#! /usr/bin/env python
# encoding: utf-8
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helper
import word2vec_helpers
from text_cnn import TextCNN
from scipy.sparse import csr_matrix
import random
# Parameters
# =======================================================
# Data loading parameters
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_integer("num_labels", 12, "Number of labels for data. (default: 12)")
# Model hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-spearated 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 regularization lambda (default: 0.0)")
# Training paramters
tf.flags.DEFINE_integer("batch_size", 10, "Batch Size (default: 20)")
tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
tf.flags.DEFINE_integer("evaluate_every", 100, "Evalue model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (defult: 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")
# Parse parameters from commands
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Prepare 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))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# Data preprocess
# =======================================================
# Load data
print("Loading data...")
x_text, y = data_helper.load_data_and_label('input_data/')
print "x_text length: ", len(x_text)
print "y length: ", y.shape
#shuffle x_text and y
#np.random.seed(10)
#x_test = np.random.permutation(x_text)
#y = np.radom.permutation(y)
#x_text = x_text[0:1000]
#y = y[0:1000]
#random select a part of the original data
new_x_text = []
new_y = []
for i in range(3000):
rand_idx = random.randint(0, len(x_text))
#rand_y = random.randint(0, len(x_text))
new_x_text.append(x_text[rand_idx])
new_y.append(y[rand_idx])
print "new_x_text length: %d" % len(new_x_text)
print "new_y length: %d" % len(new_y)
# embedding vector
print("Padding sentences...")
sentences, max_document_length = data_helper.padding_sentences(new_x_text, '<PADDING>') #max_document_length =
print("embedding_sentences...")
all_vectors = word2vec_helpers.embedding_sentences(sentences, embedding_size = FLAGS.embedding_dim, file_to_save = os.path.join(out_dir, 'trained_word2vec.model'))
print "all_vectors length %d * %d * %d : " % (len(all_vectors) , len(all_vectors[0]) , len(all_vectors[0][0]))
#x = np.array(all_vectors) ## this operation could lead to memory error!!!
#TODO: transform large vectors into sparse matrix
x = np.asarray(all_vectors)
y = np.asarray(new_y)
print("x.shape = {}".format(x.shape))
print("y.shape = {}".format(y.shape))
# Save params
training_params_file = os.path.join(out_dir, 'training_params.pickle')
params = {'num_labels' : FLAGS.num_labels, 'max_document_length' : max_document_length}
data_helper.saveDict(params, training_params_file)
# Shuffle data randomly
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
dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
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 = y_train.shape[1],
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.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=FLAGS.num_checkpoints)
# 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
}
_, 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
