def load_dataset(self):
ds = TextLineDataset(str(pathlib.Path(self.log_dir, 'file_names.txt')))
ds = ds.take(5)
ds = ds.map(self.parse_svg_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.padded_batch(2, drop_remainder=True)
return ds
class BiLSTM(object):
def __init__(self, config):
self.class_num = config.class_num
self.unit_num = config.unit_num
self.vocab_size = config.vocab_size
self.dense_unit_num = config.dense_unit_num
self.train_batch_size = config.train_batch_size
self.valid_batch_size = config.valid_batch_size
self.test_batch_size = config.test_batch_size
if config.train_mode == 'CHAR-RANDOM':
# 文本长度
self.text_length = preprocess.MAX_CHAR_TEXT_LENGTH
# 词嵌入维度
self.embedding_dim = config.embedding_dim
elif config.train_mode == 'WORD-NON-STATIC':
self.text_length = preprocess.MAX_WORD_TEXT_LENGTH
self.embedding_dim = preprocess.vec_dim
self.train_mode = config.train_mode
self.input_x = None
self.input_y = None
self.labels = None
self.dropout_keep_prob = None
self.training = None
self.embedding_inputs = None
self.embedding_inputs_expanded = None
self.loss = None
self.accuracy = None
self.prediction = None
self.vocab = None
self.vecs_dict = {}
self.embedding_W = None
def setBiLSTM(self):
# 输入层
self.input_x = tf.placeholder(tf.int32, [None, self.text_length], name="input_x")
self.labels = tf.placeholder(tf.int32, [None], name="input_y")
# 把数字标签转为one hot形式
self.input_y = tf.one_hot(self.labels, self.class_num)
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
# 训练时batch_normalization的Training参数应为True,
# 验证或测试时应为False
self.training = tf.placeholder(tf.bool, name='training')
# 词嵌入层
with tf.device('/cpu:0'), tf.name_scope('embedding'):
if self.train_mode == 'CHAR-RANDOM':
# 随机初始化的词向量
W = tf.Variable(tf.random_uniform([self.vocab_size, self.embedding_dim], -1.0, 1.0))
elif self.train_mode == 'WORD-NON-STATIC':
# 用之前读入的预训练词向量
W = tf.Variable(self.embedding_W)
self.embedding_inputs = tf.nn.embedding_lookup(W, self.input_x)
with tf.name_scope("batch_norm"):
self.embedding_inputs = tf.layers.batch_normalization(self.embedding_inputs, training=self.training)
def basic_lstm_cell():
bcell = tf.nn.rnn_cell.LSTMCell(self.unit_num)
return tf.nn.rnn_cell.DropoutWrapper(bcell, output_keep_prob=self.dropout_keep_prob)
with tf.name_scope("RNN"):
# 双向LSTM网络，每层有units_num个神经元
# ======================================================================================
# Bidirection LSTM
lstm_fw_cell = basic_lstm_cell() # forward direction cell
lstm_bw_cell = basic_lstm_cell() # backward direction cell
# [batch_size, sequence_length, hidden_size] #creates a dynamic bidirectional recurrent neural network
output, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, self.embedding_inputs,
dtype=tf.float32)
# concat output
output = tf.concat(output, axis=2) # [batch_size,sequence_length,hidden_size*2]
rnn_output = output[:, -1, :] # 取最后一个时序作为输出结果
# =========================================================================================
with tf.name_scope("dense"):
# 全连接层
# ======================================================================================
h_full = tf.layers.dense(inputs=rnn_output,
units=self.dense_unit_num,
use_bias=True,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.1),
bias_initializer=tf.constant_initializer(0.1)
)
h_full = tf.layers.dropout(h_full, rate=self.dropout_keep_prob)
h_full = tf.nn.relu(h_full)
# ==========================================================================================
# Output layer
with tf.name_scope('output'):
score = tf.layers.dense(
h_full,
units=self.class_num,
activation=None,
use_bias=True,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.1),
bias_initializer=tf.constant_initializer(0.1)
)
self.score = tf.multiply(score, 1, name='score')
self.prediction = tf.argmax(score, 1, name='prediction')
# Loss function
with tf.name_scope('loss'):
losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=score, labels=self.input_y)
self.loss = tf.reduce_mean(losses)
# Calculate accuracy
with tf.name_scope('accuracy'):
correct_predictions = tf.equal(self.prediction, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
def convert_input(self, lines):
"""
将训练集数据转换为id或词向量表示
"""
batch_x = []
batch_y = []
title = ""
# 1.id
for line in lines:
line_ = line.decode("gbk").strip().split(',')
title = ''.join(line_[0:-1]) # 逗号前段为标题
label = ''.join(line_[-1]) # 最后一项为标签
batch_x.append(preprocess.to_id(title, self.vocab, self.train_mode))
batch_y.append(label)
batch_x = np.stack(batch_x)
return batch_x, batch_y
def convert_test_input(self, titles):
"""
将测试集tsv数据转为id或词向量表示
:param titles:
:return:
"""
batch_x = []
# 1.id
for title in titles:
valid_title = title.decode('gb18030').strip('\t')
batch_x.append(preprocess.to_id(valid_title, self.vocab, self.train_mode))
batch_x = np.stack(batch_x)
return batch_x
def prepare_data(self):
# Data preparation.
# =======================================================
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
# 读取词汇表
self.vocab = preprocess.read_vocab(os.path.join('data',preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC' or self.train_mode == 'MULTI':
# 把预训练词向量的值读到变量中
self.vocab = preprocess.read_vocab(os.path.join('data', preprocess.WORD_VOCAB_PATH))
self.vecs_dict = preprocess.load_vecs(os.path.join('data', preprocess.SGNS_WORD_PATH))
self.embedding_W = np.ndarray(shape=[self.vocab_size, self.embedding_dim], dtype=np.float32)
for word in self.vocab:
# 第n行对应id为n的词的词向量
if word not in self.vecs_dict:
preprocess.add_word(word, self.vecs_dict)
self.embedding_W[self.vocab[word]] = self.vecs_dict[word]
self.dataset = TextLineDataset(os.path.join('data', preprocess.TRAIN_WITH_ID_PATH))
print('Shuffling dataset...')
self.dataset = self.dataset.shuffle(preprocess.TOTAL_TRAIN_SIZE)
# 分割数据集
# 取前VALID_SIZE个样本给验证集
valid_dataset = self.dataset.take(preprocess.VALID_SIZE).batch(self.valid_batch_size)
# 剩下的给训练集
train_dataset = self.dataset.skip(preprocess.VALID_SIZE).batch(self.train_batch_size)
# Create a reinitializable iterator
train_iterator = train_dataset.make_initializable_iterator()
valid_iterator = valid_dataset.make_initializable_iterator()
train_init_op = train_iterator.initializer
valid_init_op = valid_iterator.initializer
# 要获取元素，先sess.run(train_init_op)初始化迭代器
# 再sess.run(next_train_element)
next_train_element = train_iterator.get_next()
next_valid_element = valid_iterator.get_next()
return train_init_op, valid_init_op, next_train_element, next_valid_element
# =============================================================
# Date preparation ends.
def prepare_test_data(self):
# 读取词汇表
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
self.vocab = preprocess.read_vocab(os.path.join('data',preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC':
self.vocab = preprocess.read_vocab(os.path.join('data', preprocess.WORD_VOCAB_PATH))
# 测试集有标题，读取时注意跳过第一行
dataset = TextLineDataset(os.path.join('data',preprocess.TEST_PATH))
dataset = dataset.shuffle(preprocess.TOTAL_TEST_SIZE).batch(self.test_batch_size)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
return dataset, next_element
class TextCNN(object):
def __init__(self, config):
self.class_num = config.class_num
self.filter_sizes = config.filter_sizes
self.filter_num = config.filter_num
self.vocab_size = config.vocab_size
self.dense_unit_num = config.dense_unit_num
self.train_batch_size = config.train_batch_size
self.valid_batch_size = config.valid_batch_size
self.test_batch_size = config.test_batch_size
if config.train_mode == 'CHAR-RANDOM':
# 文本长度
self.text_length = preprocess.MAX_CHAR_TEXT_LENGTH
# 词嵌入维度
self.embedding_dim = config.embedding_dim
elif config.train_mode == 'WORD-NON-STATIC' or config.train_mode == 'MULTI':
self.text_length = preprocess.MAX_WORD_TEXT_LENGTH
self.embedding_dim = preprocess.vec_dim
self.train_mode = config.train_mode
self.input_x = None
self.input_y = None
self.labels = None
self.dropout_keep_prob = None
self.training = None
self.embedding_inputs_expanded = None
self.loss = None
self.accuracy = None
self.prediction = None
self.vocab = None
self.vecs_dict = {}
self.embedding_W = None
self.dataset = None
def setCNN(self):
# 输入层
self.input_x = tf.placeholder(tf.int32, [None, self.text_length],
name="input_x")
self.labels = tf.placeholder(tf.int32, [None], name="input_y")
# 把数字标签转为one hot形式
self.input_y = tf.one_hot(self.labels, self.class_num)
self.dropout_keep_prob = tf.placeholder(tf.float32,
name="dropout_keep_prob")
# 训练时batch_normalization的Training参数应为True,
# 验证或测试时应为False
self.training = tf.placeholder(tf.bool, name='training')
# 词嵌入层
with tf.device('/cpu:0'), tf.name_scope('embedding'):
if self.train_mode == 'CHAR-RANDOM':
# 随机初始化的词向量
W = tf.Variable(
tf.random_uniform([self.vocab_size, self.embedding_dim],
-1.0, 1.0))
embedding_inputs = tf.nn.embedding_lookup(W, self.input_x)
self.embedding_inputs_expanded = tf.expand_dims(
embedding_inputs, -1)
elif self.train_mode == 'WORD-NON-STATIC':
# 用之前读入的预训练词向量
W = tf.Variable(self.embedding_W)
embedding_inputs = tf.nn.embedding_lookup(W, self.input_x)
self.embedding_inputs_expanded = tf.expand_dims(
embedding_inputs, -1)
elif self.train_mode == 'MULTI':
W1 = tf.Variable(self.embedding_W)
W2 = tf.Variable(self.embedding_W, trainable=False)
embedding_inputs1 = tf.nn.embedding_lookup(W1, self.input_x)
embedding_inputs2 = tf.nn.embedding_lookup(W2, self.input_x)
self.embedding_inputs_expanded = tf.stack(
[embedding_inputs1, embedding_inputs2], axis=-1)
# The final pooling output, containing outputs from each filter
pool_outputs = []
# Iterate to create convolution layer for each filter
for filter_size in self.filter_sizes:
with tf.name_scope("conv-maxpool-%d" % filter_size):
# Convolution layer 1
# ==================================================================
filter_shape = [filter_size, self.embedding_dim]
conv_1 = tf.layers.conv2d(
inputs=self.embedding_inputs_expanded,
filters=self.filter_num,
kernel_size=filter_shape,
strides=[1, 1],
padding='VALID',
use_bias=True,
kernel_initializer=tf.initializers.truncated_normal(
stddev=0.1),
bias_initializer=tf.initializers.constant(0.1))
# ===================================================================
# Do batch normalization
# =================================================================
conv_1_output = tf.layers.batch_normalization(
conv_1, training=self.training)
conv_1_output = tf.nn.relu(conv_1_output)
# ======================================================================
# Pooling layer 1
# ====================================================================
conv_1_output_shape = conv_1_output.shape.as_list()
pool_1 = tf.layers.max_pooling2d(
inputs=conv_1_output,
pool_size=[conv_1_output_shape[1] - 1 + 1, 1],
strides=[1, 1],
padding='VALID')
# =====================================================================
pool_outputs.append(pool_1)
# Combine all the pooling output
# The total number of filters.
total_filter_num = self.filter_num * len(self.filter_sizes)
h_pool = tf.concat(pool_outputs, 3)
h_pool_flat = tf.reshape(h_pool, [-1, total_filter_num])
# Output shape[batch, total_filter_num]
# Full-connected layer
# ========================================================================
with tf.name_scope('dense-%d' % self.dense_unit_num):
h_full = tf.layers.dense(
h_pool_flat,
units=self.dense_unit_num,
use_bias=True,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.1),
bias_initializer=tf.constant_initializer(0.1))
h_full = tf.layers.dropout(h_full, rate=self.dropout_keep_prob)
h_full = tf.nn.relu(h_full)
# =========================================================================
# Output layer
with tf.name_scope('output'):
score = tf.layers.dense(
h_full,
units=self.class_num,
activation=None,
use_bias=True,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.1),
bias_initializer=tf.constant_initializer(0.1))
self.score = tf.multiply(score, 1, name='score')
self.prediction = tf.argmax(score, 1, name='prediction')
# Loss function
with tf.name_scope('loss'):
losses = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=score, labels=self.input_y)
self.loss = tf.reduce_mean(losses)
# Calculate accuracy
with tf.name_scope('accuracy'):
correct_predictions = tf.equal(self.prediction,
tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(
tf.cast(correct_predictions, tf.float32))
def convert_input(self, lines):
"""
将训练集数据转换为id或词向量表示
"""
batch_x = []
batch_y = []
title = ""
for line in lines:
line_ = line.decode("gbk").strip().split(',')
title = ''.join(line_[0:-1]) # 逗号前段为标题
label = ''.join(line_[-1]) # 最后一项为标签
batch_x.append(preprocess.to_id(title, self.vocab,
self.train_mode))
batch_y.append(label)
batch_x = np.stack(batch_x)
return batch_x, batch_y
def convert_test_input(self, titles):
"""
将测试集tsv数据转为id或词向量表示
:param titles:
:return:
"""
batch_x = []
# 1.id
for title in titles:
valid_title = title.decode('gb18030').strip('\t')
batch_x.append(
preprocess.to_id(valid_title, self.vocab, self.train_mode))
batch_x = np.stack(batch_x)
return batch_x
def prepare_data(self):
# Data preparation.
# =======================================================
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
# 读取词汇表
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC' or self.train_mode == 'MULTI':
# 把预训练词向量的值读到变量中
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.WORD_VOCAB_PATH))
self.vecs_dict = preprocess.load_vecs(
os.path.join('data', preprocess.SGNS_WORD_PATH))
self.embedding_W = np.ndarray(
shape=[self.vocab_size, self.embedding_dim], dtype=np.float32)
for word in self.vocab:
# 第n行对应id为n的词的词向量
if word not in self.vecs_dict:
preprocess.add_word(word, self.vecs_dict)
self.embedding_W[self.vocab[word]] = self.vecs_dict[word]
self.dataset = TextLineDataset(
os.path.join('data', preprocess.TRAIN_WITH_ID_PATH))
print('Shuffling dataset...')
self.dataset = self.dataset.shuffle(preprocess.TOTAL_TRAIN_SIZE)
# 分割数据集
# 取前VALID_SIZE个样本给验证集
valid_dataset = self.dataset.take(preprocess.VALID_SIZE).batch(
self.valid_batch_size)
# 剩下的给训练集
train_dataset = self.dataset.skip(preprocess.VALID_SIZE).batch(
self.train_batch_size)
# Create a reinitializable iterator
train_iterator = train_dataset.make_initializable_iterator()
valid_iterator = valid_dataset.make_initializable_iterator()
train_init_op = train_iterator.initializer
valid_init_op = valid_iterator.initializer
# 要获取元素，先sess.run(train_init_op)初始化迭代器
# 再sess.run(next_train_element)
next_train_element = train_iterator.get_next()
next_valid_element = valid_iterator.get_next()
return train_init_op, valid_init_op, next_train_element, next_valid_element
# =============================================================
# Date preparation ends.
def prepare_test_data(self):
# 读取词汇表
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC' or self.train_mode == 'MULTI':
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.WORD_VOCAB_PATH))
# 测试集有标题，读取时注意跳过第一行
dataset = TextLineDataset(os.path.join('data', preprocess.TEST_PATH))
dataset = dataset.shuffle(preprocess.TOTAL_TEST_SIZE).batch(
self.test_batch_size)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
return dataset, next_element