
print('After %d steps, perplexity is %.3f' % (step, np.exp(total_costs / iters)))
return np.exp(total_costs / iters)
def main(_):
train_data, valid_data, test_data, _ = reader.ptb_raw_data(DATA_PATH)
initializer = tf.random_uniform_initializer(-0.05, 0.05)
with tf.variable_scope('language_model', reuse=tf.AUTO_REUSE, initializer=initializer):
train_model = PTBModel(True, TRAIN_BATCH_SIZE, TRAIN_NUM_STEP)
with tf.variable_scope('language_model', reuse=tf.AUTO_REUSE, initializer=initializer):
eval_model = PTBModel(True, EVAL_BATCH_SIZE, EVAL_NUM_STEP)
with tf.Session() as sess:
tf.initialize_all_variables().run()
for i in range(NUM_EPOCH):
print('In iteration: %d' % (i+1))
run_epoch(sess, train_model, train_data, train_model.train_op, True)
valid_perplexity = run_epoch(sess, eval_model, valid_data, tf.no_op(), False)
print('Epoch: %d Validation Perplexity: %.3f' % (i+1, valid_perplexity))
test_perplexity = run_epoch(sess, eval_model, test_data, tf.no_op(), False)
print('Test: Test Perplexity: %.3f' % test_perplexity)
if __name__ == '__main__':
tf.app.run()
导入的reader文件来自于：reader.把reader.py文件于主Python文件置于同一目录下。
四：训练结果
perplexity 最开始值为9982.331，这基本相当于从9982多个单词随机选择下一个单词。训练结束后选择的单词降到了236个。
In iteration: 1
After 0 steps, perplexity is 9982.331
After 100 steps, perplexity is 1373.757
After 200 steps, perplexity is 1014.514
After 300 steps, perplexity is 866.278
After 400 steps, perplexity is 765.193
After 500 steps, perplexity is 693.194
After 600 steps, perplexity is 640.376
After 700 steps, perplexity is 595.449
After 800 steps, perplexity is 555.005
After 900 steps, perplexity is 523.647
After 1000 steps, perplexity is 499.572
After 1100 steps, perplexity is 476.513
After 1200 steps, perplexity is 457.129
After 1300 steps, perplexity is 439.515
Epoch: 1 Validation Perplexity: 293.073
In iteration: 2
After 0 steps, perplexity is 378.070
After 100 steps, perplexity is 265.294
After 200 steps, perplexity is 270.458
After 300 steps, perplexity is 271.947
After 400 steps, perplexity is 268.950
After 500 steps, perplexity is 266.594
After 600 steps, perplexity is 266.106
After 700 steps, perplexity is 263.804
After 800 steps, perplexity is 259.322
After 900 steps, perplexity is 256.499
After 1000 steps, perplexity is 254.953
After 1100 steps, perplexity is 251.484
After 1200 steps, perplexity is 248.905
After 1300 steps, perplexity is 246.169
Epoch: 2 Validation Perplexity: 243.809
Test: Test Perplexity: 236.948
