大家好，又见面了，我是你们的朋友全栈君。
本文我写了一个基于tensorflow的DCNN的实现，原文是A Convolutional Neural Network for Modelling Sentences，地址如下：
https://arxiv.org/abs/1404.2188
先给出我自己的github的代码https://github.com/jacky123465/DCNN（如果是python3.几的版本是可以直接运行的哦）
１. 首先是数据预处理的部分
文件名字为DataUnits.py
def clean_str(string):用于去掉数据中一些类似于问号，感叹号的内容
def clean_str(string):
string = re.sub(r"[^A-Za-z0-9:(),!?\'\`]", " ", string)
string = re.sub(r" : ", ":", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower()load_data_and_labels():用于从文件中读入x和y。同时对y进行如下处理：假设y是１到６，那么我们将其变成000001,000010,000100,001000,010000,100000
def load_data_and_labels():
"""
Loads data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# Load data from files
folder_prefix = 'data/'
#测试
#增加了一个'rb'
x_train = list(open(folder_prefix+"train", 'rb').readlines())
x_test = list(open(folder_prefix+"test", 'rb').readlines())
test_size = len(x_test)
x_text = x_train + x_test
#修改
le = len(x_text)
for i in range(le):
encode_type = chardet.detect(x_text[i])
x_text[i] = x_text[i].decode(encode_type['encoding']) # 进行相应解码，赋给原标识符（变量）
#修改
x_text = [clean_str(sent) for sent in x_text]
y = [s.split(' ')[0].split(':')[0] for s in x_text]
x_text = [s.split(" ")[1:] for s in x_text]
# Generate labels
all_label = dict()
for label in y:
if not label in all_label:
all_label[label] = len(all_label) + 1
one_hot = np.identity(len(all_label))
y = [one_hot[ all_label[label]-1 ] for label in y]
return [x_text, y, test_size]def pad_sentences(sentences, padding_word=”<PAD/>”):因为句子长短不一，所以把他的长短补充完整
def pad_sentences(sentences, padding_word="<PAD/>"):
"""
Pads all sentences to the same length. The length is defined by the longest sentence.
Returns padded sentences.
"""
sequence_length = max(len(x) for x in sentences)
padded_sentences = []
for i in range(len(sentences)):
sentence = sentences[i]
num_padding = sequence_length - len(sentence)
new_sentence = sentence + [padding_word] * num_padding
padded_sentences.append(new_sentence)
return padded_sentencesload_data:导入数据的主函数
def load_data():
"""
Loads and preprocessed data
Returns input vectors, labels, vocabulary, and inverse vocabulary.
"""
# Load and preprocess data
sentences, labels, test_size = load_data_and_labels()
sentences_padded = pad_sentences(sentences)
vocabulary, vocabulary_inv = build_vocab(sentences_padded)
x, y = build_input_data(sentences_padded, labels, vocabulary)
return [x, y, vocabulary, vocabulary_inv, test_size]batch_iter:将数据按照batch_size的大小划分，放进网络训练：
def batch_iter(data, batch_size, num_epochs):
"""
Generates a batch iterator for a dataset.
"""
data = np.array(data)
data_size = len(data)
num_batches_per_epoch = int(len(data)/batch_size) + 1
for epoch in range(num_epochs):
# Shuffle the data at each epoch
shuffle_indices = np.random.permutation(np.arange(data_size))
shuffled_data = data[shuffle_indices]
for batch_num in range(num_batches_per_epoch):
start_index = batch_num * batch_size
end_index = (batch_num + 1) * batch_size
if end_index > data_size:
end_index = data_size
start_index = end_index - batch_size
yield shuffled_data[start_index:end_index]２．然后是模型部分
包括原文中的卷积池化，全连接等操作
#对model稍作改动
import tensorflow as tf
class DCNN():
def __init__(self, batch_size, sentence_length, num_filters, embed_size, top_k, k1):
self.batch_size = batch_size
self.sentence_length = sentence_length
self.num_filters = num_filters
self.embed_size = embed_size
self.top_k = top_k
self.k1 = k1
def per_dim_conv_k_max_pooling_layer(self, x, w, b, k):
self.k1 = k
input_unstack = tf.unstack(x, axis=2)
w_unstack = tf.unstack(w, axis=1)
b_unstack = tf.unstack(b, axis=1)
convs = []
with tf.name_scope("per_dim_conv_k_max_pooling"):
for i in range(self.embed_size):
conv = tf.nn.relu(tf.nn.conv1d(input_unstack[i], w_unstack[i], stride=1, padding="SAME") + b_unstack[i])
#conv:[batch_size, sent_length+ws-1, num_filters]
conv = tf.reshape(conv, [self.batch_size, self.num_filters[0], self.sentence_length])#[batch_size, sentence_length, num_filters]
values = tf.nn.top_k(conv, k, sorted=False).values
values = tf.reshape(values, [self.batch_size, k, self.num_filters[0]])
#k_max pooling in axis=1
convs.append(values)
conv = tf.stack(convs, axis=2)
#[batch_size, k1, embed_size, num_filters[0]]
#print conv.get_shape()
return conv
def per_dim_conv_layer(self, x, w, b):
input_unstack = tf.unstack(x, axis=2)
w_unstack = tf.unstack(w, axis=1)
b_unstack = tf.unstack(b, axis=1)
convs = []
with tf.name_scope("per_dim_conv"):
for i in range(len(input_unstack)):
#yf = input_unstack[i]
conv = tf.nn.relu(tf.nn.conv1d(input_unstack[i], w_unstack[i], stride=1, padding="SAME") + b_unstack[i])#[batch_size, k1+ws2-1, num_filters[1]]
convs.append(conv)
conv = tf.stack(convs, axis=2)
#[batch_size, k1+ws-1, embed_size, num_filters[1]]
return conv
#增加的函数，只用来做folding操作
def k_max_pooling(self, x, k):
input_unstack = tf.unstack(x, axis=2)
out = []
with tf.name_scope("k_max_pooling"):
for i in range(len(input_unstack)):
conv = tf.transpose(input_unstack[i], perm=[0, 2, 1])
values = tf.nn.top_k(conv, k, sorted=False).values
values = tf.transpose(values, perm=[0, 2, 1])
out.append(values)
fold = tf.stack(out, axis=2)
return fold
def fold_k_max_pooling(self, x, k):
input_unstack = tf.unstack(x, axis=2)
out = []
with tf.name_scope("fold_k_max_pooling"):
for i in range(0, len(input_unstack), 2):
fold = tf.add(input_unstack[i], input_unstack[i+1])#[batch_size, k1, num_filters[1]]
conv = tf.transpose(fold, perm=[0, 2, 1])
values = tf.nn.top_k(conv, k, sorted=False).values #[batch_size, num_filters[1], top_k]
values = tf.transpose(values, perm=[0, 2, 1])
out.append(values)
fold = tf.stack(out, axis=2)#[batch_size, k2, embed_size/2, num_filters[1]]
return fold
def full_connect_layer(self, x, w, b, wo, dropout_keep_prob):
with tf.name_scope("full_connect_layer"):
h = tf.nn.tanh(tf.matmul(x, w) + b)
h = tf.nn.dropout(h, dropout_keep_prob)
o = tf.matmul(h, wo)
return o
def DCNN(self, sent, W1, W2, b1, b2, k1, top_k, Wh, bh, Wo, dropout_keep_prob):
conv1 = self.per_dim_conv_layer(sent, W1, b1)
conv1 = self.k_max_pooling(conv1, k1)
#conv1 = self.fold_k_max_pooling(conv1, k1)
conv2 = self.per_dim_conv_layer(conv1, W2, b2)
fold = self.fold_k_max_pooling(conv2, top_k)
#增加一个int
#fold_flatten = tf.reshape(fold, [-1, int(top_k * self.embed_size * self.num_filters[1] / 4)])
fold_flatten = tf.reshape(fold, [-1, int(top_k * self.embed_size * self.num_filters[1] / 2)])
#fold_flatten = tf.reshape(fold, [-1, int(top_k*100*14/4)])
print(fold_flatten.get_shape())
out = self.full_connect_layer(fold_flatten, Wh, bh, Wo, dropout_keep_prob)
return out３．最后是主函数
compile.py文件，整体就是一些初始化和调用模型的过程。每个参数我都写了比较详细的注释，读者可以自己调节
#coding=utf8
from models import *
import dataUtils
import numpy as np
import time
import os
class train():
def __init__(self, x_train, x_dev, y_train, y_dev, x_test, y_test):
self.x_train = x_train
self.x_dev = x_dev
self.y_train = y_train
self.y_dev = y_dev
self.x_test = x_test
self.y_test = y_test
def train(self):
embed_dim = 32
ws = [8, 5]
top_k = 4
k1 = 19
num_filters = [6, 14]
batch_size = 40
n_epochs = 25
num_hidden = 100
sentence_length = 37
num_class = 6
evaluate_every = 200
checkpoint_every = 200
num_checkpoints = 5
# --------------------------------------------------------------------------------------#
def init_weights(shape, name):
return tf.Variable(tf.truncated_normal(shape, stddev=0.01), name=name)
sent = tf.placeholder(tf.int64, [None, sentence_length])
y = tf.placeholder(tf.float64, [None, num_class])
dropout_keep_prob = tf.placeholder(tf.float32, name="dropout")
with tf.name_scope("embedding_layer"):
W = tf.Variable(tf.random_uniform([len(vocabulary), embed_dim], -1.0, 1.0), name="embed_W")
sent_embed = tf.nn.embedding_lookup(W, sent)
# input_x = tf.reshape(sent_embed, [batch_size, -1, embed_dim, 1])
input_x = tf.expand_dims(sent_embed, -1)
# [batch_size, sentence_length, embed_dim, 1]
W1 = init_weights([ws[0], embed_dim, 1, num_filters[0]], "W1")
b1 = tf.Variable(tf.constant(0.1, shape=[num_filters[0], embed_dim]), "b1")
# 增加int()
# W2 = init_weights([ws[1], int(embed_dim/2), num_filters[0], num_filters[1]], "W2")
W2 = init_weights([ws[1], int(embed_dim), num_filters[0], num_filters[1]], "W2")
b2 = tf.Variable(tf.constant(0.1, shape=[num_filters[1], embed_dim]), "b2")
# 增加int
# Wh = init_weights([int(top_k*embed_dim*num_filters[1]/4), num_hidden], "Wh")
Wh = init_weights([int(top_k * embed_dim * num_filters[1] / 2), num_hidden], "Wh")
bh = tf.Variable(tf.constant(0.1, shape=[num_hidden]), "bh")
Wo = init_weights([num_hidden, num_class], "Wo")
model = DCNN(batch_size, sentence_length, num_filters, embed_dim, top_k, k1)
out = model.DCNN(input_x, W1, W2, b1, b2, k1, top_k, Wh, bh, Wo, dropout_keep_prob)
with tf.name_scope("cost"):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out, labels=y))
# train_step = tf.train.AdamOptimizer(lr).minimize(cost)
predict_op = tf.argmax(out, axis=1, name="predictions")
with tf.name_scope("accuracy"):
acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(out, 1)), tf.float32))
# -------------------------------------------------------------------------------------------#
print('Started training')
with tf.Session() as sess:
# init = tf.global_variables_initializer().run()
global_step = tf.Variable(0, name="global_step", trainable=False)
# 学习率函数
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cost)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
# Keep track of gradient values and sparsity
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram("{}/grad/hist".format(v.name), g)
sparsity_summary = tf.summary.scalar("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.summary.merge(grad_summaries)
# Output directory for models and summaries
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", cost)
acc_summary = tf.summary.scalar("accuracy", acc)
# Train Summaries
train_summary_op = tf.summary.merge([loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=num_checkpoints)
# Initialize all variables
sess.run(tf.global_variables_initializer())
def train_step(x_batch, y_batch):
feed_dict = {
sent: x_batch,
y: y_batch,
dropout_keep_prob: 0.5
}
_, step, summaries, loss, accuracy = sess.run(
[train_op, global_step, train_summary_op, cost, acc],
feed_dict)
print("TRAIN step {}, loss {:g}, acc {:g}".format(step, loss, accuracy))
train_summary_writer.add_summary(summaries, step)
def dev_step(x_batch, y_batch, writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {
sent: x_batch,
y: y_batch,
dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy = sess.run(
[global_step, dev_summary_op, cost, acc],
feed_dict)
print("VALID step {}, loss {:g}, acc {:g}".format(step, loss, accuracy))
if writer:
writer.add_summary(summaries, step)
return accuracy, loss
# 添加list强制装换
batches = dataUtils.batch_iter(list(zip(self.x_train, self.y_train)), batch_size, n_epochs)
# Training loop. For each batch...
max_acc = 0
best_at_step = 0
for batch in batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % evaluate_every == 0:
print("\nEvaluation:")
acc_dev, _ = dev_step(self.x_dev, self.y_dev, writer=dev_summary_writer)
if acc_dev >= max_acc:
max_acc = acc_dev
best_at_step = current_step
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("")
if current_step % checkpoint_every == 0:
print('Best of valid = {}, at step {}'.format(max_acc, best_at_step))
saver.restore(sess, checkpoint_prefix + '-' + str(best_at_step))
print('Finish training. On test set:')
acc, loss = dev_step(self.x_test, self.y_test, writer=None)
print(acc, loss)
dev = 300
# Load data
print("Loading data...")
x_, y_, vocabulary, vocabulary_inv, test_size = dataUtils.load_data()
# x_:长度为5952的np.array。（包含5452个训练集和500个测试集）其中每个句子都是padding成长度为37的list（padding的索引为0）
# y_:长度为5952的np.array。每一个都是长度为6的onehot编码表示其类别属性
# vocabulary：长度为8789的字典，说明语料库中一共包含8789各单词。key是单词，value是索引
# vocabulary_inv：长度为8789的list，是按照单词出现次数进行排列。依次为：<PAD?>,\\?,the,what,is,of,in,a....
# test_size:500,测试集大小
# Randomly shuffle data
x, x_test = x_[:-test_size], x_[-test_size:]
y, y_test = y_[:-test_size], y_[-test_size:]
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
x_train, x_dev = x_shuffled[:-dev], x_shuffled[-dev:]
y_train, y_dev = y_shuffled[:-dev], y_shuffled[-dev:]
print("Train/Dev/Test split: {:d}/{:d}/{:d}".format(len(y_train), len(y_dev), len(y_test)))
yf = train(x_train, x_dev, y_train, y_dev, x_test, y_test)
yf.train()本人第一次写这种长的博客，如果有写的不好的地方，欢迎大家指正
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