def train(train_dir, val_dir, labels_file, word2vec_path, batch_size,
max_steps, log_step, val_step, snapshot, out_dir):
'''
训练...
:param train_dir: 训练数据目录
:param val_dir: val数据目录
:param labels_file: labels文件目录
:param word2vec_path: 词向量模型文件
:param batch_size: batch size
:param max_steps: 最大迭代次数
:param log_step: log显示间隔
:param val_step: 测试间隔
:param snapshot: 保存模型间隔
:param out_dir: 模型ckpt和summaries输出的目录
:return:
'''
max_sentence_length = 300
embedding_dim = 50
filter_sizes = [3, 4, 5, 6]
num_filters = 200 # Number of filters per filter size
base_lr = 0.001 # 学习率
dropout_keep_prob = 0.5
l2_reg_lambda = 0.0 # "L2 regularization lambda (default: 0.0)
allow_soft_placement = True # 如果你指定的设备不存在，允许TF自动分配设备
log_device_placement = False # 是否打印设备分配日志
print("Loading data...")
w2vModel = create_word2vec.load_wordVectors(word2vec_path)
labels_set = files_processing.read_txt(labels_file)
labels_nums = len(labels_set)
train_file_list = create_batch_data.get_file_list(file_dir=train_dir,
postfix='*.npy')
train_batch = create_batch_data.get_data_batch(train_file_list,
labels_nums=labels_nums,
batch_size=batch_size,
shuffle=False,
one_hot=True)
val_file_list = create_batch_data.get_file_list(file_dir=val_dir,
postfix='*.npy')
val_batch = create_batch_data.get_data_batch(val_file_list,
labels_nums=labels_nums,
batch_size=batch_size,
shuffle=False,
one_hot=True)
print("train data info *****************************")
train_nums = create_word2vec.info_npy(train_file_list)
print("val data info *****************************")
val_nums = create_word2vec.info_npy(val_file_list)
print("labels_set info *****************************")
files_processing.info_labels_set(labels_set)
# Training
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=allow_soft_placement,
log_device_placement=log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn = TextCNN(sequence_length=max_sentence_length,
num_classes=labels_nums,
embedding_size=embedding_dim,
filter_sizes=filter_sizes,
num_filters=num_filters,
l2_reg_lambda=l2_reg_lambda)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=base_lr)
# optimizer = tf.train.MomentumOptimizer(learning_rate=0.01, momentum=0.9)
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(
"{}/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
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=3)
# Initialize all variables
sess.run(tf.global_variables_initializer())
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: dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run([
train_op, global_step, train_summary_op, cnn.loss,
cnn.accuracy
], feed_dict)
if step % log_step == 0:
print("training: 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 = {
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)
if writer:
writer.add_summary(summaries, step)
return loss, accuracy
for i in range(max_steps):
train_batch_data, train_batch_label = create_batch_data.get_next_batch(
train_batch)
train_batch_data = create_word2vec.indexMat2vector_lookup(
w2vModel, train_batch_data)
train_step(train_batch_data, train_batch_label)
current_step = tf.train.global_step(sess, global_step)
if current_step % val_step == 0:
val_losses = []
val_accs = []
# for k in range(int(val_nums/batch_size)):
for k in range(100):
val_batch_data, val_batch_label = create_batch_data.get_next_batch(
val_batch)
val_batch_data = create_word2vec.indexMat2vector_lookup(
w2vModel, val_batch_data)
val_loss, val_acc = dev_step(val_batch_data,
val_batch_label,
writer=dev_summary_writer)
val_losses.append(val_loss)
val_accs.append(val_acc)
mean_loss = np.array(val_losses, dtype=np.float32).mean()
mean_acc = np.array(val_accs, dtype=np.float32).mean()
print("--------Evaluation:step {}, loss {:g}, acc {:g}".
format(current_step, mean_loss, mean_acc))
if current_step % snapshot == 0:
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
print('indexMat1:\n{}'.format(indexMat1))
print('indexMat2:\n{}'.format(indexMat2))
print('indexMat3:\n{}'.format(indexMat3))
if __name__ == '__main__':
word2vec_path = "../../word2vec/models/THUCNews_word2Vec/THUCNews_word2Vec_128.model"
train_out_dir = '../data/train_data'
labels_file = '../data/THUCNews_labels.txt'
labels_set = files_processing.read_txt(labels_file)
files_processing.info_labels_set(labels_set)
# create_test_data(out_dir)
file_list = get_file_list(file_dir=train_out_dir, postfix='*.npy')
create_word2vec.info_npy(file_list)
iter = 5 # 迭代3次，每次输出一个batch个
labels_nums = len(labels_set)
batch = get_data_batch(file_list,
labels_nums=14,
batch_size=6,
shuffle=False,
one_hot=False)
for i in range(iter):
print('**************************')
batch_indexMat, batch_labels = get_next_batch(batch)
# 解码：将索引矩阵解码为字词
# word2vec_path = 'out/trained_word2vec.model'
w2vModel = create_word2vec.load_wordVectors(word2vec_path)
def batch_predict(val_dir, labels_file, models_path, word2vec_path,
batch_size):
'''
预测...
:param val_dir: val数据目录
:param labels_file: labels文件目录
:param models_path: 模型文件
:param word2vec_path: 词向量模型文件
:param batch_size: batch size
:return:
'''
max_sentence_length = 300
embedding_dim = 128
filter_sizes = [3, 4, 5, 6]
num_filters = 200 # Number of filters per filter size
l2_reg_lambda = 0.0 # "L2 regularization lambda (default: 0.0)
print("Loading data...")
w2vModel = create_word2vec.load_wordVectors(word2vec_path)
labels_set = files_processing.read_txt(labels_file)
labels_nums = len(labels_set)
val_file_list = create_batch_data.get_file_list(file_dir=val_dir,
postfix='*.npy')
val_batch = create_batch_data.get_data_batch(val_file_list,
labels_nums=labels_nums,
batch_size=batch_size,
shuffle=False,
one_hot=True)
print("val data info *****************************")
val_nums = create_word2vec.info_npy(val_file_list)
print("labels_set info *****************************")
files_processing.info_labels_set(labels_set)
# Training
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
cnn = TextCNN(sequence_length=max_sentence_length,
num_classes=labels_nums,
embedding_size=embedding_dim,
filter_sizes=filter_sizes,
num_filters=num_filters,
l2_reg_lambda=l2_reg_lambda)
# Initialize all variables
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path)
def dev_step(x_batch, y_batch):
"""
Evaluates model on a dev set
"""
feed_dict = {
cnn.input_x: x_batch,
cnn.input_y: y_batch,
cnn.dropout_keep_prob: 1.0
}
loss, accuracy = sess.run([cnn.loss, cnn.accuracy], feed_dict)
return loss, accuracy
val_losses = []
val_accs = []
for k in range(int(val_nums / batch_size)):
# for k in range(int(10)):
val_batch_data, val_batch_label = create_batch_data.get_next_batch(
val_batch)
val_batch_data = create_word2vec.indexMat2vector_lookup(
w2vModel, val_batch_data)
val_loss, val_acc = dev_step(val_batch_data, val_batch_label)
val_losses.append(val_loss)
val_accs.append(val_acc)
print("--------Evaluation:step {}, loss {:g}, acc {:g}".format(
k, val_loss, val_acc))
mean_loss = np.array(val_losses, dtype=np.float32).mean()
mean_acc = np.array(val_accs, dtype=np.float32).mean()
print("--------Evaluation:step {}, mean loss {:g}, mean acc {:g}".
format(k, mean_loss, mean_acc))