内容简介：GAN主要分为两部分：（GAN目标是训练一个生成模型完美的拟合真实数据分布使得判别模型无法区分。）（1）生成模型：模拟真实数据的分布。（2）判别模型：判断一个样本是真实的样本还是生成的样本。
本文着重介绍SeqGAN原理 和 SeqGAN代码详解：
SeqGAN原理部分：
首先介绍GAN：
GAN主要分为两部分：（GAN目标是训练一个生成模型完美的拟合真实数据分布使得判别模型无法区分。）
（1）生成模型：模拟真实数据的分布。
（2）判别模型：判断一个样本是真实的样本还是生成的样本。
其次介绍SeqGAN：
GAN在图像领域的应用较多， 在文本方面效果不佳的原因主要是GAN 在生成连续离散序列时会遇到两个问题：一是因为生成器的输出是离散的，梯度更新从判别器传到生成器比较困难；二是判别器只有当序列被完全生成后才能进行判断，但此刻指导用处已不太大，而如果生成器生成序列的同时判别器来判断，如何平衡当前序列的分数和未来序列的分数又是一个难题。
在这篇论文中，作者提出了一个序列生成模型——SeqGAN ，来解决上述这两个问题。作者将生成器看作是强化学习中的stochastic policy，这样SeqGAN 就可以直接通过gradient policy update 避免生成器中的可导问题。同时，判别器对整个序列的评分作为强化学习的奖励信号可以通过Monte Carlo 搜索传递到序列生成的中间时刻。
具体来说，作者将生成器生成序列的过程看做是一个强化学习中的序列决策过程。生成模型被看作一个agent，目前为止已生成的序列表示当前state，下一个要生成的单词则是采取的action，判别模型对序列的评价分数则是返回的reward。
模型结构如下所示：
如上图，左边是判别器的训练，通过输入来自真实数据的正样例和来自生成器生成的负样例从而训练，判别器由 CNN 组成；右边是生成器的训练，通过将判别器判别的概率回传给生成器从而训练，这里使用了 Monte Carlo search 和 policy gradient 方法。
代码详细解释：
sequence_gan.py
import numpy as np
import tensorflow as tf
import random
from dataloader import Gen_Data_loader, Dis_dataloader
from generator import Generator
from discriminator import Discriminator
from rollout import ROLLOUT
from target_lstm import TARGET_LSTM
import cPickle
####
# 生成器、 target_lstm、rollout使用同一套模型（ rnn(不同的变种) ）
# 辨别器： 选用的cnn。
####
#########################################################################################
# 生成器的超参数部分
# Generator Hyper-parameters
######################################################################################
# 词的embedding
EMB_DIM = 32 # embedding dimension
# rnn的隐含层单元
HIDDEN_DIM = 32 # hidden state dimension of lstm cell
# 序列的最大长度
SEQ_LENGTH = 20 # sequence length
# rnn开始标示
START_TOKEN = 0
# 预训练
PRE_EPOCH_NUM = 120 # supervise (maximum likelihood estimation) epochs
SEED = 88
BATCH_SIZE = 64
#########################################################################################
# Discriminator Hyper-parameters
#########################################################################################
# 词的embedding选用的是64
dis_embedding_dim = 64
# 定义CNN中的卷积核大小
dis_filter_sizes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20]
# 定义CNN中的卷积核数量
dis_num_filters = [100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160]
dis_dropout_keep_prob = 0.75
dis_l2_reg_lambda = 0.2
dis_batch_size = 64
#########################################################################################
# Basic Training Parameters
#########################################################################################
TOTAL_BATCH = 200
positive_file = 'save/real_data.txt'
negative_file = 'save/generator_sample.txt'
eval_file = 'save/eval_file.txt'
generated_num = 10000
# 根据训练好的模型，生成文本； 思想是基于seqence2seqence的思想。
def generate_samples(sess, trainable_model, batch_size, generated_num, output_file):
# Generate Samples
# 用于保存生成的文本， 之后保存到txt文件中。
generated_samples = []
# 开始批量生成文本数据
for _ in range(int(generated_num / batch_size)):
generated_samples.extend(trainable_model.generate(sess))
# 讲生成的文本保存到txt文件中。 用于后边训练rnn模型。
with open(output_file, 'w') as fout:
for poem in generated_samples:
buffer = ' '.join([str(x) for x in poem]) + '\n'
fout.write(buffer)
# task loss用于比较真实数据和生成数据样本分布比较, (注意啊，着重看样本分布)
def target_loss(sess, target_lstm, data_loader):
# target_loss means the oracle negative log-likelihood tested with the oracle model "target_lstm"
# For more details, please see the Section 4 in https://arxiv.org/abs/1609.05473
nll = []
# 重置索引，从0开始。
data_loader.reset_pointer()
# 遍历每一个batch， 统计真实数据与样本数据分布的比较。
for it in xrange(data_loader.num_batch):
batch = data_loader.next_batch()
g_loss = sess.run(target_lstm.pretrain_loss, {target_lstm.x: batch})
nll.append(g_loss)
return np.mean(nll)
# 用于生成模型的预训练。 此处 使用中规中矩的rnn的思路。
def pre_train_epoch(sess, trainable_model, data_loader):
# Pre-train the generator using MLE for one epoch
supervised_g_losses = []
# 重置索引，从0开始。
data_loader.reset_pointer()
# 基于训练样本与测试样本的分布差异，对模型参数进行更新。
for it in xrange(data_loader.num_batch):
batch = data_loader.next_batch()
_, g_loss = trainable_model.pretrain_step(sess, batch)
supervised_g_losses.append(g_loss)
return np.mean(supervised_g_losses)
def main():
# 随机种子，出一道思考题，这是干嘛子用的啊？
random.seed(SEED)
np.random.seed(SEED)
# 断言 START_TOKEN 是否为0.
assert START_TOKEN == 0
# 初始化数据模块，用于训练
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
# 初始化数据模块，用于测试
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 5000
# 初始化辨别器数据模块，用于训练
dis_data_loader = Dis_dataloader(BATCH_SIZE)
# 定义生成器， 用于模型预训练、 测试。
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
# TARGET_LSTM的权重，用于后面初始化模型参数。
target_params = cPickle.load(open('save/target_params.pkl'))
# 基于模型参数初始化TARGET_LSTM模型，用于数据生成 和 基于task loss的真实样本与预测样本的模型评估。
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
# 定义辨别器， 用于模型预训练。
discriminator = Discriminator(sequence_length=20, num_classes=2, vocab_size=vocab_size, embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes, num_filters=dis_num_filters, l2_reg_lambda=dis_l2_reg_lambda)
# GPU——config
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
# 基于训练好的target_lstm生成训练数据。
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
# 将训练数据加载到生成器数据模块
gen_data_loader.create_batches(positive_file)
# 定义日志存放文件。
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print ('Start pre-training...')
log.write('pre-training...\n')
# 对生成器进行预训练。
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
# 经过五个epoch测试一次。
if epoch % 5 == 0:
# 生成模型预测数据
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print ('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
print ('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
# 对辨别器进行预训练。
for _ in range(50):
# 基于预训练的生成器生成 假的文本数据。
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
# 将假的文本数据 加载到 辨别器数据模块， 用于辨别器的预训练。
dis_data_loader.load_train_data(positive_file, negative_file)
# 辨别器训练3次，接着训练辨别器。
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
####################### 预备工作干好了，该办大事了 #########################
# ROLLOUT功能是干嘛的呢， 嘿嘿， 说白了就是补全， 补成完整的句子： 一是因为生成器的输出是离散的，梯度更新从判别器传到生成器比较困难；二是判别器只有当序列被完全生成后才能进行判断，但此刻指导用处已不太大，而如果生成器生成序列的同时判别器来判断，如何平衡当前序列的分数和未来序列的分数又是一个难题。
# 使用生成器参数初始化辨别器的参数。
rollout = ROLLOUT(generator, 0.8)
print ('#########################################################################')
print ('Start Adversarial Training...')
log.write('adversarial training...\n')
# 那么我们要开始进行训练了。 规则： 训练生成器一次； 训练辨别器五次。 平衡生成器与判别器。
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
# 训练一次生成器。
for it in range(1):
samples = generator.generate(sess)
# 基于生成器生成的数据 和 辨别器计算rewards。
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
# 基于rewards更新生成器模型参数
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
# 迭代五次，测试一次，测试流程跟上面一样哦。
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
print ('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
# Update roll-out parameters
# 记得用生成器的模型参数进行更新rollout。
rollout.update_params()
# Train the discriminator
# 训练辨别器五次。
for _ in range(5):
# 根据训练的生成器模型，生成句子。
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
# 将假的文本数据 加载到 辨别器数据模块， 用于辨别器的预训练。
dis_data_loader.load_train_data(positive_file, negative_file)
# 辨别器训练3次，重新生成假数据，接着训练辨别器。
for _ in range(3):
# 重置索引，从0开始。
dis_data_loader.reset_pointer()
# 读取每一个batchz-size， 训练辨别器。
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
# close
log.close()
if __name__ == '__main__':
main()
dataloader.py
import numpy as np
class Gen_Data_loader():
def __init__(self, batch_size):
self.batch_size = batch_size
self.token_stream = []
def create_batches(self, data_file):
self.token_stream = []
with open(data_file, 'r') as f:
for line in f:
line = line.strip()
line = line.split()
parse_line = [int(x) for x in line]
if len(parse_line) == 20:
self.token_stream.append(parse_line)
self.num_batch = int(len(self.token_stream) / self.batch_size)
self.token_stream = self.token_stream[:self.num_batch * self.batch_size]
self.sequence_batch = np.split(np.array(self.token_stream), self.num_batch, 0)
self.pointer = 0
def next_batch(self):
ret = self.sequence_batch[self.pointer]
self.pointer = (self.pointer + 1) % self.num_batch
return ret
def reset_pointer(self):
self.pointer = 0
class Dis_dataloader():
def __init__(self, batch_size):
self.batch_size = batch_size
self.sentences = np.array([])
self.labels = np.array([])
def load_train_data(self, positive_file, negative_file):
# Load data
positive_examples = []
negative_examples = []
with open(positive_file)as fin:
for line in fin:
line = line.strip()
line = line.split()
parse_line = [int(x) for x in line]
positive_examples.append(parse_line)
with open(negative_file)as fin:
for line in fin:
line = line.strip()
line = line.split()
parse_line = [int(x) for x in line]
if len(parse_line) == 20:
negative_examples.append(parse_line)
self.sentences = np.array(positive_examples + negative_examples)
# Generate labels
positive_labels = [[0, 1] for _ in positive_examples]
negative_labels = [[1, 0] for _ in negative_examples]
self.labels = np.concatenate([positive_labels, negative_labels], 0)
# Shuffle the data
shuffle_indices = np.random.permutation(np.arange(len(self.labels)))
self.sentences = self.sentences[shuffle_indices]
self.labels = self.labels[shuffle_indices]
# Split batches
self.num_batch = int(len(self.labels) / self.batch_size)
self.sentences = self.sentences[:self.num_batch * self.batch_size]
self.labels = self.labels[:self.num_batch * self.batch_size]
self.sentences_batches = np.split(self.sentences, self.num_batch, 0)
self.labels_batches = np.split(self.labels, self.num_batch, 0)
self.pointer = 0
def next_batch(self):
ret = self.sentences_batches[self.pointer], self.labels_batches[self.pointer]
self.pointer = (self.pointer + 1) % self.num_batch
return ret
def reset_pointer(self):
self.pointer = 0
discriminator.py
import tensorflow as tf
import numpy as np
# An alternative to tf.nn.rnn_cell._linear function, which has been removed in Tensorfow 1.0.1
# The highway layer is borrowed from https://github.com/mkroutikov/tf-lstm-char-cnn
def linear(input_, output_size, scope=None):
'''
Linear map: output[k] = sum_i(Matrix[k, i] * input_[i] ) + Bias[k]
Args:
input_: a tensor or a list of 2D, batch x n, Tensors.
output_size: int, second dimension of W[i].
scope: VariableScope for the created subgraph; defaults to "Linear".
Returns:
A 2D Tensor with shape [batch x output_size] equal to
sum_i(input_[i] * W[i]), where W[i]s are newly created matrices.
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
'''
shape = input_.get_shape().as_list()
if len(shape) != 2:
raise ValueError("Linear is expecting 2D arguments: %s" % str(shape))
if not shape[1]:
raise ValueError("Linear expects shape[1] of arguments: %s" % str(shape))
input_size = shape[1]
# Now the computation.
with tf.variable_scope(scope or "SimpleLinear"):
matrix = tf.get_variable("Matrix", [output_size, input_size], dtype=input_.dtype)
bias_term = tf.get_variable("Bias", [output_size], dtype=input_.dtype)
return tf.matmul(input_, tf.transpose(matrix)) + bias_term
def highway(input_, size, num_layers=1, bias=-2.0, f=tf.nn.relu, scope='Highway'):
"""Highway Network (cf. http://arxiv.org/abs/1505.00387).
t = sigmoid(Wy + b)
z = t * g(Wy + b) + (1 - t) * y
where g is nonlinearity, t is transform gate, and (1 - t) is carry gate.
"""
with tf.variable_scope(scope):
for idx in range(num_layers):
g = f(linear(input_, size, scope='highway_lin_%d' % idx))
t = tf.sigmoid(linear(input_, size, scope='highway_gate_%d' % idx) + bias)
output = t * g + (1. - t) * input_
input_ = output
return output
class Discriminator(object):
"""
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
"""
def __init__(
self, sequence_length, num_classes, vocab_size,
embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
with tf.variable_scope('discriminator'):
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(
tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
name="W")
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for filter_size, num_filter in zip(filter_sizes, num_filters):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, embedding_size, 1, num_filter]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_filter]), name="b")
conv = tf.nn.conv2d(
self.embedded_chars_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
# Maxpooling over the outputs
pooled = tf.nn.max_pool(
h,
ksize=[1, sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool")
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = sum(num_filters)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# Add highway
with tf.name_scope("highway"):
self.h_highway = highway(self.h_pool_flat, self.h_pool_flat.get_shape()[1], 1, 0)
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.h_highway, self.dropout_keep_prob)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W = tf.Variable(tf.truncated_normal([num_filters_total, num_classes], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
self.ypred_for_auc = tf.nn.softmax(self.scores)
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# CalculateMean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
self.params = [param for param in tf.trainable_variables() if 'discriminator' in param.name]
d_optimizer = tf.train.AdamOptimizer(1e-4)
grads_and_vars = d_optimizer.compute_gradients(self.loss, self.params, aggregation_method=2)
self.train_op = d_optimizer.apply_gradients(grads_and_vars)
generator.py
import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
class Generator(object):
def __init__(self, num_emb, batch_size, emb_dim, hidden_dim,
sequence_length, start_token,
learning_rate=0.01, reward_gamma=0.95):
self.num_emb = num_emb
self.batch_size = batch_size
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.sequence_length = sequence_length
self.start_token = tf.constant([start_token] * self.batch_size, dtype=tf.int32)
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.reward_gamma = reward_gamma
self.g_params = []
self.d_params = []
self.temperature = 1.0
self.grad_clip = 5.0
self.expected_reward = tf.Variable(tf.zeros([self.sequence_length]))
with tf.variable_scope('generator'):
self.g_embeddings = tf.Variable(self.init_matrix([self.num_emb, self.emb_dim]))
self.g_params.append(self.g_embeddings)
self.g_recurrent_unit = self.create_recurrent_unit(self.g_params) # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit(self.g_params) # maps h_t to o_t (output token logits)
# placeholder definition
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.sequence_length]) # sequence of tokens generated by generator
self.rewards = tf.placeholder(tf.float32, shape=[self.batch_size, self.sequence_length]) # get from rollout policy and discriminator
# processed for batch
with tf.device("/cpu:0"):
self.processed_x = tf.transpose(tf.nn.embedding_lookup(self.g_embeddings, self.x), perm=[1, 0, 2]) # seq_length x batch_size x emb_dim
# Initial states
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = tf.stack([self.h0, self.h0])
gen_o = tensor_array_ops.TensorArray(dtype=tf.float32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
# sequence2sequence的套路。。。
def _g_recurrence(i, x_t, h_tm1, gen_o, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
o_t = self.g_output_unit(h_t) # batch x vocab , logits not prob
log_prob = tf.log(tf.nn.softmax(o_t))
next_token = tf.cast(tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]), tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings, next_token) # batch x emb_dim
gen_o = gen_o.write(i, tf.reduce_sum(tf.multiply(tf.one_hot(next_token, self.num_emb, 1.0, 0.0),
tf.nn.softmax(o_t)), 1)) # [batch_size] , prob
gen_x = gen_x.write(i, next_token) # indices, batch_size
return i + 1, x_tp1, h_t, gen_o, gen_x
_, _, _, self.gen_o, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4: i < self.sequence_length,
body=_g_recurrence,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, gen_o, gen_x))
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x, perm=[1, 0]) # batch_size x seq_length
# supervised pretraining for generator
g_predictions = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
# 中规中矩的RNN。
def _pretrain_recurrence(i, x_t, h_tm1, g_predictions):
h_t = self.g_recurrent_unit(x_t, h_tm1)
o_t = self.g_output_unit(h_t)
g_predictions = g_predictions.write(i, tf.nn.softmax(o_t)) # batch x vocab_size
x_tp1 = ta_emb_x.read(i)
return i + 1, x_tp1, h_t, g_predictions
_, _, _, self.g_predictions = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3: i < self.sequence_length,
body=_pretrain_recurrence,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token),
self.h0, g_predictions))
self.g_predictions = tf.transpose(self.g_predictions.stack(), perm=[1, 0, 2]) # batch_size x seq_length x vocab_size
# pretraining loss
self.pretrain_loss = -tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.clip_by_value(tf.reshape(self.g_predictions, [-1, self.num_emb]), 1e-20, 1.0)
)
) / (self.sequence_length * self.batch_size)
# training updates
pretrain_opt = self.g_optimizer(self.learning_rate)
self.pretrain_grad, _ = tf.clip_by_global_norm(tf.gradients(self.pretrain_loss, self.g_params), self.grad_clip)
self.pretrain_updates = pretrain_opt.apply_gradients(zip(self.pretrain_grad, self.g_params))
#######################################################################################################
# Unsupervised Training
#######################################################################################################
self.g_loss = -tf.reduce_sum(
tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.clip_by_value(tf.reshape(self.g_predictions, [-1, self.num_emb]), 1e-20, 1.0)
), 1) * tf.reshape(self.rewards, [-1])
)
g_opt = self.g_optimizer(self.learning_rate)
self.g_grad, _ = tf.clip_by_global_norm(tf.gradients(self.g_loss, self.g_params), self.grad_clip)
self.g_updates = g_opt.apply_gradients(zip(self.g_grad, self.g_params))
def generate(self, sess):
outputs = sess.run(self.gen_x)
return outputs
def pretrain_step(self, sess, x):
outputs = sess.run([self.pretrain_updates, self.pretrain_loss], feed_dict={self.x: x})
return outputs
def init_matrix(self, shape):
return tf.random_normal(shape, stddev=0.1)
def init_vector(self, shape):
return tf.zeros(shape)
def create_recurrent_unit(self, params):
# Weights and Bias for input and hidden tensor
self.Wi = tf.Variable(self.init_matrix([self.emb_dim, self.hidden_dim]))
self.Ui = tf.Variable(self.init_matrix([self.hidden_dim, self.hidden_dim]))
self.bi = tf.Variable(self.init_matrix([self.hidden_dim]))
self.Wf = tf.Variable(self.init_matrix([self.emb_dim, self.hidden_dim]))
self.Uf = tf.Variable(self.init_matrix([self.hidden_dim, self.hidden_dim]))
self.bf = tf.Variable(self.init_matrix([self.hidden_dim]))
self.Wog = tf.Variable(self.init_matrix([self.emb_dim, self.hidden_dim]))
self.Uog = tf.Variable(self.init_matrix([self.hidden_dim, self.hidden_dim]))
self.bog = tf.Variable(self.init_matrix([self.hidden_dim]))
self.Wc = tf.Variable(self.init_matrix([self.emb_dim, self.hidden_dim]))
self.Uc = tf.Variable(self.init_matrix([self.hidden_dim, self.hidden_dim]))
self.bc = tf.Variable(self.init_matrix([self.hidden_dim]))
params.extend([
self.Wi, self.Ui, self.bi,
self.Wf, self.Uf, self.bf,
self.Wog, self.Uog, self.bog,
self.Wc, self.Uc, self.bc])
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def create_output_unit(self, params):
self.Wo = tf.Variable(self.init_matrix([self.hidden_dim, self.num_emb]))
self.bo = tf.Variable(self.init_matrix([self.num_emb]))
params.extend([self.Wo, self.bo])
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
def g_optimizer(self, *args, **kwargs):
return tf.train.AdamOptimizer(*args, **kwargs)
rollout.py
import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
import numpy as np
class ROLLOUT(object):
def __init__(self, lstm, update_rate):
self.lstm = lstm
self.update_rate = update_rate
self.num_emb = self.lstm.num_emb
self.batch_size = self.lstm.batch_size
self.emb_dim = self.lstm.emb_dim
self.hidden_dim = self.lstm.hidden_dim
self.sequence_length = self.lstm.sequence_length
self.start_token = tf.identity(self.lstm.start_token)
self.learning_rate = self.lstm.learning_rate
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = self.create_recurrent_unit() # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit() # maps h_t to o_t (output token logits)
#####################################################################################################
# placeholder definition
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.sequence_length]) # sequence of tokens generated by generator
self.given_num = tf.placeholder(tf.int32)
# processed for batch
with tf.device("/cpu:0"):
self.processed_x = tf.transpose(tf.nn.embedding_lookup(self.g_embeddings, self.x), perm=[1, 0, 2]) # seq_length x batch_size x emb_dim
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
ta_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length)
ta_x = ta_x.unstack(tf.transpose(self.x, perm=[1, 0]))
#####################################################################################################
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = tf.stack([self.h0, self.h0])
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
# When current index i < given_num, use the provided tokens as the input at each time step
def _g_recurrence_1(i, x_t, h_tm1, given_num, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
x_tp1 = ta_emb_x.read(i)
gen_x = gen_x.write(i, ta_x.read(i))
return i + 1, x_tp1, h_t, given_num, gen_x
# When current index i >= given_num, start roll-out, use the output as time step t as the input at time step t+1
def _g_recurrence_2(i, x_t, h_tm1, given_num, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
o_t = self.g_output_unit(h_t) # batch x vocab , logits not prob
log_prob = tf.log(tf.nn.softmax(o_t))
next_token = tf.cast(tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]), tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings, next_token) # batch x emb_dim
gen_x = gen_x.write(i, next_token) # indices, batch_size
return i + 1, x_tp1, h_t, given_num, gen_x
i, x_t, h_tm1, given_num, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, given_num, _4: i < given_num,
body=_g_recurrence_1,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, self.given_num, gen_x))
_, _, _, _, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4: i < self.sequence_length,
body=_g_recurrence_2,
loop_vars=(i, x_t, h_tm1, given_num, self.gen_x))
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x, perm=[1, 0]) # batch_size x seq_length
def get_reward(self, sess, input_x, rollout_num, discriminator):
rewards = []
for i in range(rollout_num):
# given_num between 1 to sequence_length - 1 for a part completed sentence
for given_num in range(1, self.sequence_length ):
feed = {self.x: input_x, self.given_num: given_num}
samples = sess.run(self.gen_x, feed)
feed = {discriminator.input_x: samples, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
rewards[given_num - 1] += ypred
# the last token reward
feed = {discriminator.input_x: input_x, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
# completed sentence reward
rewards[self.sequence_length - 1] += ypred
rewards = np.transpose(np.array(rewards)) / (1.0 * rollout_num) # batch_size x seq_length
return rewards
def create_recurrent_unit(self):
# Weights and Bias for input and hidden tensor
self.Wi = tf.identity(self.lstm.Wi)
self.Ui = tf.identity(self.lstm.Ui)
self.bi = tf.identity(self.lstm.bi)
self.Wf = tf.identity(self.lstm.Wf)
self.Uf = tf.identity(self.lstm.Uf)
self.bf = tf.identity(self.lstm.bf)
self.Wog = tf.identity(self.lstm.Wog)
self.Uog = tf.identity(self.lstm.Uog)
self.bog = tf.identity(self.lstm.bog)
self.Wc = tf.identity(self.lstm.Wc)
self.Uc = tf.identity(self.lstm.Uc)
self.bc = tf.identity(self.lstm.bc)
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def update_recurrent_unit(self):
# Weights and Bias for input and hidden tensor
self.Wi = self.update_rate * self.Wi + (1 - self.update_rate) * tf.identity(self.lstm.Wi)
self.Ui = self.update_rate * self.Ui + (1 - self.update_rate) * tf.identity(self.lstm.Ui)
self.bi = self.update_rate * self.bi + (1 - self.update_rate) * tf.identity(self.lstm.bi)
self.Wf = self.update_rate * self.Wf + (1 - self.update_rate) * tf.identity(self.lstm.Wf)
self.Uf = self.update_rate * self.Uf + (1 - self.update_rate) * tf.identity(self.lstm.Uf)
self.bf = self.update_rate * self.bf + (1 - self.update_rate) * tf.identity(self.lstm.bf)
self.Wog = self.update_rate * self.Wog + (1 - self.update_rate) * tf.identity(self.lstm.Wog)
self.Uog = self.update_rate * self.Uog + (1 - self.update_rate) * tf.identity(self.lstm.Uog)
self.bog = self.update_rate * self.bog + (1 - self.update_rate) * tf.identity(self.lstm.bog)
self.Wc = self.update_rate * self.Wc + (1 - self.update_rate) * tf.identity(self.lstm.Wc)
self.Uc = self.update_rate * self.Uc + (1 - self.update_rate) * tf.identity(self.lstm.Uc)
self.bc = self.update_rate * self.bc + (1 - self.update_rate) * tf.identity(self.lstm.bc)
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def create_output_unit(self):
self.Wo = tf.identity(self.lstm.Wo)
self.bo = tf.identity(self.lstm.bo)
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
def update_output_unit(self):
self.Wo = self.update_rate * self.Wo + (1 - self.update_rate) * tf.identity(self.lstm.Wo)
self.bo = self.update_rate * self.bo + (1 - self.update_rate) * tf.identity(self.lstm.bo)
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
def update_params(self):
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = self.update_recurrent_unit()
self.g_output_unit = self.update_output_unit()
target_lstm.py
import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
class TARGET_LSTM(object):
def __init__(self, num_emb, batch_size, emb_dim, hidden_dim, sequence_length, start_token, params):
self.num_emb = num_emb
self.batch_size = batch_size
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.sequence_length = sequence_length
self.start_token = tf.constant([start_token] * self.batch_size, dtype=tf.int32)
self.g_params = []
self.temperature = 1.0
self.params = params
tf.set_random_seed(66)
with tf.variable_scope('generator'):
self.g_embeddings = tf.Variable(self.params[0])
self.g_params.append(self.g_embeddings)
self.g_recurrent_unit = self.create_recurrent_unit(self.g_params) # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit(self.g_params) # maps h_t to o_t (output token logits)
# placeholder definition
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.sequence_length]) # sequence of tokens generated by generator
# processed for batch
with tf.device("/cpu:0"):
self.processed_x = tf.transpose(tf.nn.embedding_lookup(self.g_embeddings, self.x), perm=[1, 0, 2]) # seq_length x batch_size x emb_dim
# initial states
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = tf.stack([self.h0, self.h0])
# generator on initial randomness
gen_o = tensor_array_ops.TensorArray(dtype=tf.float32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
def _g_recurrence(i, x_t, h_tm1, gen_o, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
o_t = self.g_output_unit(h_t) # batch x vocab , logits not prob
log_prob = tf.log(tf.nn.softmax(o_t))
next_token = tf.cast(tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]), tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings, next_token) # batch x emb_dim
gen_o = gen_o.write(i, tf.reduce_sum(tf.multiply(tf.one_hot(next_token, self.num_emb, 1.0, 0.0),
tf.nn.softmax(o_t)), 1)) # [batch_size] , prob
gen_x = gen_x.write(i, next_token) # indices, batch_size
return i + 1, x_tp1, h_t, gen_o, gen_x
_, _, _, self.gen_o, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4: i < self.sequence_length,
body=_g_recurrence,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, gen_o, gen_x)
)
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x, perm=[1, 0]) # batch_size x seq_length
# supervised pretraining for generator
g_predictions = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
def _pretrain_recurrence(i, x_t, h_tm1, g_predictions):
h_t = self.g_recurrent_unit(x_t, h_tm1)
o_t = self.g_output_unit(h_t)
g_predictions = g_predictions.write(i, tf.nn.softmax(o_t)) # batch x vocab_size
x_tp1 = ta_emb_x.read(i)
return i + 1, x_tp1, h_t, g_predictions
_, _, _, self.g_predictions = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3: i < self.sequence_length,
body=_pretrain_recurrence,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token),
self.h0, g_predictions))
self.g_predictions = tf.transpose(
self.g_predictions.stack(), perm=[1, 0, 2]) # batch_size x seq_length x vocab_size
# pretraining loss
self.pretrain_loss = -tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.reshape(self.g_predictions, [-1, self.num_emb]))) / (self.sequence_length * self.batch_size)
self.out_loss = tf.reduce_sum(
tf.reshape(
-tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.reshape(self.g_predictions, [-1, self.num_emb])), 1
), [-1, self.sequence_length]
), 1
) # batch_size
def generate(self, session):
# h0 = np.random.normal(size=self.hidden_dim)
outputs = session.run(self.gen_x)
return outputs
def init_matrix(self, shape):
return tf.random_normal(shape, stddev=1.0)
def create_recurrent_unit(self, params):
# Weights and Bias for input and hidden tensor
self.Wi = tf.Variable(self.params[1])
self.Ui = tf.Variable(self.params[2])
self.bi = tf.Variable(self.params[3])
self.Wf = tf.Variable(self.params[4])
self.Uf = tf.Variable(self.params[5])
self.bf = tf.Variable(self.params[6])
self.Wog = tf.Variable(self.params[7])
self.Uog = tf.Variable(self.params[8])
self.bog = tf.Variable(self.params[9])
self.Wc = tf.Variable(self.params[10])
self.Uc = tf.Variable(self.params[11])
self.bc = tf.Variable(self.params[12])
params.extend([
self.Wi, self.Ui, self.bi,
self.Wf, self.Uf, self.bf,
self.Wog, self.Uog, self.bog,
self.Wc, self.Uc, self.bc])
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def create_output_unit(self, params):
self.Wo = tf.Variable(self.params[13])
self.bo = tf.Variable(self.params[14])
params.extend([self.Wo, self.bo])
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit
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数据挖掘十大算法
（美）吴信东（Xindong Wu）、（美），库玛尔 ，（Vipin Kumar） / 李文波、吴素研 / 清华大学出版社 / 2013-5 / 39.00元
《世界著名计算机教材精选：数据挖掘十大算法》详细介绍了在实际中用途最广、影响最大的十种数据挖掘算法，这十种算法是数据挖掘领域的顶级专家进行投票筛选的，覆盖了分类、聚类、统计学习、关联分析和链接分析等重要的数据挖掘研究和发展主题。《世界著名计算机教材精选：数据挖掘十大算法》对每一种算法都进行了多个角度的深入剖析，包括算法历史、算法过程、算法特性、软件实现、前沿发展等，此外，在每章最后还给出了丰富的习......一起来看看 《数据挖掘十大算法》 这本书的介绍吧!
