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cnn_model.py
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cnn_model.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 28 22:55:25 2018
@author: Rishi
"""
'''
CNN Model
'''
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import sys
from tensorflow.contrib import learn
from text_cnn import TextCNN
import data_helpers as dh
import pandas as pd
from sklearn.preprocessing import MultiLabelBinarizer
#================================= Loading data ==============================#
print("Loading data...")
with open('test+train.tsv',encoding='utf8') as tsvfile:
train_test_data = pd.read_csv(tsvfile, delimiter='\t',header=None)
tsvfile.close()
with open('test.tsv',encoding='utf8') as tsvfile:
test_data=pd.read_csv(tsvfile, delimiter='\t', header=None)
tsvfile.close()
with open('train.tsv',encoding='utf8') as tsvfile:
train_data=pd.read_csv(tsvfile, delimiter='\t', header=None)
tsvfile.close()
test_text=test_data[2]
Y_test=test_data[1]
train_test_text=train_test_data[2]
Y_train_test=train_test_data[1]
train_text=train_data[2]
Y_train=train_data[1]
del test_data, train_test_data, train_data
#============================== Model Parameters =================================#
# 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-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)")
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(sys.argv)
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Load data
print("Loading data...")
x_text, y = dh.load_data_and_labels(train_test_text,Y_train_test)
x_1, y_train = dh.load_data_and_labels(train_text,Y_train)
x_2, y_test = dh.load_data_and_labels(test_text,Y_test)
# Build vocabulary
print("building vocab...")
max_document_length = max([len(x.split(" ")) for x in x_text])
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
x = np.array(list(vocab_processor.fit_transform((x_text))))
x_train = np.array(list(vocab_processor.fit_transform((x_1))))
x_test = np.array(list(vocab_processor.fit_transform((x_2))))
g=tf.Graph()
with g.as_default():
sess=tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement= True))
with sess.as_default():
''' instantiate the class TextCNN '''
print("instantiating the class...")
cnn = TextCNN(
sequence_length=x_train.shape[1],
num_classes = y_train.shape[1],
vocab_size=len(vocab_processor.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)
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(1e-4)
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)
print("keep track of grad and values....")
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("output directoru 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
print("summaries for loss and accuracy...")
loss_summary = tf.summary.scalar("loss", cnn.loss)
acc_summary = tf.summary.scalar("accuracy", cnn.accuracy)
# Train Summaries
print("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)
# Test summaries
print("test summaries...")
test_summary_op = tf.summary.merge([loss_summary, acc_summary])
test_summary_dir = os.path.join(out_dir, "summaries", "test")
test_summary_writer = tf.summary.FileWriter(test_summary_dir, sess.graph)
# Checkpointing
print("checkpointing...")
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
# Tensorflow assumes this directory already exists so we need to create it
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.all_variables())
# Write vocabulary
print("write vocab...")
vocab_processor.save(os.path.join(out_dir, "vocab"))
print("sess.run...")
sess.run(tf.initialize_all_variables())
print("train_step...")
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)
print("test step...")
def test_step(x_batch, y_batch, writer=None):
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, test_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)
print("generate batches...")
# Generate batches
batches = dh.batch_iter(list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
for batch in batches:
print("inside loop...")
# print('batch',batch[1,1])
x_batch, y_batch = zip(*batch)
# print('x_batch, y_batch',x_batch, y_batch)
train_step(x_batch, y_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print("inside if1...")
print("\nEvaluation:")
test_step(x_test, y_test, writer=test_summary_writer)
print("")
if current_step % FLAGS.checkpoint_every == 0:
print("inside if2...")
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
print("Saved model checkpoint to {}\n".format(path))