self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions,
tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions,
"float"),
name="accuracy")
# data loading
data_path = '/home/ubuntu/RRing/ratings_test_22.csv'
contents, points = tool.loading_rdata(data_path)
contents = tool.cut(contents, cut=2)
# tranform document to vector
max_document_length = 200
x, vocabulary, vocab_size = tool.make_input(contents, max_document_length)
#print(vocab_size)
#print(vocabulary)
#print('사전단어수 : %s' % (vocab_size))
y = tool.make_output(points,
threshold=0.5) # 긍정 부정으로 0, 1로만 구분되어 있어서 0.5 를 선택함
# divide dataset into train/test set
#x_train, x_test, y_train, y_test = tool.divide(x,y,train_prop=0.8) # 80% 는 train 20% 는 test
def train():
# # data loading
data_path = 'preprocessed_POSCO.csv' # csv 파일로 불러오기
# 포스코 모델
# data_path = 'repro_45.csv' # csv 파일로 불러오기
contents, points = tool.loading_rdata(data_path) # CSV 읽어오기
vocab_list = tool.cut(contents) # contents 에 모든 기사들을 1개의 리스트에 통합
# transform document to vector
max_document_length = 1400
x, vocabulary, vocab_size = tool.make_vocab(vocab_list,
max_document_length)
tool.save_vocab('news_vocab_sk.txt', contents, max_document_length)
# tool.save_vocab('news_vocab_posco.txt', vocabulary, max_document_length)
# vocab = tool.load_vocab('news_vocab_sk.txt')
print('사전단어수 : %s' % (vocab_size))
y = tool.make_output(points, threshold=0)
# divide dataset into train/test set
x_train, x_test, y_train, y_test = tool.divide(x, y, train_prop=0.9)
# Model Hyperparameters
flags.DEFINE_integer('embedding_dim', 128,
"Dimensionality of embedded vector (default: 128)")
flags.DEFINE_string('filter_sizes', '3,4,5',
"Comma-separated filter sizes (default: '3,4,5')")
flags.DEFINE_integer('num_filters', 128,
"Number of filters per filter size (default: 128)")
flags.DEFINE_float('dropout_keep_prob', 0.5,
"Dropout keep probability (default: 0.5)")
flags.DEFINE_float('l2_reg_lambda', 0.1,
"L2 regularization lambda (default: 0.0)")
# Training parameters
flags.DEFINE_integer("batch_size", 128, "Batch Size (default: 64)")
flags.DEFINE_integer("num_epochs", 200,
"Number of training epochs (default: 200)")
flags.DEFINE_integer(
"evaluate_every", 100,
"Evaluate model on dev set after this many steps (default: 100)")
flags.DEFINE_integer("checkpoint_every", 100,
"Save model after this many steps (default: 100)")
flags.DEFINE_integer("num_checkpoints", 10,
"Number of checkpoints to store (default: 5)")
# Misc Parameters
flags.DEFINE_boolean("allow_soft_placement", True,
"Allow device soft device placement")
flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
FLAGS = flags.FLAGS
# print('\nParameters : ')
# for attr, value in sorted(FLAGS.flag_values_dict()):
# print('{}={}'.format(attr.upper(), value))
# print('')
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
cnn = TextCNN(sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=vocab_size,
embedding_size=FLAGS.embedding_dim,
filter_sizes=list(
map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda)
# cnn = CharCNN()
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in grads_and_vars:
if g is not None:
grad_hist_summary = tf.summary.histogram(
"{}".format(v.name), g)
sparsity_summary = tf.summary.scalar(
"{}".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", cnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)
# 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=FLAGS.num_checkpoints)
# Initialize all variables
sess.run(tf.global_variables_initializer())
# initW = tool.load_embedding_vectors(vocabulary)
# sess.run(cnn.W.assign(initW))
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run([
train_op, global_step, train_summary_op, cnn.loss,
cnn.accuracy
], feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, 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 = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy = sess.run(
[global_step, dev_summary_op, cnn.loss, cnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
if writer:
writer.add_summary(summaries, step)
def batch_iter(data, batch_size, num_epochs, shuffle=True):
"""
Generates a batch iterator for a dataset.
"""
data = np.array(data)
data_size = len(data)
num_batches_per_epoch = int((len(data) - 1) / batch_size) + 1
for epoch in range(num_epochs):
# Shuffle the data at each epoch
if shuffle:
shuffle_indices = np.random.permutation(
np.arange(data_size))
shuffled_data = data[shuffle_indices]
else:
shuffled_data = data
for batch_num in range(num_batches_per_epoch):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size,
data_size)
yield shuffled_data[start_index:end_index]
# Generate batches
batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size,
FLAGS.num_epochs)
testpoint = 0
# Training loop. For each batch...
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 % FLAGS.evaluate_every == 0:
if testpoint + 100 < len(x_test):
testpoint += 100
else:
testpoint = 0
print("\nEvaluation:")
dev_step(x_test[testpoint:testpoint + 100],
y_test[testpoint:testpoint + 100],
writer=dev_summary_writer)
print("")
if current_step % FLAGS.checkpoint_every == 0:
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
x_train_genres[i][idx] = 1
# company one-hot encode
x_train_companies = np.zeros((row_count, len(companies_set)))
for i in range(len(x_companies_text)):
companies_array = ast.literal_eval(x_companies_text[i])
for company in companies_array:
try:
idx = companies_set.index(company['id'])
x_train_companies[i][idx] = 1
except:
pass
# Text 준비
contents = tool.cut(x_train_text, 2)
max_document_length = 256
x, vocabulary, vocab_size = tool.make_input(contents,max_document_length)
x_train, x_test, y_train, y_test = x[list(range(5000))+list(range(-2500,0))], x[5000:6000], y_train_point[list(range(5000))+list(range(-2500,0))], y_train_point[5000:6000]
#train reshape
x_train = x_train.astype(np.float32)
x_train = x_train.reshape(x_train.shape[0], 16, 16, 1)
y_train = y_train.reshape(y_train.shape[0], 1)
#test reshape
x_test = x_test.astype(np.float32)
x_test = x_test.reshape(x_test.shape[0], 16, 16, 1)
y_test = y_test.reshape(y_test.shape[0], 1)
losses = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions,
tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions,
"float"),
name="accuracy")
data_path = './data.csv'
contents, cls = tool.loading_rdata(data_path, eng=True, num=True, punc=False)
contents = tool.cut(contents)
max_document_length = 10
x, vocabulary, vocab_size = tool.make_input(contents, max_document_length)
print('사전단어수 : %s' % (vocab_size))
y = tool.make_output(cls)
x_train, x_test, y_train, y_test = tool.divide(x, y, train_prop=1)
# tranform document to vector
# train 데이터와 dev데이터를 나눠주는 역활을한다.
# 다른코드는 파일로 저장을하는데 여기서는 그냥 함수콜을 해서 리턴해서 로컬변수로 저장하는듯함
# 데이터 타입과 value들을 말해주는데 fllags의 모든 항목들을 보여준다.
# 3. train the model and test
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
cnn = TextCNN(sequence_length=x_train.shape[1],
# sparse_cross_entropy_with_logits
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels= self.input_y)# 아웃풋 건드는것
#losses = tf.nn.sparse_cross_entropy_with_logits(logits=self.predictions, labels= self.input_y)# 아웃풋 건드는것
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
#loss 건드는것
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
# data loading
data_path='./data.csv' # csv파일 경로지정
contents, cls = tool.loading_rdata(data_path, eng=True, num=True, punc=False)#여기서 csv파일을
#읽어서 내용과 판결 calssyfi를 리스트화 시킨다.
contents = tool.cut(contents)# 문서에 있는 단어를 tokenization한 모든 문서 개수 리스트를 반환
# tranform document to vector
max_document_length = 10# 여기서 10은 내가 이미 만들어놓은 데이터가 10개씩 잘려있기 때문에 이렇게 주어줌
x, vocabulary, vocab_size = tool.make_input(contents,max_document_length)
# 이것은 그냥 단순히 10개로 한정해서 사전과 사전단어개수와 input 리스트를 리턴하는 함수임으로
# 다른 코드와는 다른점이 있다. 유의
print('사전단어수 : %s' % (vocab_size))
y = tool.make_output(cls)
# cassify해주는 함수이다. y값을 1,0,0 /0,1,0 /0,0,1로 임베딩해서 아웃풋해놓음
# divide dataset into train/test set
x_train, x_test, y_train, y_test = tool.divide(x,y,train_prop=0.8)
# train 데이터와 dev데이터를 나눠주는 역활을한다.
# 다른코드는 파일로 저장을하는데 여기서는 그냥 함수콜을 해서 리턴해서 로컬변수로 저장하는듯함
# Model Hyperparameters