# 构建计算图——LSTM模型
# embedding
# LSTM
# fc
# train_op
# 训练流程代码
# 数据集封装
# api: next_batch(batch_size)
# 词表封装:
# api: sentence2id(text_sentence): 句子转换id
# 类别的封装：
# api: category2id(text_category).
import tensorflow as tf
import os
import sys
import numpy as np
import math
tf.logging.set_verbosity(tf.logging.INFO)
print("ok1")
# 定义数据超参数
def get_default_params():
return tf.contrib.training.HParams(
num_embedding_size = 16, # 词的embedding长度
num_timesteps = 50, # lstm步长，一个句子词的个数
num_lstm_nodes = [32, 32],
num_lstm_layers = 2,
num_fc_nodes = 32,
batch_size = 100,
clip_lstm_grads = 1.0, # 梯度上限
learning_rate = 0.001,
num_word_threshold = 10, # 词频阈值
)
hps = get_default_params()
train_file = 'F:/channelE/lstm/text_classification_data/cnews.train.seg.txt'
val_file = 'F:/channelE/lstm/text_classification_data/cnews.val.seg.txt'
test_file = 'F:/channelE/lstm/text_classification_data/cnews.test.seg.txt'
vocab_file = 'F:/channelE/lstm/text_classification_data/cnews.vocab.txt'
category_file = 'F:/channelE/lstm/text_classification_data/cnews.category.txt'
output_folder = 'F:/channelE/lstm/run_text_rnn'
if not os.path.exists(output_folder):
os.mkdir(output_folder)
print("ok2")
# 词表封装类
class Vocab:
def __init__(self, filename, num_word_threshold):
self._word_to_id = {}
self._unk = -1
self._num_word_threshold = num_word_threshold
self._read_dict(filename)
def _read_dict(self, filename):
with open(filename, 'r', encoding='utf-8') as f:
lines = f.readlines()
for line in lines:
word, frequency = line.strip('\r\n').split('\t')
# word = word.decode('utf-8')
frequency = int(frequency)
if frequency < self._num_word_threshold:
continue
idx = len(self._word_to_id)
if word == '<UNK>':
self._unk = idx
self._word_to_id[word] = idx
def word_to_id(self, word):
return self._word_to_id.get(word, self._unk)
@property
def unk(self):
return self._unk
def size(self):
return len(self._word_to_id)
def sentence_to_id(self, sentence):
word_ids = [self.word_to_id(cur_word) \
for cur_word in sentence.split()]
return word_ids
# 类别封装
class CategoryDict:
def __init__(self, filename):
self._category_to_id = {}
with open(filename, 'r') as f:
lines = f.readlines()
for line in lines:
category = line.strip('\r\n')
idx = len(self._category_to_id)
self._category_to_id[category] = idx
def size(self):
return len(self._category_to_id)
def category_to_id(self, category):
if not category in self._category_to_id:
pass
# raise Execption(
# "%s is not in our category list" % category_name)
return self._category_to_id[category]
vocab = Vocab(vocab_file, hps.num_word_threshold)
vocab_size = vocab.size()
tf.logging.info('vocab_size: %d' % vocab_size)
print("vocab_size:",vocab_size) # vocab_size: 77323
category_vocab = CategoryDict(category_file)
num_classes = category_vocab.size()
tf.logging.info('num_classes: %d' % num_classes)
test_str = '时尚'
tf.logging.info(
'label: %s, id: %d' % (
test_str,
category_vocab.category_to_id(test_str)))
print("label: %s, id: %d", (test_str,category_vocab.category_to_id(test_str)))
print("ok3")
# 数据集封装
class TextDataSet:
def __init__(self, filename, vocab, category_vocab, num_timesteps):
self._vocab = vocab
self._category_vocab = category_vocab
self._num_timesteps = num_timesteps
# matrix
self._inputs = []
# vector
self._outputs = []
self._indicator = 0
self._parse_file(filename)
def _parse_file(self, filename):
'''
解析文件
:param filename:
:return:
'''
tf.logging.info('Loading data from %s', filename)
with open(filename, 'r', encoding='utf-8') as f:
lines = f.readlines()
for line in lines:
label, content = line.strip('\r\n').split('\t')
id_label = self._category_vocab.category_to_id(label)
id_words = self._vocab.sentence_to_id(content)
id_words = id_words[0: self._num_timesteps]
padding_num = self._num_timesteps - len(id_words)
id_words = id_words + [
self._vocab.unk for i in range(padding_num)]
self._inputs.append(id_words)
self._outputs.append(id_label)
self._inputs = np.asarray(self._inputs, dtype=np.int32)
self._outputs = np.asarray(self._outputs, dtype=np.int32)
self._random_shuffle()
def _random_shuffle(self):
p = np.random.permutation(len(self._inputs))
self._inputs = self._inputs[p]
self._outputs = self._outputs[p]
def next_batch(self, batch_size):
end_indicator = self._indicator + batch_size
if end_indicator > len(self._inputs):
self._random_shuffle()
self._indicator = 0
end_indicator = batch_size
if end_indicator > len(self._inputs):
raise Execption("batch_size: %d is too large" % batch_size)
batch_inputs = self._inputs[self._indicator: end_indicator]
batch_outputs = self._outputs[self._indicator: end_indicator]
self._indicator = end_indicator
return batch_inputs, batch_outputs
train_dataset = TextDataSet(
train_file, vocab, category_vocab, hps.num_timesteps)
val_dataset = TextDataSet(
val_file, vocab, category_vocab, hps.num_timesteps)
test_dataset = TextDataSet(
test_file, vocab, category_vocab, hps.num_timesteps)
print("train_dataset.__sizeof__()",train_dataset)
print("val_dataset.__sizeof__()",val_dataset.__sizeof__())
print("test_dataset.__sizeof__",test_dataset.__sizeof__())
print(train_dataset.next_batch(2))
print(val_dataset.next_batch(2))
print(test_dataset.next_batch(2))
print("ok4")
# 分类模型
def create_model(hps, vocab_size, num_classes):
num_timesteps = hps.num_timesteps
batch_size = hps.batch_size
inputs = tf.placeholder(tf.int32, (batch_size, num_timesteps))
outputs = tf.placeholder(tf.int32, (batch_size,))
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
global_step = tf.Variable(
tf.zeros([], tf.int64), name='global_step', trainable=False)
embedding_initializer = tf.random_uniform_initializer(-1.0, 1.0)
with tf.variable_scope(
'embedding', initializer=embedding_initializer):
embeddings = tf.get_variable(
'embedding',
[vocab_size, hps.num_embedding_size],
tf.float32)
# [1, 10, 7] -> [embeddings[1], embeddings[10], embeddings[7]]
embed_inputs = tf.nn.embedding_lookup(embeddings, inputs)
# LSTM
scale = 1.0 / math.sqrt(hps.num_embedding_size + hps.num_lstm_nodes[-1]) / 3.0
lstm_init = tf.random_uniform_initializer(-scale, scale)
def _generate_params_for_lstm_cell(x_size, h_size, bias_size):
"""generates parameters for pure lstm implementation."""
x_w = tf.get_variable('x_weights', x_size)
h_w = tf.get_variable('h_weights', h_size)
b = tf.get_variable('biases', bias_size,
initializer=tf.constant_initializer(0.0))
return x_w, h_w, b
with tf.variable_scope('lstm_nn', initializer=lstm_init):
"""
cells = []
for i in range(hps.num_lstm_layers):
cell = tf.contrib.rnn.BasicLSTMCell(
hps.num_lstm_nodes[i],
state_is_tuple = True)
cell = tf.contrib.rnn.DropoutWrapper(
cell,
output_keep_prob = keep_prob)
cells.append(cell)
cell = tf.contrib.rnn.MultiRNNCell(cells)
initial_state = cell.zero_state(batch_size, tf.float32)
# rnn_outputs: [batch_size, num_timesteps, lstm_outputs[-1]]
rnn_outputs, _ = tf.nn.dynamic_rnn(
cell, embed_inputs, initial_state = initial_state)
last = rnn_outputs[:, -1, :]
"""
with tf.variable_scope('inputs'):
ix, ih, ib = _generate_params_for_lstm_cell(
x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],
h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],
bias_size=[1, hps.num_lstm_nodes[0]]
)
with tf.variable_scope('outputs'):
ox, oh, ob = _generate_params_for_lstm_cell(
x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],
h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],
bias_size=[1, hps.num_lstm_nodes[0]]
)
with tf.variable_scope('forget'):
fx, fh, fb = _generate_params_for_lstm_cell(
x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],
h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],
bias_size=[1, hps.num_lstm_nodes[0]]
)
with tf.variable_scope('memory'):
cx, ch, cb = _generate_params_for_lstm_cell(
x_size=[hps.num_embedding_size, hps.num_lstm_nodes[0]],
h_size=[hps.num_lstm_nodes[0], hps.num_lstm_nodes[0]],
bias_size=[1, hps.num_lstm_nodes[0]]
)
state = tf.Variable(
tf.zeros([batch_size, hps.num_lstm_nodes[0]]),
trainable=False
)
h = tf.Variable(
tf.zeros([batch_size, hps.num_lstm_nodes[0]]),
trainable=False
)
for i in range(num_timesteps):
# [batch_size, 1, embed_size]
embed_input = embed_inputs[:, i, :]
embed_input = tf.reshape(embed_input,
[batch_size, hps.num_embedding_size])
forget_gate = tf.sigmoid(
tf.matmul(embed_input, fx) + tf.matmul(h, fh) + fb)
input_gate = tf.sigmoid(
tf.matmul(embed_input, ix) + tf.matmul(h, ih) + ib)
output_gate = tf.sigmoid(
tf.matmul(embed_input, ox) + tf.matmul(h, oh) + ob)
mid_state = tf.tanh(
tf.matmul(embed_input, cx) + tf.matmul(h, ch) + cb)
state = mid_state * input_gate + state * forget_gate
h = output_gate * tf.tanh(state)
last = h
fc_init = tf.uniform_unit_scaling_initializer(factor=1.0)
with tf.variable_scope('fc', initializer=fc_init):
fc1 = tf.layers.dense(last,
hps.num_fc_nodes,
activation=tf.nn.relu,
name='fc1')
fc1_dropout = tf.contrib.layers.dropout(fc1, keep_prob)
logits = tf.layers.dense(fc1_dropout,
num_classes,
name='fc2')
with tf.name_scope('metrics'):
softmax_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=outputs)
loss = tf.reduce_mean(softmax_loss)
# [0, 1, 5, 4, 2] -> argmax: 2
y_pred = tf.argmax(tf.nn.softmax(logits),
1,
output_type=tf.int32)
correct_pred = tf.equal(outputs, y_pred)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
with tf.name_scope('train_op'):
tvars = tf.trainable_variables()
for var in tvars:
tf.logging.info('variable name: %s' % (var.name))
grads, _ = tf.clip_by_global_norm(
tf.gradients(loss, tvars), hps.clip_lstm_grads) # 限制梯度，防止梯度爆炸
optimizer = tf.train.AdamOptimizer(hps.learning_rate)
train_op = optimizer.apply_gradients(
zip(grads, tvars), global_step=global_step)
return ((inputs, outputs, keep_prob),
(loss, accuracy),
(train_op, global_step))
placeholders, metrics, others = create_model(
hps, vocab_size, num_classes)
inputs, outputs, keep_prob = placeholders
loss, accuracy = metrics
train_op, global_step = others
print("ok5")
init_op = tf.global_variables_initializer()
train_keep_prob_value = 0.8
test_keep_prob_value = 1.0
num_train_steps = 10000
# Train: 99.7%
# Valid: 92.7%
# Test: 93.2%
with tf.Session() as sess:
sess.run(init_op)
for i in range(num_train_steps):
batch_inputs, batch_labels = train_dataset.next_batch(
hps.batch_size)
outputs_val = sess.run([loss, accuracy, train_op, global_step],
feed_dict = {
inputs: batch_inputs,
outputs: batch_labels,
keep_prob: train_keep_prob_value,
})
loss_val, accuracy_val, _, global_step_val = outputs_val
if global_step_val % 20 == 0:
tf.logging.info("Step: %5d, loss: %3.3f, accuracy: %3.3f"
% (global_step_val, loss_val, accuracy_val))
来源：CSDN
作者：beata2
链接：https://blog.csdn.net/beata2/article/details/100639898