def evaluate_sentence(sentence, vocabulary):
"""
Translates a string to its equivalent in the integer vocabulary and feeds it to the network.
Outputs result to stdout.
"""
#print(current_milli_time())
x_to_eval = data_helpers.string_to_int(sentence, vocabulary, maxLengthInX)
#print(current_milli_time())
#result = sess.run(tf.argmax(network_out,1), feed_dict={data_in: x_to_eval, dropout_keep_prob: 1.0})
#print(current_milli_time())
unnorm_result = sess.run(network_out,
feed_dict={
data_in: x_to_eval,
dropout_keep_prob: 1.0
})
#print(current_milli_time())
network_sentiment = "NEU"
if unnorm_result[0].item(1) > 0.6:
network_sentiment = "POS"
elif unnorm_result[0].item(1) < 0.4:
network_sentiment = "NEG"
log("Custom input evaluation:", network_sentiment)
log("Actual output:", str(unnorm_result[0]))
return network_sentiment
def evaluate_sentence(sentence, vocabulary):
x_to_eval = string_to_int(clean_str(sentence), vocabulary, max(len(_) for _ in x))
result = sess.run(tf.argmax(network_out, 1),
feed_dict={data_in: x_to_eval,
dropout_keep_prob: 1.0})
unorm_result = sess.run(network_out, feed_dict={data_in: x_to_eval,
dropout_keep_prob: 1.0})
return result[0]
def evaluate_sentence(sentence, vocabulary):
"""
Translates a string to its equivalent in the integer vocabulary and feeds it to the network.
Outputs result to stdout.
"""
x_to_eval = data_helpers.string_to_int(sentence, vocabulary, max(len(i) for i in x))
result = sess.run(tf.argmax(network_out,1), feed_dict={data_in: x_to_eval, dropout_keep_prob: 1.0})
unnorm_result = sess.run(network_out, feed_dict={data_in: x_to_eval, dropout_keep_prob: 1.0})
network_sentiment = "POS" if result == 1 else "NEG"
log("Custom input evaluation:", network_sentiment)
log("Actual output:", str(unnorm_result[0]))
def evaluate_sentence(sentence, vocabulary):
"""
Translates a string to its equivalent in the integer vocabulary and feeds it
to the network.
Outputs result to stdout.
"""
x_to_eval = string_to_int(sentence, vocabulary, max(len(_) for _ in x))
result = sess.run(tf.argmax(network_out, 1),
feed_dict={data_in: x_to_eval,
dropout_keep_prob: 1.0})
unnorm_result = sess.run(network_out, feed_dict={data_in: x_to_eval,
dropout_keep_prob: 1.0})
network_sentiment = 'POS' if result == 1 else 'NEG'
log('Custom input evaluation:', network_sentiment)
log('Actual output:', str(unnorm_result[0]))
def evaluate_sentence(sentence):
x_to_eval = string_to_int(sentence["text_cleaned"], vocabulary,
max(len(_) for _ in x))
result = sess.run(tf.argmax(network_out, 1),
feed_dict={
data_in: x_to_eval,
dropout_keep_prob: 1.0
})
# unnorm_result = sess.run(network_out, feed_dict={data_in: x_to_eval,
# dropout_keep_prob: 1.0})
network_sentiment = 'POS' if result == 1 else 'NEG'
# log('Custom input evaluation:', network_sentiment)
# log('Actual output:', str(unnorm_result[0]))
if result is not None:
return result[0]
else:
return 'ERROR'
def evaluate_sentence(id, sentence, vocabulary):
"""
Translates a string to its equivalent in the integer vocabulary and feeds it
to the network.
Outputs result to stdout.
"""
x_to_eval = string_to_int(sentence, vocabulary, max(len(_) for _ in x))
result = sess.run(tf.argmax(network_out, 1),
feed_dict={
data_in: x_to_eval,
dropout_keep_prob: 1.0
})
unnorm_result = sess.run(network_out,
feed_dict={
data_in: x_to_eval,
dropout_keep_prob: 1.0
})
network_sentiment = '1' if result == 1 else '-1'
log(id + "," + network_sentiment)
def evaluate_sentence(sentence, vocabulary):
"""
Translates a string to its equivalent in the integer vocabulary and feeds it to the network.
Outputs result to stdout.
"""
#print(current_milli_time())
x_to_eval = data_helpers.string_to_int(sentence, vocabulary, maxLengthInX)
#print(current_milli_time())
#result = sess.run(tf.argmax(network_out,1), feed_dict={data_in: x_to_eval, dropout_keep_prob: 1.0})
#print(current_milli_time())
unnorm_result = sess.run(network_out, feed_dict={data_in: x_to_eval, dropout_keep_prob: 1.0})
#print(current_milli_time())
network_sentiment = "NEU"
if unnorm_result[0].item(1) > 0.6:
network_sentiment = "POS"
elif unnorm_result[0].item(1) < 0.4:
network_sentiment = "NEG"
log("Custom input evaluation:", network_sentiment)
log("Actual output:", str(unnorm_result[0]))
return network_sentiment
RUN_DIR,
'minimal_graph.txt',
as_text=True)
if FLAGS.submit:
log('Loading submit data')
submit_examples = list(
open("../twitter-datasets/test_data.txt", "r").readlines())
submit_examples = [s.strip() for s in submit_examples]
splitter = [s.split(',', 1) for s in submit_examples]
sentences = [s[1] for s in splitter]
ids = [s[0] for s in splitter]
#evaluate_sentence(tweet_id, tweet_data, vocabulary)
max_len = max(len(_) for _ in x)
x_to_eval = [
string_to_int(sentence, vocabulary, max_len)[0]
for sentence in sentences
]
log('generating submissions data')
result = sess.run(tf.argmax(network_out, 1),
feed_dict={
data_in: x_to_eval,
dropout_keep_prob: 1.0
})
result = ['1' if r == 1 else '-1' for r in result]
log('saving submissions')
with open("../twitter-datasets/tscnnsubmission.csv", "w") as f:
f.write("Id,Prediction\n")
for i, p in enumerate(result):
f.write(str(ids[i]) + "," + str(p) + "\n")
