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trainTextRNN.py
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trainTextRNN.py
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#! /user/bin/evn python
# -*- coding:utf8 -*-
"""
@Author : Lau James
@Contact : LauJames2017@whu.edu.cn
@Project : Structure_Func_Recognition
@File : trainTextRNN.py
@Time : 1/20/18 2:36 PM
@Software : PyCharm
@Copyright: "Copyright (c) 2017 Lau James. All Rights Reserved"
"""
import tensorflow as tf
import numpy as np
import os
from data import dataHelper
import time
import datetime
from model.textRNN import TextRNN
# Data loading params
tf.flags.DEFINE_float("dev_sample_percentage", 0.01, "Percentage of the training data to use for validation")
tf.flags.DEFINE_string("data_file", "./data/labeled_data",
"Data source for the data.")
# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 200, "Dimensionality of character embedding (default: 128)")
#tf.flags.DEFINE_integer("seq_length", 600, "sequence length (default: 600)")
tf.flags.DEFINE_integer("num_classes", 5, "Number of classes (default: 5)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.8, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")
tf.flags.DEFINE_integer("num_layers", 2, "number of layers (default: 2)")
tf.flags.DEFINE_integer("hidden_dim", 128, "neural numbers of hidden layer (default: 128)")
tf.flags.DEFINE_string("rnn_type", 'gru', "rnn type (default: gru)")
tf.flags.DEFINE_float("learning_rate", 1e-3, "learning rate (default:1e-3)")
# 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", 250, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 500, "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("")
# Data Preparation
# ==================================================
# Load data
print("Loading data...")
x_text, y = dataHelper.get_para_label(FLAGS.data_file)
# Build vocabulary
max_document_length = max([len(x.split(" ")) for x in x_text])
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(max_document_length)
# 神器,填充到最大长度
x = np.array(list(vocab_processor.fit_transform(x_text)))
# 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
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:]
del x, y, x_shuffled, y_shuffled
print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
# Training
# ===============================================
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=0.5,
allow_growth=True
)
session_config = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement,
gpu_options=gpu_options
)
sess = tf.Session(config=session_config)
with sess.as_default():
rnn = TextRNN(
sequence_length=x_train.shape[1],
num_classes=FLAGS.num_classes,
vocab_size=len(vocab_processor.vocabulary_),
embedding_dim=FLAGS.embedding_dim,
num_layers=FLAGS.num_layers,
hidden_dim=FLAGS.hidden_dim,
rnn=FLAGS.rnn_type)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads_and_vars = optimizer.compute_gradients(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-rnn", timestamp))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", rnn.loss)
acc_summary = tf.summary.scalar("accuracy", 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
vocab_processor.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
:param x_batch:
:param y_batch:
:return:
"""
feed_dict = {
rnn.input_x: x_batch,
rnn.input_y: y_batch,
rnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run([train_op, global_step, train_summary_op, rnn.loss,
rnn.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_dev, y_dev):
"""
Evaluates model on a dev set
:param x_dev:
:param y_dev:
:return:
"""
data_len = len(x_dev)
batch_eval = dataHelper.batch_iter_eval(x_dev, y_dev)
total_loss = 0.0
total_acc = 0.0
for x_batch_eval, y_batch_eval in batch_eval:
batch_len = len(x_batch_eval)
feed_dict = {
rnn.input_x: x_batch_eval,
rnn.input_y: y_batch_eval,
rnn.dropout_keep_prob: 1.0
}
loss, accuracy = sess.run(
[rnn.loss, rnn.accuracy],
feed_dict)
total_loss += loss * batch_len
total_acc += accuracy * batch_len
time_str = datetime.datetime.now().isoformat()
print("{}: loss {:g}, acc {:g}".format(time_str, total_loss / data_len, total_acc / data_len))
# Generate batches
batches = dataHelper.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)
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))