vanilla_train.py

# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import os
import time
import datetime
from data_helper.helper import load_all_data_and_labels, batch_iter, tokenizer
from vanilla.cnn import TextCNN
from tensorflow.contrib import learn
import pickle
# Parameters
# ==================================================
# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 300, "Dimensionality of character embedding (default: 300)")
tf.flags.DEFINE_string("filter_sizes", "1,2,3,4", "Comma-separated filter sizes (default: '1,2,3,4')")
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", 0.1, "L2 regularization lambda (default: 0.1)")

# Training parameters
tf.flags.DEFINE_integer("batch_size", 32, "Batch Size (default: 64)")
tf.flags.DEFINE_integer("num_epochs", 7, "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("")

#load in train and dev data
train_x, train_y = load_all_data_and_labels(['./data/new_train.json'])
dev_x, dev_y = load_all_data_and_labels(['./data/new_dev.json'])
x_text = train_x + dev_x
y = train_y + dev_y
y = np.copy(np.array(y))
#transform y
labels = np.unique(y)
y = np.array([list(map(int, labels == y_)) for y_ in y])

# Build vocabulary
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)))

#dev_sample_index = -1 * int(0.1 * float(len(y)))
dev_sample_index = -len(dev_x)
x_train, x_dev = x[:dev_sample_index], x[dev_sample_index:]
y_train, y_dev = y[:dev_sample_index], y[dev_sample_index:]

#load test data
test_x, test_y = load_all_data_and_labels(['./data/new_test.json'])
x_test = np.array(list(vocab_processor.fit_transform(test_x)))
y_test = np.array([list(map(int, labels == y_)) for y_ in test_y])

del x, y, train_x,train_y, dev_x, dev_y, test_x, test_y

print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))
print("Test Size: {:d}".format(len(y_test)))

# Training
# ==================================================

with tf.Graph().as_default():
    session_conf = tf.ConfigProto(
      allow_soft_placement=FLAGS.allow_soft_placement,
      log_device_placement=FLAGS.log_device_placement)
    session_conf.gpu_options.allow_growth = True
    sess = tf.Session(config=session_conf)
    with sess.as_default():
        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)

        # 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
        timestamp = str(int(time.time()))
        out_dir = os.path.abspath(os.path.join(os.path.curdir, "graphs", 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_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
            """
            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)
            if step % 10 == 0:
                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 = 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("")
        # Testing
        def test(x_batch, y_batch):
            feed_dict = {
                cnn.input_x: x_batch,
                cnn.input_y: y_batch,
                cnn.dropout_keep_prob: 1.0
            }
            predictions, loss, accuracy = sess.run(
                [cnn.predictions,cnn.loss,cnn.accuracy],
                feed_dict
            )
            return predictions, loss, accuracy
        predictions, loss, accuracy = test(x_test, y_test)
        print("\nTest Accuracy: {:g}; Loss: {:g}".format(accuracy, loss))
        with open('./predictions/vanilla_predictions.pickle', 'wb') as f:
            pickle.dump(predictions, f)

#accuracy:0.457595, loss: 1.65062