First, I'm sorry.
I have poor English.
Thanks to Yoon Kim and dennybritz.
I edit your script to apply multi-labels support.
this script support multi-label data like next:
text1....\t label1
text2....\t label2
text3....\t label1
text4....\t label3
python train.py --train_data_path="./data/train.txt"
python eval.py --checkpoint_dir="./runs/1463968251/checkpoints/" --test_data_path="./data/test.txt"
import codecs
import os.path
import numpy as np
import re
import itertools
from collections import Counter
PAD_MARK = "<PAD/>"
UNK_MARK = "<UNK/>"
def clean_str(string):
"""
Tokenization/string cleaning for all datasets except for SST.
Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
"""
# string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string) # blocked to allow non-english char-set
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()
def load_data_and_labels( train_data_path ):
"""
Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# Load data from files
data = list()
labels = list()
for line in codecs.open( train_data_path, 'r', encoding='utf8' ).readlines() :
if 1 > len( line.strip() ) : continue;
t = line.split(u"\t");
if 2 != len(t) :
print "data format error" + line
continue;
data.append(t[0])
labels.append(t[1])
data = [s.strip() for s in data]
labels = [s.strip() for s in labels]
# Split by words
x_text = [clean_str(sent) for sent in data]
x_text = [s.split(u" ") for s in x_text]
return [x_text, labels]
def pad_sentences(sentences, max_sent_len_path):
"""
Pads all sentences to the same length. The length is defined by the longest sentence.
Returns padded sentences.
"""
max_sequence_length = 0
# Load base max sent length
if len(max_sent_len_path) > 0 :
max_sequence_length = int( open( max_sent_len_path, 'r' ).readlines()[0] )
else :
max_sequence_length = max(len(x) for x in sentences)
padded_sentences = []
for i in range(len(sentences)):
sentence = sentences[i]
if max_sequence_length <= len(sentence) :
padded_sentences.append(sentence[:max_sequence_length])
continue
num_padding = max_sequence_length - len(sentence)
new_sentence = sentence + [PAD_MARK] * num_padding
padded_sentences.append(new_sentence)
return padded_sentences, max_sequence_length
def build_vocab(sentences, base_vocab_path):
"""
Builds a vocabulary mapping from word to index based on the sentences.
Returns vocabulary mapping and inverse vocabulary mapping.
"""
vocabulary_inv = []
# Load base vocabulary
if len(base_vocab_path) > 0 :
vL = [ [w.strip()] for w in codecs.open( base_vocab_path, 'r', encoding='utf8' ).readlines() ]
c = Counter(itertools.chain(*vL))
vocabulary_inv = [x[0] for x in c.most_common()]
else :
# Build vocabulary
word_counts = Counter(itertools.chain(*sentences))
# Mapping from index to word
vocabulary_inv = vocabulary_inv + [x[0] for x in word_counts.most_common()]
if not UNK_MARK in vocabulary_inv :
vocabulary_inv.append(UNK_MARK)
vocabulary_inv = list(set(vocabulary_inv))
vocabulary_inv.sort()
# Mapping from word to index
vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
if not UNK_MARK in vocabulary :
vocabulary[UNK_MARK] = vocabulary[PAD_MARK]
return [vocabulary, vocabulary_inv]
def make_onehot(idx, size) :
onehot = []
for i in range(size) :
if idx==i : onehot.append(1);
else : onehot.append(0);
return onehot
# end def
def make_label_dic(labels) :
"""
creator: [email protected]
create date: 2016.05.22
make 'label : one hot' dic
"""
label_onehot = dict()
onehot_label = dict()
for i, label in enumerate(labels) :
onehot = make_onehot(i,len(labels))
label_onehot[label] = onehot
onehot_label[str(onehot)] = label
return label_onehot, onehot_label
# end def
def build_onehot(labels, base_label_path):
"""
Builds a vocabulary mapping from label to onehot based on the sentences.
Returns vocabulary mapping and inverse vocabulary mapping.
"""
uniq_labels = []
# Load base vocabulary
if len(base_label_path) > 0 :
vL = [ [w.strip()] for w in codecs.open( base_label_path, 'r', encoding='utf8' ).readlines() ]
c = Counter(itertools.chain(*vL))
uniq_labels = [x[0] for x in c.most_common()]
else :
# Build vocabulary
label_counts = Counter(labels)
# Mapping from index to word
uniq_labels = uniq_labels + [x[0] for x in label_counts.most_common()]
uniq_labels = list(set(uniq_labels))
uniq_labels.sort()
label_onehot, onehot_label = make_label_dic( uniq_labels )
return [uniq_labels, label_onehot, onehot_label]
def build_input_data(sentences, vocabulary, labels, label_onehot):
"""
Maps sentencs and labels to vectors based on a vocabulary.
"""
vL = []
for sentence in sentences :
wL = []
for word in sentence :
if word in vocabulary :
wL.append( vocabulary[word] )
else :
wL.append( vocabulary[UNK_MARK] )
vL.append(wL)
x = np.array(vL)
y = np.array([ label_onehot[label] for label in labels ])
return [x, y]
def load_data( train_data_path, checkpoint_dir="" ):
"""
Loads and preprocessed data for the MR dataset.
Returns input vectors, labels, vocabulary, and inverse vocabulary.
"""
# Load and preprocess data
max_sent_len_path = "" if len(checkpoint_dir)<1 else checkpoint_dir+"/max_sent_len"
vocab_path = "" if len(checkpoint_dir)<1 else checkpoint_dir+"/vocab"
label_path = "" if len(checkpoint_dir)<1 else checkpoint_dir+"/label"
sentences, labels = load_data_and_labels( train_data_path )
sentences_padded, max_sequence_length = pad_sentences(sentences, max_sent_len_path)
vocabulary, vocabulary_inv = build_vocab(sentences_padded, vocab_path)
uniq_labels, label_onehot, onehot_label = build_onehot(labels, label_path)
x, y = build_input_data(sentences_padded, vocabulary, labels, label_onehot)
return [x, y, vocabulary, vocabulary_inv, onehot_label, max_sequence_length]
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)/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]
#! /usr/bin/env python
import codecs
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
# Parameters
# ==================================================
# Model Hyperparameters
tf.flags.DEFINE_string("train_data_path", "./data/train.txt", "Data path to training")
tf.flags.DEFINE_integer("embedding_dim", 128, "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", 128, "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 regularizaion lambda (default: 0.0)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 200, "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)")
# 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("")
# Data Preparatopn
# ==================================================
# Load data
print("Loading data...")
x, y, vocabulary, vocabulary_inv, onehot_label, max_sequence_length = data_helpers.load_data( FLAGS.train_data_path )
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
# Split train/test set
# TODO: This is very crude, should use cross-validation
x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
print("Labels: %d: %s" % ( len(onehot_label), ','.join( onehot_label.values() ) ) )
print("Vocabulary Size: {:d}".format(len(vocabulary)))
print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
# Training
# ==================================================
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():
cnn = TextCNN(
sequence_length=x_train.shape[1],
num_classes=len(onehot_label),
vocab_size=len(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)
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.histogram_summary("{}/grad/hist".format(v.name), g)
sparsity_summary = tf.scalar_summary("{}/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.merge_summary(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.scalar_summary("loss", cnn.loss)
acc_summary = tf.scalar_summary("accuracy", cnn.accuracy)
# Train Summaries
train_summary_op = tf.merge_summary([loss_summary, acc_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.train.SummaryWriter(train_summary_dir, sess.graph_def)
# Dev summaries
dev_summary_op = tf.merge_summary([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.train.SummaryWriter(dev_summary_dir, sess.graph_def)
# 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)
# Save additional model info
codecs.open( os.path.join(checkpoint_dir, "max_sent_len"), "w", encoding='utf8').write( str(max_sequence_length) )
codecs.open( os.path.join(checkpoint_dir, "vocab"), "w", encoding='utf8').write( '\n'.join(vocabulary_inv) )
codecs.open( os.path.join(checkpoint_dir, "label"), "w", encoding='utf8').write( '\n'.join(onehot_label.values()) )
saver = tf.train.Saver(tf.all_variables())
# Initialize all variables
sess.run(tf.initialize_all_variables())
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)
# Generate batches
batches = data_helpers.batch_iter(
list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# 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:
print("\nEvaluation:")
dev_step(x_dev, y_dev, 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))
#! /usr/bin/env python
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
# Parameters
# ==================================================
# Eval Parameters
tf.flags.DEFINE_string("test_data_path", "./data/test.txt", "Data path to evaluation")
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run")
# 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("")
# Load data. Load your own data here
print("Loading data...")
x_test, y_test, vocabulary, vocabulary_inv, onehot_label, max_sequence_length = data_helpers.load_data( FLAGS.test_data_path, FLAGS.checkpoint_dir )
y_test = np.argmax(y_test, axis=1)
print("Labels: %d: %s" % ( len(onehot_label), ','.join( sorted(onehot_label.values()) ) ) )
print("Vocabulary size: {:d}".format(len(vocabulary)))
print("Test set size {:d}".format(len(y_test)))
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
with 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():
# Load the saved meta graph and restore variables
print "FLAGS.checkpoint_dir %s" % FLAGS.checkpoint_dir
print "checkpoint_file %s" % checkpoint_file
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x = graph.get_operation_by_name("input_x").outputs[0]
# input_y = graph.get_operation_by_name("input_y").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(x_test, FLAGS.batch_size, 1, shuffle=False)
# Collect the predictions here
all_predictions = []
for x_test_batch in batches:
batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
all_predictions = np.concatenate([all_predictions, batch_predictions])
# Print accuracy
print "y_test: " + str(y_test)
print "all_predictions: " + str(all_predictions)
correct_predictions = float(sum(all_predictions == y_test))
print("Total number of test examples: {}".format(len(y_test)))
print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))
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