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Textual entailment training using TensorFlow.
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import urllib
import sys
import os
import zipfile
glove_vectors_file = "glove.6B.50d.txt"
snli_dev_file = "snli_1.0_dev.txt"
snli_full_dataset_file = "snli_1.0_train.txt"
glove_wordmap = {}
with open(glove_vectors_file, "r", encoding="utf-8") as glove:
for line in glove:
name, vector = tuple(line.split(" ", 1))
glove_wordmap[name] = np.fromstring(vector, sep=" ")
def sentence2sequence(sentence):
tokens = sentence.lower().split(" ")
rows = []
words = []
#Greedy search for tokens
for token in tokens:
i = len(token)
while len(token) > 0 and i > 0:
word = token[:i]
if word in glove_wordmap:
token = token[i:]
i = len(token)
i = i-1
return rows, words
rnn_size = 64
rnn = tf.contrib.rnn.BasicRNNCell(rnn_size)
#Constants setup
max_hypothesis_length, max_evidence_length = 30, 30
batch_size, vector_size, hidden_size = 128, 50, 64
lstm_size = hidden_size
weight_decay = 0.0001
learning_rate = 1
input_p, output_p = 0.5, 0.5
training_iterations_count = 100000
display_step = 10
def score_setup(row):
convert_dict = {
'entailment': 0,
'neutral': 1,
'contradiction': 2
score = np.zeros((3,))
for x in range(1,6):
tag = row["label"+str(x)]
if tag in convert_dict: score[convert_dict[tag]] += 1
return score / (1.0*np.sum(score))
def fit_to_size(matrix, shape):
res = np.zeros(shape)
slices = [slice(0,min(dim,shape[e])) for e, dim in enumerate(matrix.shape)]
res[slices] = matrix[slices]
return res
def split_data_into_scores():
import csv
with open(snli_dev_file,"r") as data:
train = csv.DictReader(data, delimiter='\t')
evi_sentences = []
hyp_sentences = []
labels = []
scores = []
for row in train:
hyp_sentences = np.stack([fit_to_size(x, (max_hypothesis_length, vector_size))
for x in hyp_sentences])
evi_sentences = np.stack([fit_to_size(x, (max_evidence_length, vector_size))
for x in evi_sentences])
return (hyp_sentences, evi_sentences), labels, np.array(scores)
data_feature_list, correct_values, correct_scores = split_data_into_scores()
l_h, l_e = max_hypothesis_length, max_evidence_length
N, D, H = batch_size, vector_size, hidden_size
l_seq = l_h + l_e
lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
lstm_drop = tf.contrib.rnn.DropoutWrapper(lstm, input_p, output_p)
# N: The number of elements in each of our batches,
# which we use to train subsets of data for efficiency's sake.
# l_h: The maximum length of a hypothesis, or the second sentence. This is
# used because training an RNN is extraordinarily difficult without
# rolling it out to a fixed length.
# l_e: The maximum length of evidence, the first sentence. This is used
# because training an RNN is extraordinarily difficult without
# rolling it out to a fixed length.
# D: The size of our used GloVe or other vectors.
hyp = tf.placeholder(tf.float32, [N, l_h, D], 'hypothesis')
evi = tf.placeholder(tf.float32, [N, l_e, D], 'evidence')
y = tf.placeholder(tf.float32, [N, 3], 'label')
# hyp: Where the hypotheses will be stored during training.
# evi: Where the evidences will be stored during training.
# y: Where correct scores will be stored during training.
# lstm_size: the size of the gates in the LSTM,
# as in the first LSTM layer's initialization.
lstm_back = tf.contrib.rnn.BasicLSTMCell(lstm_size)
# lstm_back: The LSTM used for looking backwards
# through the sentences, similar to lstm.
# input_p: the probability that inputs to the LSTM will be retained at each
# iteration of dropout.
# output_p: the probability that outputs from the LSTM will be retained at
# each iteration of dropout.
lstm_drop_back = tf.contrib.rnn.DropoutWrapper(lstm_back, input_p, output_p)
# lstm_drop_back: A dropout wrapper for lstm_back, like lstm_drop.
fc_initializer = tf.random_normal_initializer(stddev=0.1)
# fc_initializer: initial values for the fully connected layer's weights.
# hidden_size: the size of the outputs from each lstm layer.
# Multiplied by 2 to account for the two LSTMs.
fc_weight = tf.get_variable('fc_weight', [2*hidden_size, 3],
initializer = fc_initializer)
# fc_weight: Storage for the fully connected layer's weights.
fc_bias = tf.get_variable('bias', [3])
# fc_bias: Storage for the fully connected layer's bias.
# tf.GraphKeys.REGULARIZATION_LOSSES: A key to a collection in the graph
# designated for losses due to regularization.
# In this case, this portion of loss is regularization on the weights
# for the fully connected layer.
x = tf.concat([hyp, evi], 1) # N, (Lh+Le), d
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2]) # (Le+Lh), N, d
# Reshaping to (n_steps*batch_size, n_input)
x = tf.reshape(x, [-1, vector_size]) # (Le+Lh)*N, d
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(x, l_seq,)
# x: the inputs to the bidirectional_rnn
# tf.contrib.rnn.static_bidirectional_rnn: Runs the input through
# two recurrent networks, one that runs the inputs forward and one
# that runs the inputs in reversed order, combining the outputs.
rnn_outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm, lstm_back,
x, dtype=tf.float32)
# rnn_outputs: the list of LSTM outputs, as a list.
# What we want is the latest output, rnn_outputs[-1]
classification_scores = tf.matmul(rnn_outputs[-1], fc_weight) + fc_bias
# The scores are relative certainties for how likely the output matches
# a certain entailment:
# 0: Positive entailment
# 1: Neutral entailment
# 2: Negative entailment
with tf.variable_scope('Accuracy'):
predicts = tf.cast(tf.argmax(classification_scores, 1), 'int32')
y_label = tf.cast(tf.argmax(y, 1), 'int32')
corrects = tf.equal(predicts, y_label)
num_corrects = tf.reduce_sum(tf.cast(corrects, tf.float32))
accuracy = tf.reduce_mean(tf.cast(corrects, tf.float32))
with tf.variable_scope("loss"):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits = classification_scores, labels = y)
loss = tf.reduce_mean(cross_entropy)
total_loss = loss + weight_decay * tf.add_n(
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
opt_op = optimizer.minimize(total_loss)
# Initialize variables
init = tf.global_variables_initializer()
# Use TQDM if installed
tqdm_installed = False
from tqdm import tqdm
tqdm_installed = True
# Launch the Tensorflow session
sess = tf.Session()
writer = tf.summary.FileWriter('/log', sess.graph) # write to file
merge_op = tf.summary.merge_all() # operation to merge all summary
# training_iterations_count: The number of data pieces to train on in total
# batch_size: The number of data pieces per batch
training_iterations = range(0,training_iterations_count,batch_size)
if tqdm_installed:
# Add a progress bar if TQDM is installed
training_iterations = tqdm(training_iterations)
for i in training_iterations:
# Select indices for a random data subset
batch = np.random.randint(data_feature_list[0].shape[0], size=batch_size)
# Use the selected subset indices to initialize the graph's
# placeholder values
hyps, evis, ys = (data_feature_list[0][batch,:],
# Run the optimization with these initialized values
r =[opt_op], feed_dict={hyp: hyps, evi: evis, y: ys})
# display_step: how often the accuracy and loss should
# be tested and displayed.
if (i/batch_size) % display_step == 0:
# Calculate batch accuracy
acc =, feed_dict={hyp: hyps, evi: evis, y: ys})
# Calculate batch loss
tmp_loss =, feed_dict={hyp: hyps, evi: evis, y: ys})
# Display results
print("Iter " + str(i/batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(tmp_loss) + ", Training Accuracy= " + \
summary = tf.Summary(value=[tf.Summary.Value(tag="Accuracy",
writer.add_summary(summary, i)
evidences = ["Janos and Jade both were at the scene of the car crash."]
hypotheses = ["Multiple people saw the accident."]
sentence1 = [fit_to_size(np.vstack(sentence2sequence(evidence)[0]),
(30, 50)) for evidence in evidences]
sentence2 = [fit_to_size(np.vstack(sentence2sequence(hypothesis)[0]),
(30,50)) for hypothesis in hypotheses]
prediction =, feed_dict={hyp: (sentence1 * N),
evi: (sentence2 * N),
y: [[0,0,0]]*N})
print(["Positive", "Neutral", "Negative"][np.argmax(prediction[0])]+
" entailment")

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Orbifold commented Jun 21, 2018

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