def train_rnn():
x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb'))
x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb'))
vocab_processer = learn.preprocessing.VocabularyProcessor.restore(FLAGS.vocab_path)
train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# 构建rnn 节点
rnns = RNNsClassification(
embedding_mat=None,
embedding_dims=FLAGS.embedding_dim,
vocab_size=len(vocab_processer.vocabulary_),
non_static=False,
hidden_unit=FLAGS.hidden_unit,
sequence_length=FLAGS.sequence_length,
num_tags=FLAGS.num_tags,
cell=FLAGS.cell,
num_layers=FLAGS.num_layer,
l2_reg_lambda=FLAGS.l2_reg_lambda)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
# 优化算法
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(rnns.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
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(FLAGS.out_dir, "runs_rnn", timestamp))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", rnns.loss)
acc_summary = tf.summary.scalar("accuracy", rnns.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)
# Write vocabulary
# vocab_proccesser.save(os.path.join(out_dir, "vocab"))
# Initialize all variables
sess.run(tf.global_variables_initializer())
def train_step(x_batch, y_batch):
"""
A single training step
"""
feed_dict = {
rnns.input_x: x_batch,
rnns.input_y: y_batch,
rnns.dropout_keep_prob: FLAGS.dropout_keep_prob
}
# 执行 节点操作
_, step, summaries, loss, accuracy = sess.run(
[train_op, global_step, train_summary_op, rnns.loss, rnns.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
if step % 20 == 0:
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 = {
rnns.input_x: x_batch,
rnns.input_y: y_batch,
rnns.dropout_keep_prob: 1.0
}
step, summaries, loss, accuracy, correct = sess.run(
[global_step, dev_summary_op, rnns.loss, rnns.accuracy, rnns.num_correct],
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)
return loss, accuracy, correct
# Generate batches
# batches = data_helper.batch_iter(
# list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
best_acc = 0.0
best_step = 0
for batch in train_batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
# 更新全局步数
current_step = tf.train.global_step(sess, global_step)
# 计算评估结果
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
total_dev_correct = 0
dev_batches = batch_iter(list(zip(x_dev, y_dev)), FLAGS.batch_size, 1)
for dev_batch in dev_batches:
x_dev_batch, y_dev_batch = zip(*dev_batch)
acc, loss, num_dev_correct = dev_step(x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
accuracy = float(total_dev_correct) / len(y_dev)
print('Accuracy on dev set: {}'.format(accuracy))
# loss_, accuracy_ = dev_step(x_dev, y_dev, writer=dev_summary_writer)
if accuracy > best_acc:
best_acc = accuracy
best_step = current_step
# 保存模型计算结果
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
print("")
print('\nBset dev at {}, accuray {:g}'.format(best_step, best_acc))
def train_cnns():
"""
训练CNNs模型用于文本分类
:return:
"""
# Parameters
# ==================================================
# Data loading params
tf.flags.DEFINE_float(
"train_size", .1,
"Percentage of the training data to use for validation")
tf.flags.DEFINE_string("train_path", r'../../../dataset/train',
"Data source.")
tf.flags.DEFINE_string("dev_path", r'../../../dataset/dev', "Data source.")
tf.flags.DEFINE_string('vocab_path', r'../../../dataset/vocab',
'vocabulary path')
tf.flags.DEFINE_integer('sequence_length', 500, 'length of each sequence')
tf.flags.DEFINE_integer("num_tags", 14, "number classes of datasets.")
tf.flags.DEFINE_string('out_dir', '../../models', 'output directory')
# Model Hyperparameters
tf.flags.DEFINE_integer(
"embedding_dim", 200,
"Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5",
"Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer(
"num_filters", 200, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5,
"Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0,
"L2 regularization lambda (default: 0.0)")
tf.flags.DEFINE_float('learning_rate', 0.01, 'learning_rate of gradient')
# Training parameters
tf.flags.DEFINE_integer("batch_size", 32, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 100,
"Number of training epochs (default: 200)")
tf.flags.DEFINE_integer(
"evaluate_every", 100,
"Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100,
"Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5,
"Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True,
"Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print('\nParameters')
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# 加载数据
# Generate batches
# train_manager = batch_manager(FLAGS.train_path, FLAGS.sequence_length, FLAGS.batch_size, FLAGS.num_epochs)
# dev_manager = batch_manager(FLAGS.dev_path, FLAGS.sequence_length, FLAGS.batch_size, 1)
x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb'))
x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb'))
vocab_processer = learn.preprocessing.VocabularyProcessor.restore(
FLAGS.vocab_path)
train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size,
FLAGS.num_epochs)
# dev_x, dev_y, _ = load_data(FLAGS.dev_path, FLAGS.sequence_length)
# 构建图，进行训练
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# 构建cnn 节点
cnn = CNNClassification(
sequence_length=FLAGS.sequence_length,
num_tags=FLAGS.num_tags,
vocab_size=len(vocab_processer.vocabulary_),
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)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
# 优化算法
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(
"{}/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(FLAGS.out_dir, "runs_cnn", 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)
# Write vocabulary
# vocab_processer.save(os.path.join(out_dir, "vocab"))
# 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: FLAGS.dropout_keep_prob
}
# 执行 节点操作
_, 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()
if step % 20 == 0:
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, correct = sess.run([
global_step, dev_summary_op, cnn.loss, cnn.accuracy,
cnn.num_correct
], feed_dict)
if writer:
writer.add_summary(summaries, step)
return loss, accuracy, correct
# Training loop. For each batch...
best_acc = 0.0
best_step = 0
for batch in train_batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
# 更新全局步数
current_step = tf.train.global_step(sess, global_step)
# 计算评估结果
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
dev_batches = batch_iter(list(zip(x_dev, y_dev)),
FLAGS.batch_size, 1)
correct = 0.0
for batch_dev in dev_batches:
x_dev_batch, y_dev_batch = zip(*batch_dev)
loss_, accuracy_, correct_ = dev_step(
x_dev_batch,
y_dev_batch,
writer=dev_summary_writer)
# print(correct_)
correct += correct_
# print(dev_manager.length)
accuracy_ = correct / len(y_dev)
# loss_, accuracy_, correct_ = dev_step(x_dev, y_dev, writer=dev_summary_writer)
time_str = datetime.datetime.now().isoformat()
print("{}: acc {:g}".format(time_str, accuracy_))
if accuracy_ > best_acc:
best_acc = accuracy_
best_step = current_step
# 保存模型计算结果
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
print("")
print('\nBset dev at {}, accuray {:g}'.format(best_step, best_acc))
def train_cnnrnn():
# Parameters
# ==================================================
# Data loading params
tf.flags.DEFINE_float(
"dev_sample_percentage", .1,
"Percentage of the training data to use for validation")
tf.flags.DEFINE_string("train_path", "thu_train", "Data source.")
tf.flags.DEFINE_string("dev_path", "thu_dev", "Data source.")
tf.flags.DEFINE_integer('sequence_length', 400, 'length of each sequence')
tf.flags.DEFINE_integer("num_tags", 3, "number classes of datasets.")
# Model Hyperparameters
tf.flags.DEFINE_integer(
"embedding_dim", 200,
"Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5",
"Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_boolean('non_static', True,
'non static train word embedding')
tf.flags.DEFINE_string("celll", "lstm",
"Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer(
"num_filters", 200, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_integer("max_pool_size", 4, "max pool size")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5,
"Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_integer("hidden_units", 200, "number of RNN hidden cell")
tf.flags.DEFINE_string("cell", 'lstm',
"Which RNN cell will be used (dedault: lstm)")
tf.flags.DEFINE_float("num_rnn_layer", 1, "RNN layers (default: 1)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0,
"L2 regularization lambda (default: 0.0)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 32, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 100,
"Number of training epochs (default: 200)")
tf.flags.DEFINE_integer(
"evaluate_every", 100,
"Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100,
"Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5,
"Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True,
"Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False,
"Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print('\nParameters')
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Generate batches
x_train, y_train = pickle.load(open('train', 'rb'))
x_dev, y_dev = pickle.load(open('dev', 'rb'))
vocab_processer = learn.preprocessing.VocabularyProcessor.restore('vocab')
train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size,
FLAGS.num_epochs)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn_rnn = CNNRNNsClassification(
embedding_mat=None,
vocab_size=len(vocab_processer.vocabulary_),
sequence_length=FLAGS.sequence_length,
num_tags=FLAGS.num_tags,
non_static=FLAGS.non_static,
hidden_unit=FLAGS.hidden_units,
max_pool_size=FLAGS.max_pool_size,
filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
embedding_dim=FLAGS.embedding_dim,
cell=FLAGS.cell,
num_layers=1,
l2_reg_lambda=FLAGS.l2_reg_lambda)
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cnn_rnn.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
timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs_cnnrnn", timestamp))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", cnn_rnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn_rnn.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)
# write vocabulary
# pickle.dumps(word_index, open(os.path.join(out_dir, 'vocab'), 'wb'))
# initlize all vraiables
sess.run(tf.initialize_all_variables())
def real_len(batches):
return [
np.ceil(
np.argmin(batch + [0]) * 1.0 / FLAGS.max_pool_size)
for batch in batches
]
def train_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x:
x_batch,
cnn_rnn.input_y:
y_batch,
cnn_rnn.dropout_keep_prob:
FLAGS.dropout_keep_prob,
cnn_rnn.batch_size:
len(x_batch),
cnn_rnn.pad:
np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]),
cnn_rnn.real_len:
real_len(x_batch),
}
_, step, loss, accuracy = sess.run(
[train_op, global_step, cnn_rnn.loss, cnn_rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
if step % 20 == 0:
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
def dev_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x:
x_batch,
cnn_rnn.input_y:
y_batch,
cnn_rnn.dropout_keep_prob:
1.0,
cnn_rnn.batch_size:
len(x_batch),
cnn_rnn.pad:
np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]),
cnn_rnn.real_len:
real_len(x_batch),
}
step, loss, accuracy, num_correct, predictions = sess.run([
global_step, cnn_rnn.loss, cnn_rnn.accuracy,
cnn_rnn.num_correct, cnn_rnn.predictions
], feed_dict)
# time_str = datetime.datetime.now().isoformat()
# print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
return accuracy, loss, num_correct, predictions
# Training starts here
best_accuracy, best_at_step = 0, 0
# Train the model with x_train and y_train
for train_batch in train_batches:
x_train_batch, y_train_batch = zip(*train_batch)
train_step(x_train_batch, y_train_batch)
current_step = tf.train.global_step(sess, global_step)
# Evaluate the model with x_dev and y_dev
if current_step % FLAGS.checkpoint_every == 0:
total_dev_correct = 0
dev_batches = batch_iter(list(zip(x_dev, y_dev)),
FLAGS.batch_size, 1)
for dev_batch in dev_batches:
x_dev_batch, y_dev_batch = zip(*dev_batch)
acc, loss, num_dev_correct, predictions = dev_step(
x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
print()
accuracy = float(total_dev_correct) / len(y_dev)
print('Accuracy on dev set: {}'.format(accuracy))
if accuracy >= best_accuracy:
best_accuracy, best_at_step = accuracy, current_step
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print('Saved model {} at step {}'.format(
path, best_at_step))
print('Best accuracy {} at step {}'.format(
best_accuracy, best_at_step))
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
print(
'Training is complete, testing the best model on x_test and y_test'
)
def train_cnnrnn():
# Generate batches
x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb'))
x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb'))
vocab_processer = pickle.load(open(FLAGS.vocab_path, 'rb'))
train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size,
FLAGS.num_epochs)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_conf.gpu_options.allow_growth = True
sess = tf.Session(config=session_conf)
with sess.as_default():
cnn_rnn = CNNRNNsClassification(
embedding_mat=None,
vocab_size=len(vocab_processer.vocabulary_),
sequence_length=FLAGS.sequence_length,
num_tags=FLAGS.num_tags,
non_static=FLAGS.non_static,
hidden_unit=FLAGS.hidden_units,
max_pool_size=FLAGS.max_pool_size,
filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
embedding_dim=FLAGS.embedding_dim,
cell=FLAGS.cell,
num_layers=1,
l2_reg_lambda=FLAGS.l2_reg_lambda)
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(1e-3)
grads_and_vars = optimizer.compute_gradients(cnn_rnn.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
timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(FLAGS.out_dir, "runs_cnnrnn"))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", cnn_rnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn_rnn.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)
# write vocabulary
# pickle.dumps(word_index, open(os.path.join(out_dir, 'vocab'), 'wb'))
# initlize all vraiables
# tf.global_variables_initializer
sess.run(tf.initialize_all_variables())
def real_len(batches):
return [
np.ceil(
np.argmin(batch + [0]) * 1.0 / FLAGS.max_pool_size)
for batch in batches
]
def train_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x:
x_batch,
cnn_rnn.input_y:
y_batch,
cnn_rnn.dropout_keep_prob:
FLAGS.dropout_keep_prob,
cnn_rnn.batch_size:
len(x_batch),
cnn_rnn.pad:
np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]),
cnn_rnn.real_len:
real_len(x_batch),
}
_, step, loss, accuracy = sess.run(
[train_op, global_step, cnn_rnn.loss, cnn_rnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
if step % 20 == 0:
print("{}: step {}, loss {:g}, acc {:g}".format(
time_str, step, loss, accuracy))
def dev_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x:
x_batch,
cnn_rnn.input_y:
y_batch,
cnn_rnn.dropout_keep_prob:
1.0,
cnn_rnn.batch_size:
len(x_batch),
cnn_rnn.pad:
np.zeros([len(x_batch), 1, FLAGS.embedding_dim, 1]),
cnn_rnn.real_len:
real_len(x_batch),
}
step, loss, accuracy, num_correct, predictions = sess.run([
global_step, cnn_rnn.loss, cnn_rnn.accuracy,
cnn_rnn.num_correct, cnn_rnn.predictions
], feed_dict)
# time_str = datetime.datetime.now().isoformat()
# print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
return accuracy, loss, num_correct, predictions
# Training starts here
best_accuracy, best_at_step = 0, 0
# Train the model with x_train and y_train
for train_batch in train_batches:
x_train_batch, y_train_batch = zip(*train_batch)
train_step(x_train_batch, y_train_batch)
current_step = tf.train.global_step(sess, global_step)
# Evaluate the model with x_dev and y_dev
if current_step % FLAGS.checkpoint_every == 0:
total_dev_correct = 0
dev_batches = batch_iter(list(zip(x_dev, y_dev)),
FLAGS.batch_size, 1)
for dev_batch in dev_batches:
x_dev_batch, y_dev_batch = zip(*dev_batch)
acc, loss, num_dev_correct, predictions = dev_step(
x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
accuracy = float(total_dev_correct) / len(y_dev)
print('Accuracy on dev set: {}'.format(accuracy))
if accuracy >= best_accuracy:
best_accuracy, best_at_step = accuracy, current_step
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print('Saved model {} at step {}'.format(
path, best_at_step))
print('Best accuracy {} at step {}'.format(
best_accuracy, best_at_step))
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
print(
'Training is complete, testing the best model on x_test and y_test'
)
def train_cnns():
"""
使用普通数据加载方式，进行训练，适用于小数据集
:return:
"""
# 加载数据
# Generate batches
# train_manager = batch_manager(FLAGS.train_path, FLAGS.sequence_length, FLAGS.batch_size, FLAGS.num_epochs)
# dev_manager = batch_manager(FLAGS.dev_path, FLAGS.sequence_length, FLAGS.batch_size, 1)
x_train, y_train = pickle.load(open(FLAGS.train_path, 'rb'))
x_dev, y_dev = pickle.load(open(FLAGS.dev_path, 'rb'))
term2id, id2term = pickle.load(open(FLAGS.vocab_path, 'rb'))
train_batches = batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size,
FLAGS.num_epochs)
# dev_x, dev_y, _ = load_data(FLAGS.dev_path, FLAGS.sequence_length)
# 构建图，进行训练
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# 构建cnn 节点
cnn = CNNClassification(sequence_length=FLAGS.sequence_length,
num_tags=FLAGS.num_tags,
vocab_size=len(term2id),
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)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
# 优化算法
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(
"{}/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(FLAGS.out_dir, "runs_cnn"))
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)
# Write vocabulary
# vocab_processer.save(os.path.join(out_dir, "vocab"))
# 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: FLAGS.dropout_keep_prob
}
# 执行 节点操作
_, 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()
if step % 20 == 0:
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, correct = sess.run([
global_step, dev_summary_op, cnn.loss, cnn.accuracy,
cnn.num_correct
], feed_dict)
if writer:
writer.add_summary(summaries, step)
return loss, accuracy, correct
# Training loop. For each batch...
best_acc = 0.0
best_step = 0
for batch in train_batches:
x_batch, y_batch = zip(*batch)
train_step(x_batch, y_batch)
# 更新全局步数
current_step = tf.train.global_step(sess, global_step)
# 计算评估结果
if current_step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
dev_batches = batch_iter(list(zip(x_dev, y_dev)),
FLAGS.batch_size, 1)
correct = 0.0
for batch_dev in dev_batches:
x_dev_batch, y_dev_batch = zip(*batch_dev)
loss_, accuracy_, correct_ = dev_step(
x_dev_batch,
y_dev_batch,
writer=dev_summary_writer)
# print(correct_)
correct += correct_
# print(dev_manager.length)
accuracy_ = correct / len(y_dev)
# loss_, accuracy_, correct_ = dev_step(x_dev, y_dev, writer=dev_summary_writer)
time_str = datetime.datetime.now().isoformat()
print("{}: acc {:g}".format(time_str, accuracy_))
if accuracy_ > best_acc:
best_acc = accuracy_
best_step = current_step
# 保存模型计算结果
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))
print("")
print('\nBset dev at {}, accuray {:g}'.format(best_step, best_acc))
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement
)
sess = tf.Session(config=session_conf)
with sess.as_default():
# 从文件中加载模型
saver = tf.train.import_meta_graph('{}.meta'.format(model_path))
saver.restore(sess, model_path)
# 获取变量
input_x = graph.get_operation_by_name('input_x').outputs[0]
dropout_keep_prob = graph.get_operation_by_name('dropout_keep_prob').outputs[0]
predictions = graph.get_operation_by_name('output/predictons').outputs[0]
batches = batch_iter(list(test_x), FLAGS.batch_size, 1, shuffle=False)
all_predictions = []
# prediction
for x_test_batch in batches:
feed_dict = {input_x:x_test_batch, dropout_keep_prob:1.0}
batch_pre = sess.run(predictions, feed_dict=feed_dict)
all_predictions = np.concatenate([all_predictions, batch_pre])
if test_y is not None:
correct_prediction = float(sum(all_predictions == test_y))
print('Total number of test example : {}'.format(len(test_y)))
print('Accuracy:{:g}'.format(correct_prediction/float(len(test_y))))