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
def test_pipeline(self, num_threads):
real_fname = os.path.join(self.dataset_path, 'test_real.txt')
# extract directories
real_dir, inst_dir = self.real_dir, self.inst_dir
# count lines
num_real = count_lines(real_fname)
# dataset creation
with tf.name_scope('dataset'):
real = TextLineDataset(real_fname)
# @see https://www.tensorflow.org/api_docs/python/tf/contrib/data/shuffle_and_repeat
#synt.apply(shuffle_and_repeat(buffer_size = num_synt)) #, count = 1))
#real.apply(shuffle_and_repeat(buffer_size = num_real)) #, count = ceil(ratio)))
real = real.shuffle(num_real) # no repetition! .repeat()
# real data only
augment = 0 # self.params.get('augment', 0)
def name2real(name):
inst = read_instr(os.path.join(inst_dir, name.decode() + '.png'))
if augment:
src_dir = self.params.get('augment_src', 'best')
# print('{}/{}/{}'.format(real_dir, str(src_dir), name.decode() + '.JPG'))
full = read_image(os.path.join(real_dir, str(src_dir), 'rgb', name.decode() + '.jpg'), False)
pnts = read_points(os.path.join(real_dir, str(src_dir), 'points', name.decode() + '.txt'))
if isinstance(src_dir, float):
pnts *= src_dir
self.params['augment_scale'] = 0.
real = random_crop(full, pnts, self.params)
else:
real = read_image(os.path.join(real_dir, '160x160', 'gray', name.decode() + '.jpg'))
return real, inst, name.decode()
real = real.map(lambda name: tuple(tf.py_func(name2real, [name], [tf.float32, tf.int32, tf.string])), num_parallel_calls = num_threads)
#dataset = Dataset.zip((rend, xfer, real, inst_synt, inst_real))
dataset = Dataset.zip({ 'real': real })
dataset = dataset.batch(self.batch_size, drop_remainder = True) # we need full batches!
dataset = dataset.prefetch(self.batch_size * 2)
return dataset
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 CNN(object):
def __init__(self, config):
self.class_num = config.class_num
self.img_size = config.img_size
self.crop_size = config.crop_size
self.train_batch_size = config.train_batch_size
self.test_batch_size = config.test_batch_size
self.test_per_batch = config.test_per_batch
self.batch_x = ''
self.batch_y = ''
self.input_x = ''
self.labels = ''
self.input_y = ''
self.dropout_keep_prob = ''
self.training = ''
self.embedding_inputs = ''
self.embedding_inputs_expanded = ''
self.loss = ''
self.accuracy = ''
self.prediction = ''
self.vocab = ''
def _set_input(self):
# Input layer
self.input_x = tf.placeholder(tf.float32, [None, self.img_size, self.img_size, 1], name="input_x")
self.labels = tf.placeholder(tf.int32, [None], name="labels")
self.input_y = tf.one_hot(self.labels, self.class_num, name='input_y')
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')
self.input_x_enhanced = tf.map_fn(self.image_enhance, self.input_x)
def _conv(self, input, ksize, stride, filters):
return tf.layers.conv2d(
inputs=input,
filters=filters,
kernel_size=[ksize, ksize],
strides=[stride, stride],
padding='SAME',
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.initializers.constant(0.0),
)
def _maxpool_2x2(self, input):
return tf.layers.max_pooling2d(
inputs=input,
pool_size=[2, 2],
strides=[2, 2],
padding='SAME',
)
def _fc(self, input, units, dropout_keep_prob, name=None):
fc_output = tf.layers.dense(
inputs=input,
units=units,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
bias_initializer=tf.initializers.constant(0.0),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.001),
bias_regularizer=tf.contrib.layers.l2_regularizer(0.001),
name=name
)
return tf.layers.dropout(fc_output, dropout_keep_prob)
def setVGG19(self):
"""
在此函数中设定模型
:return:
"""
self._set_input()
# 加入批标准化以减少过拟合
input_x_norm = tf.layers.batch_normalization(self.input_x_enhanced, training=self.training)
# conv3-64
conv3_64_1 = self._conv(input_x_norm, 3, 1, 64)
conv3_64_output = self._conv(conv3_64_1, 3, 1, 64)
# maxpool-1
maxpool_1_output = self._maxpool_2x2(conv3_64_output)
# conv3-128
conv3_128_1 = self._conv(maxpool_1_output, 3, 1, 128)
conv3_128_output = self._conv(conv3_128_1, 3, 1, 128)
# maxpool-2
maxpool_2_output = self._maxpool_2x2(conv3_128_output)
# conv3-256
conv3_256_1 = self._conv(maxpool_2_output, 3, 1, 256)
conv3_256_2 = self._conv(conv3_256_1, 3, 1, 256)
conv3_256_3 = self._conv(conv3_256_2, 3, 1, 256)
conv3_256_output = self._conv(conv3_256_3, 3, 1, 256)
# maxpool-3
maxpool_3_output = self._maxpool_2x2(conv3_256_output)
# conv3-512
conv3_512_1 = self._conv(maxpool_3_output, 3, 1, 512)
conv3_512_2 = self._conv(conv3_512_1, 3, 1, 512)
conv3_512_3 = self._conv(conv3_512_2, 3, 1, 512)
conv3_512_output = self._conv(conv3_512_3, 3, 1, 512)
# maxpool-4
maxpool_4_output = self._maxpool_2x2(conv3_512_output)
# conv4-512
conv3_512_1 = self._conv(maxpool_4_output, 3, 1, 512)
conv3_512_2 = self._conv(conv3_512_1, 3, 1, 512)
conv3_512_3 = self._conv(conv3_512_2, 3, 1, 512)
conv3_512_output = self._conv(conv3_512_3, 3, 1, 512)
# maxpool-5
maxpool_5_output = self._maxpool_2x2(conv3_512_output)
# flatten
shape = maxpool_5_output.shape.as_list()
dims = shape[1]*shape[2]*shape[3]
maxpool_5_output_flatten = tf.reshape(maxpool_5_output, [-1, dims])
# fully-connected-1
fc_1 = self._fc(maxpool_5_output_flatten, 2048, self.dropout_keep_prob)
# fully-connected-2
fc_2 = self._fc(fc_1, 2048, self.dropout_keep_prob)
# fully-connected-3
fc_3 = self._fc(fc_2, 1000, self.dropout_keep_prob)
# 输出层
self.score = self._fc(fc_3, self.class_num, self.dropout_keep_prob, name='score')
self.prediction = tf.argmax(self.score, 1, name='prediction')
# Loss function
with tf.name_scope('loss'):
losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.score, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + tf.losses.get_regularization_loss()
# 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):
"""
把读取的字符串数据转为形状为[batch_size, img_size, img_size, 1]的数组，
作为CNN的输入
:param pixels:
:param labels:
:return:
"""
batch_x = []
batch_y = []
for line in lines:
line_ = line.decode('utf-8').strip().split(',')
pixels = line_[1].split() # 像素值
label = line_[0] # 第一项为标签
batch_x.append([float(x) for x in pixels])
batch_y.append(int(label))
batch_x = np.stack(batch_x)
batch_x = batch_x.reshape([-1, self.img_size, self.img_size, 1])
batch_y = np.asarray(batch_y)
return batch_x, batch_y
def prepare_data(self):
self.train_dataset = TextLineDataset(os.path.join('data', preprocess.FILTERED_TRAIN_PATH)).skip(1)
self.test_dataset = TextLineDataset(os.path.join('data', preprocess.FILTERED_TEST_PATH)).skip(1)
print('Shuffling dataset...')
# 打乱数据
train_dataset = self.train_dataset.shuffle(5000).batch(self.train_batch_size)
test_dataset = self.test_dataset.shuffle(2500).batch(self.test_batch_size)
# Create a reinitializable iterator
train_iterator = train_dataset.make_initializable_iterator()
test_iterator = test_dataset.make_initializable_iterator()
train_init_op = train_iterator.initializer
test_init_op = test_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_test_element = test_iterator.get_next()
return train_init_op, test_init_op, next_train_element, next_test_element
# ==============================================================
def image_enhance(self, image):
"""
对图片进行数据增强
:param image: shape为[heigth, width, 1]的numpy数组或tensor
:return:
"""
# 进行随机裁剪
images_crop = tf.image.random_crop(image, [self.crop_size, self.crop_size, 1])
# 随机水平翻转
images_crop = tf.image.random_flip_left_right(images_crop)
# 随机对比度
images_crop = tf.image.random_contrast(images_crop, 0.5, 1.5)
# 随机亮度
images_crop = tf.image.random_brightness(images_crop, max_delta=0.5)
noise = tf.random_normal(shape=tf.shape(images_crop), mean=0.0, stddev=10.0,
dtype=tf.float32)
images_crop = tf.add(images_crop, noise)
return images_crop
def setVGG16(self):
"""
在此函数中设定模型
:return:
"""
self._set_input()
# 加入批标准化以减少过拟合
input_x_norm = tf.layers.batch_normalization(self.input_x_enhanced, training=self.training)
# conv3-64
conv3_64_1 = self._conv(input_x_norm, 3, 1, 64)
conv3_64_output = self._conv(conv3_64_1, 3, 1, 64)
# maxpool-1
maxpool_1_output = self._maxpool_2x2(conv3_64_output)
# conv3-128
conv3_128_1 = self._conv(maxpool_1_output, 3, 1, 128)
conv3_128_output = self._conv(conv3_128_1, 3, 1, 128)
# maxpool-2
maxpool_2_output = self._maxpool_2x2(conv3_128_output)
# conv3-256
conv3_256_1 = self._conv(maxpool_2_output, 3, 1, 256)
conv3_256_2 = self._conv(conv3_256_1, 3, 1, 256)
conv3_256_output = self._conv(conv3_256_2, 1, 1, 256)
# maxpool-3
maxpool_3_output = self._maxpool_2x2(conv3_256_output)
# conv3-512
conv3_512_1 = self._conv(maxpool_3_output, 3, 1, 512)
conv3_512_2 = self._conv(conv3_512_1, 3, 1, 512)
conv3_512_output = self._conv(conv3_512_2, 1, 1, 512)
# maxpool-4
maxpool_4_output = self._maxpool_2x2(conv3_512_output)
# conv4-512
conv3_512_1 = self._conv(maxpool_4_output, 3, 1, 512)
conv3_512_2 = self._conv(conv3_512_1, 3, 1, 512)
conv3_512_output = self._conv(conv3_512_2, 1, 1, 512)
# maxpool-5
maxpool_5_output = self._maxpool_2x2(conv3_512_output)
# flatten
shape = maxpool_5_output.shape.as_list()
dims = shape[1]*shape[2]*shape[3]
maxpool_5_output_flatten = tf.reshape(maxpool_5_output, [-1, dims])
# fully-connected-1
fc_1 = self._fc(maxpool_5_output_flatten, 2048, self.dropout_keep_prob)
# fully-connected-2
fc_2 = self._fc(fc_1, 2048, self.dropout_keep_prob)
# fully-connected-3
fc_3 = self._fc(fc_2, 1000, self.dropout_keep_prob)
# 输出层
self.score = self._fc(fc_3, self.class_num, self.dropout_keep_prob, name='score')
self.prediction = tf.argmax(self.score, 1, name='prediction')
# Loss function
with tf.name_scope('loss'):
losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.score, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + tf.losses.get_regularization_loss()
# 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))
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