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tf_attention_models.py
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from __future__ import print_function, division
import os
import numpy as np
import tensorflow as tf
import fire
import logging ## library for print information about argparse
import argparse
import ConfigParser as configparser
# from paste.deploy.converters import asbool, asint, aslist
from tensorflow.contrib.rnn import GRUCell
from tensorflow.python.ops.rnn import bidirectional_dynamic_rnn as bi_rnn
from tqdm import tqdm
from utils import batch_generator, get_max_len_info
from data_prep_for_visualization import prepare_data
# This is piece of code is Copyright (c) 2017 to Ilya Ivanov and grants permission under MIT Licence
# https://github.com/ilivans/tf-rnn-attention/blob/master/attention.py
# Implementation as proposed by Yang et al. in "Hierarchical Attention Networks for Document Classification" (2016)
class Model(object):
def attention(inputs, attention_size, time_major=False, return_alphas=False):
if isinstance(inputs, tuple):
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
inputs = tf.concat(inputs, 2)
if time_major:
# (T,B,D) => (B,T,D)
inputs = tf.array_ops.transpose(inputs, [1, 0, 2])
hidden_size = inputs.shape[2].value # D value - hidden size of the RNN layer
# Trainable parameters
w_omega = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1))
b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
with tf.name_scope('v'):
# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
# the shape of `v` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
v = tf.tanh(tf.tensordot(inputs, w_omega, axes=1) + b_omega)
# For each of the timestamps its vector of size A from `v` is reduced with `u` vector
vu = tf.tensordot(v, u_omega, axes=1, name='vu') # (B,T) shape
alphas = tf.nn.softmax(vu, name='alphas') # (B,T) shape
# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
output = tf.reduce_sum(inputs * tf.expand_dims(alphas, -1), 1)
if not return_alphas:
return output
else:
return output, alphas
def build_attention_model():
# Different placeholders
with tf.name_scope('Inputs'):
batch_ph = tf.placeholder(tf.int32, [None, SEQUENCE_LENGTH], name='batch_ph')
target_ph = tf.placeholder(tf.float32, [None], name='target_ph')
seq_len_ph = tf.placeholder(tf.int32, [None], name='seq_len_ph')
keep_prob_ph = tf.placeholder(tf.float32, name='keep_prob_ph')
# Embedding layer
with tf.name_scope('Embedding_layer'):
embeddings_var = tf.Variable(tf.random_uniform([vocabulary_size, EMBEDDING_DIM], -1.0, 1.0), trainable=True)
tf.summary.histogram('embeddings_var', embeddings_var)
batch_embedded = tf.nn.embedding_lookup(embeddings_var, batch_ph)
# (Bi-)RNN layer(-s)
rnn_outputs, _ = bi_rnn(GRUCell(HIDDEN_UNITS), GRUCell(HIDDEN_UNITS),
inputs=batch_embedded, sequence_length=seq_len_ph, dtype=tf.float32)
tf.summary.histogram('RNN_outputs', rnn_outputs)
# Attention layer
with tf.name_scope('Attention_layer'):
attention_output, alphas = attention(rnn_outputs, ATTENTION_UNITS, return_alphas=True)
tf.summary.histogram('alphas', alphas)
# Dropout
drop = tf.nn.dropout(attention_output, keep_prob_ph)
# Fully connected layer
with tf.name_scope('Fully_connected_layer'):
W = tf.Variable(
tf.truncated_normal([HIDDEN_UNITS * 2, 1], stddev=0.1)) # Hidden size is multiplied by 2 for Bi-RNN
b = tf.Variable(tf.constant(0., shape=[1]))
y_hat = tf.nn.xw_plus_b(drop, W, b)
y_hat = tf.squeeze(y_hat)
tf.summary.histogram('W', W)
with tf.name_scope('Metrics'):
# Cross-entropy loss and optimizer initialization
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_hat, labels=target_ph))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(loss)
# Accuracy metric
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.round(tf.sigmoid(y_hat)), target_ph), tf.float32))
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
# Batch generators
train_batch_generator = batch_generator(X_train, y_train, BATCH_SIZE)
test_batch_generator = batch_generator(X_test, y_test, BATCH_SIZE)
session_conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
saver = tf.train.Saver()
return batch_ph, target_ph, seq_len_ph, keep_prob_ph, alphas, loss, accuracy, optimizer, merged, \
train_batch_generator, test_batch_generator, session_conf, saver
def save_model():
batch_ph, target_ph, seq_len_ph, keep_prob_ph, alphas, loss, accuracy, optimizer, merged, \
train_batch_generator, test_batch_generator, session_conf, saver = build_attention_model()
with tf.Session(config=session_conf) as sess:
sess.run(tf.global_variables_initializer())
print("Start learning...")
for epoch in range(EPOCHS):
loss_train = 0
loss_test = 0
accuracy_train = 0
accuracy_test = 0
# Training
num_batches = X_train.shape[0] // BATCH_SIZE
for b in tqdm(range(num_batches)):
x_batch, y_batch = next(train_batch_generator)
seq_lists = []
for x in x_batch:
if 0 not in list(x):
seq_lists.append(SEQUENCE_LENGTH)
else:
seq_lists.append(list(x).index(0) + 1)
seq_len = np.array(seq_lists)
loss_tr, acc, _, summary = sess.run([loss, accuracy, optimizer, merged],
feed_dict={batch_ph: x_batch,
target_ph: y_batch,
seq_len_ph: seq_len,
keep_prob_ph: KEEP_PROB})
accuracy_train += acc
loss_train = loss_tr * DELTA + loss_train * (1 - DELTA)
accuracy_train /= num_batches
# Testing
num_batches = X_test.shape[0] // BATCH_SIZE
for batch in tqdm(range(num_batches)):
x_batch, y_batch = next(test_batch_generator)
seq_lists = []
for x in x_batch:
if 0 not in list(x):
seq_lists.append(SEQUENCE_LENGTH)
else:
seq_lists.append(list(x).index(0) + 1)
seq_len = np.array(seq_lists)
loss_test_batch, acc, summary = sess.run([loss, accuracy, merged],
feed_dict={batch_ph: x_batch,
target_ph: y_batch,
seq_len_ph: seq_len,
keep_prob_ph: 1.0})
accuracy_test += acc
loss_test += loss_test_batch
accuracy_test /= num_batches
loss_test /= num_batches
print("loss: {:.3f}, val_loss: {:.3f}, acc: {:.3f}, val_acc: {:.3f}".format(
loss_train, loss_test, accuracy_train, accuracy_test))
saver.save(sess, MODEL_PATH)
def main(model_path="models/tf_attention",
batch_size=50,
epochs=2,
embedding_dim=100,
hiddin_units=150,
attention_units=50,
keep_prob=0.8,
delta=0.5,
shuffle=False):
if __name__ == '__main__':
fire.Fire(main)