def data_construct(input_file, batch_size, vocab, max_length=100, config=None):
logger.info('Loading data...')
x_test, contents, labels, y_test = data_helpers.load_test_data(
input_file, max_length, vocab, config)
if config:
test_iter = mx.io.NDArrayIter(x_test, y_test, batch_size)
else:
test_iter = mx.io.NDArrayIter(x_test, None, batch_size)
return test_iter, contents, labels
def main():
x = data_helpers.load_test_data('data/test.csv')
a = inference(x)
print(a)
np.savetxt('submission_softmax.csv',
np.c_[range(1,
len(x) + 1), a],
delimiter=',',
header='ImageId,Label',
comments='',
fmt='%d')
def main():
trained_model = "checkpoints/model.ckpt"
embedding_size = 100 # Word embedding dimension
batch_size = 128 # Batch data size
sequence_length = 300 # Sentence length
rnn_size = 50 # Number of hidden layer neurons
attention_matrix_size = 100
margin = 0.1
gpu_mem_usage = 0.75
gpu_device = "/gpu:0"
embeddings, word2idx = data_helpers.load_embedding('vectors.nobin')
voc = data_helpers.load_vocab(
'D:\\DataMining\\Datasets\\insuranceQA\\V1\\vocabulary')
all_answers = data_helpers.load_answers(
'D:\\DataMining\\Datasets\\insuranceQA\\V1\\answers.label.token_idx',
voc)
questions, answers, labels, qids, aids = data_helpers.load_test_data(
'D:\\DataMining\\Datasets\\insuranceQA\\V1\\question.test1.label.token_idx.pool',
all_answers, voc, word2idx, 300)
with tf.Graph().as_default(), tf.device(gpu_device):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_mem_usage)
session_conf = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
model = QALSTM(batch_size, sequence_length, embeddings, embedding_size,
rnn_size, margin, attention_matrix_size)
with tf.Session(config=session_conf).as_default(
) as sess: # config=session_conf
saver = tf.train.Saver()
print("Start loading the model")
saver.restore(sess, trained_model)
print("The model is loaded")
scores = []
for question, answer in data_helpers.test_batch_iter(
questions, answers, batch_size):
feed_dict = {model.qtest: question, model.atest: answer}
score = sess.run([model.scores], feed_dict)
scores.extend(score[0].tolist())
MAP, MRR = eval_map_mrr(qids, aids, scores, labels)
print('MAP %2.3f\tMRR %2.3f' % (MAP, MRR))
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
# Data Preparatopn
# ==================================================
# Load data
print("Loading data...")
x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file,
FLAGS.negative_data_file)
x_eval = data_helpers.load_test_data(FLAGS.test_data_file)
# Pad sentences
sentences_padded_all, max_length = data_helpers.pad_sentences(x_text + x_eval)
sentences_padded, max_length = data_helpers.pad_sentences(x_text, max_length)
# Build vocabulary
vocabulary, vocabulary_inv = data_helpers.build_vocab(sentences_padded_all)
x, y = data_helpers.build_input_data(sentences_padded, y, vocabulary)
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
seq_len = len(tmp_list)
if seq_len > max_len:
seq_len = max_len
real_len.append(seq_len)
return real_len
def load_train_params(train_dir):
sorted_label = json.loads(open(train_dir + '/sorted_label.json').read())
train_params = json.loads(open(train_dir + '/train_params.json').read())
return sorted_label,train_params
# CHANGE THIS: Load data. Load your own data here
if FLAGS.eval_train:
train_dir = os.path.join(FLAGS.checkpoint_dir, "..", "trained_results")
sorted_label, train_params = load_train_params(train_dir)
x_raw, y_test = data_helpers.load_test_data(FLAGS.test_data_file, sorted_label)
y_test = np.argmax(y_test, axis=1)
else:
x_raw = ["a masterpiece four years in the making", "everything is off."]
y_test = [1, 0]
# Map data into vocabulary
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
x_real_len_test = np.array(get_real_len(x_raw, train_params['max_document_length']))
x_test = np.array(list(vocab_processor.transform(x_raw)))
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
def eval():
# Map data into vocabulary
source_vocab_path = os.path.join(FLAGS.checkpoint_dir, "..",
"source_vocab")
source_vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor.restore(
source_vocab_path)
source_max_sentence_length = len(
list(source_vocab_processor.transform(['test']))[0])
target_vocab_path = os.path.join(FLAGS.checkpoint_dir, "..",
"target_vocab")
target_vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor.restore(
target_vocab_path)
target_max_sentence_length = len(
list(target_vocab_processor.transform(['test']))[0])
with tf.device('/cpu:0'):
source_sent, target_sent = data_helpers.load_test_data(
FLAGS.test_source_dir, FLAGS.test_target_dir,
source_max_sentence_length, target_max_sentence_length)
source_eval = np.array(list(source_vocab_processor.transform(source_sent)))
target_eval = np.array(list(target_vocab_processor.transform(target_sent)))
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
source = graph.get_operation_by_name("encoder_x").outputs[0]
target = graph.get_operation_by_name("decoder_y").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name(
"output/predictions").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(list(source_eval),
FLAGS.batch_size,
1,
shuffle=False)
# Collect the predictions here
all_predictions = np.empty([0, target_max_sentence_length], int)
for batch in batches:
# auto-regressive infer
batch_predictions = np.ones_like(batch)
for j in range(target_max_sentence_length):
pred = sess.run(predictions,
feed_dict={
source: batch,
target: batch_predictions
})
batch_predictions[:, j] = pred[:, j]
all_predictions = np.concatenate(
[all_predictions, batch_predictions])
is_target = np.not_equal(target_eval, 0).astype(float)
accuracy = np.sum(
np.equal(all_predictions, target_eval).astype(float) *
is_target) / np.sum(is_target)
print("Total number of test examples: {}\n".format(
len(target_eval)))
print("Accuracy: {:g}".format(accuracy))
prediction_sent = []
for idx_seq in all_predictions:
prediction_sent.append(" ".join(
target_vocab_processor.vocabulary_.reverse(idx)
for idx in idx_seq))
# BLEU Score
list_of_references = []
hypotheses = []
for pred, target in zip(prediction_sent, target_sent):
if len(pred.split()) > 3 and len(target.split()) > 3:
list_of_references.append([pred.split()])
hypotheses.append(target.split())
chencherry = SmoothingFunction()
score = corpus_bleu(list_of_references,
hypotheses,
smoothing_function=chencherry.method4)
print("BLEU Score : {:g}\n".format(score * 100))
# Samples of Translation Result
if not os.path.exists('results'): os.mkdir('results')
f = open(FLAGS.output_dir, 'w')
for idx, (s, t, p) in enumerate(
zip(source_sent, target_sent, prediction_sent)):
f.write("Sample #%d\n" % idx)
f.write("Source : %s\n" % s)
f.write("Target : %s\n" % t)
f.write("Predict : %s\n\n" % p)
f.close()
def predict(data, params_path=FLAGS.checkpoint_dir):
num_sent = len(data)
mask = np.ones(shape=[FLAGS.sequence_length]).nonzero()
with tf.device('/cpu:0'):
x_text_ta, x_position_ta, x_id_ta, x_text_av, x_position_av, x_id_av = data_helpers.load_test_data(data)
checkpoint_file = params_path + '/'
# find Time & Attribute tuples
checkpoint_file_ta = checkpoint_file + 'model_ta_final'
graph = tf.Graph()
with 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 = FLAGS.gpu_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file_ta))
saver.restore(sess, checkpoint_file_ta)
# Get the placeholders from the graph by name
input_text = graph.get_operation_by_name("input_text").outputs[0]
input_position = graph.get_operation_by_name("input_position").outputs[0]
input_mask = graph.get_operation_by_name("position_mask").outputs[0]
emb_dropout_keep_prob = graph.get_operation_by_name("emb_dropout_keep_prob").outputs[0]
rnn_dropout_keep_prob = graph.get_operation_by_name("rnn_dropout_keep_prob").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
probablities = graph.get_operation_by_name("output/probabilities").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(x_text_ta, x_position_ta, None, FLAGS.batch_size, 1, shuffle=False)
# Collect the predictions here
preds = []
probs = []
for x_batch in batches:
x_text_batch, x_position_batch = x_batch
pred, prob = sess.run([predictions, probablities], {input_text: x_text_batch,
input_position: x_position_batch,
input_mask: mask,
emb_dropout_keep_prob: 1.0,
rnn_dropout_keep_prob: 1.0,
dropout_keep_prob: 1.0})
preds.append(pred)
probs.append(prob[:, 1])
preds = np.concatenate(preds)
probs = np.concatenate(probs)
time_attr = np.concatenate((x_position_ta, probs[:, None]), axis=1)
mask_ta = np.where(preds == 1)
id_ta = x_id_ta[mask_ta].copy()
time_attr = time_attr[mask_ta].copy()
# find Attribute & Value tuples
checkpoint_file_av = checkpoint_file + 'model_av_final'
graph = tf.Graph()
with 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 = FLAGS.gpu_allow_growth
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file_av))
saver.restore(sess, checkpoint_file_av)
# Get the placeholders from the graph by name
input_text = graph.get_operation_by_name("input_text").outputs[0]
input_position = graph.get_operation_by_name("input_position").outputs[0]
input_mask = graph.get_operation_by_name("position_mask").outputs[0]
emb_dropout_keep_prob = graph.get_operation_by_name("emb_dropout_keep_prob").outputs[0]
rnn_dropout_keep_prob = graph.get_operation_by_name("rnn_dropout_keep_prob").outputs[0]
dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
probablities = graph.get_operation_by_name("output/probabilities").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(x_text_av, x_position_av, None, FLAGS.batch_size, 1, shuffle=False)
# Collect the predictions here
preds = []
probs = []
for x_batch in batches:
x_text_batch, x_position_batch = x_batch
pred, prob = sess.run([predictions, probablities], {input_text: x_text_batch,
input_position: x_position_batch,
input_mask: mask,
emb_dropout_keep_prob: 1.0,
rnn_dropout_keep_prob: 1.0,
dropout_keep_prob: 1.0})
preds.append(pred)
probs.append(prob[:, 1])
preds = np.concatenate(preds)
probs = np.concatenate(probs)
attr_value = np.concatenate((x_position_av, probs[:, None]), axis=1)
mask_av = np.where(preds == 1)
id_av = x_id_av[mask_av].copy()
attr_value = attr_value[mask_av].copy()
# combining (time, attribute, value) tuples
two_tuples = []
for id in range(num_sent):
mask_ta = np.where(id_ta == id)
mask_av = np.where(id_av == id)
two_tuples.append({'time_attr': time_attr[mask_ta], 'attr_val': attr_value[mask_av]})
results = []
for item in two_tuples:
results.append(Two2Three(item))
for id in range(num_sent):
data[id]['results'] = results[id].tolist()
return data
def test_cnn(test_examples, test_labels, checkpoint_file, vocabulary):
# Eval Parameters
#tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
#tf.flags.DEFINE_string("checkpoint_dir", checkpoint_dir, "checkpoint directory from training run")
# 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 data. Load your own data here
print("Loading data...")
x_test, y_test, vocabulary, vocabulary_inv = data_helpers.load_test_data(
test_examples, test_labels, vocabulary)
#x_test, y_test, vocabulary, vocabulary_inv = data_helpers.load_data()
y_test = np.argmax(y_test, axis=1)
print("Vocabulary size: {:d}".format(len(vocabulary)))
print("Test set size {:d}".format(len(y_test)))
print("\nEvaluating...\n")
# Evaluation
# ==================================================
#checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
graph = tf.Graph()
#with graph.as_default(), tf.device('/gpu:2'):
with graph.as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.10,
# allow_growth = True)
gpu_options = tf.GPUOptions(allow_growth=True)
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement,
gpu_options=gpu_options)
sess = tf.Session(config=session_conf)
with sess.as_default():
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
# Get the placeholders from the graph by name
input_x = graph.get_operation_by_name("input_x").outputs[0]
# input_y = graph.get_operation_by_name("input_y").outputs[0]
dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
predictions = graph.get_operation_by_name(
"output/predictions").outputs[0]
# Generate batches for one epoch
batches = data_helpers.batch_iter(x_test,
FLAGS.batch_size,
1,
shuffle=False)
# Collect the predictions here
all_predictions = []
for x_test_batch in batches:
batch_predictions = sess.run(predictions, {
input_x: x_test_batch,
dropout_keep_prob: 1.0
})
all_predictions = np.concatenate(
[all_predictions, batch_predictions])
# Print accuracy and fscores
correct_predictions = float(sum(all_predictions == y_test))
print("Total number of test examples: {}".format(len(y_test)))
print("Accuracy: {:g}".format(correct_predictions / float(len(y_test))))
"""
gold_labels = []
for gold_label in y_test:
gold_labels.append(np.argmax(gold_label))
predicted_labels = []
for prediction in all_predictions:
predicted_labels.append(np.argmax(prediction))
"""
return all_predictions
initializer(name, arg_dict[name])
param_blocks.append( (i, arg_dict[name], args_grad[name], name) )
data = cnn_exec.arg_dict['data']
label = cnn_exec.arg_dict['softmax_label']
return CNNModel(cnn_exec=cnn_exec, symbol=cnn, data=data, label=label, param_blocks=param_blocks)
pkl_file = open('vocab.pkl', 'rb')
vocab = pickle.load(pkl_file)
pkl_file.close()
sentence=data_helpers.load_test_data()
sentences_padded = data_helpers.pad_sentences(sentence)
sentence_test=[]
for sent in sentences_padded:
l=[]
for word in sent:
if word in vocab:
l.append(vocab[word])
else:
l.append(0)
sentence_test.append(l)
sentence_test=np.array(sentence_test)
vocab_size = len(vocab)
num_embed = 50
batch_size = 100
tf.app.flags.DEFINE_boolean('log_device_placement', False,
'Demonstrate which variables are on what device.')
# Store all elemnts in FLAG structure!
FLAGS = tf.app.flags.FLAGS
if not os.path.isabs(FLAGS.train_dir):
raise ValueError('You must assign absolute path for --train_dir')
if not os.path.isabs(FLAGS.checkpoint_dir):
raise ValueError('You must assign absolute path for --checkpoint_dir')
maybe_download_and_extract()
images_train, cls_train, labels_train = load_training_data()
images_test, cls_test, labels_test = load_test_data()
tensors_key = ['images_train', 'labels_train', 'images_test', 'labels_test']
tensors = [images_train, labels_train, images_test, labels_test]
data = dict(zip(tensors_key, tensors))
num_train_samples = images_train.shape[0]
height = 32
width = 32
num_channels = 3
print(load_class_names())
graph = tf.Graph()
with graph.as_default():
global_step = tf.Variable(0, name="global_step", trainable=False)
"Checkpoint directory from training run")
# 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("")
x_raw = data_helpers.load_test_data(
'/Users/Winnerineast/Documents/haodaifu/NewData/tobetrained.csv')
# Map data into vocabulary
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocabulary, vocabulary_inv, max_length = data_helpers.restore_vocabulary(
vocab_path)
sentences_padded, tmp_length = data_helpers.pad_sentences(x_raw, max_length)
x_test, y_test = data_helpers.build_input_data(sentences_padded, None,
vocabulary)
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
