Exemplos de InputHelper.getTestDataSet em Python

Exemplo n.º 1

EVAL_FILEPATH = 'validation.txt0'
# 词表（在训练过程中已生成）
VOCAB_FILEPATH = 'runs/1528462228/checkpoints/vocab'
# 模型文件
MODEL = 'runs/1528462228/checkpoints/model-10000'

# 语句最多长度(包含多少个词)
MAX_DOCUMENT_LENGTH = 30

# Misc Parameters
ALLOW_SOFT_PLACEMENT = True
LOG_DEVICE_PLACEMENT = False

inpH = InputHelper()

x1_test, x2_test, y_test = inpH.getTestDataSet(EVAL_FILEPATH, VOCAB_FILEPATH, MAX_DOCUMENT_LENGTH)

# for index ,value in enumerate(x1_test):
#     print (index, x1_test[index], x2_test[index], y_test[index])
# sys.exit(0)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = MODEL
print checkpoint_file
graph = tf.Graph()
with graph.as_default():
    session_conf = tf.ConfigProto(
        allow_soft_placement=ALLOW_SOFT_PLACEMENT,

Exemplo n.º 2

                        "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("")

if FLAGS.eval_filepath == None or FLAGS.vocab_filepath == None or FLAGS.model == None:
    print("Eval or Vocab filepaths are empty.")
    exit()

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test, x2_test, y_test = inpH.getTestDataSet(FLAGS.eval_filepath,
                                               FLAGS.vocab_filepath, 300)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print checkpoint_file
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

Exemplo n.º 3

Arquivo: eval.py Projeto: maowenqiang1015/sigir

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

if FLAGS.eval_filepath==None or FLAGS.vocab_filepath==None or FLAGS.model==None :
    print("Eval or Vocab filepaths are empty.")
    exit()

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test,x2_test,y_test,x1_temp,x2_temp,add_fea_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath, 30)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print checkpoint_file
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

Exemplo n.º 4

                        "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("")

if FLAGS.eval_filepath == None or FLAGS.vocab_filepath == None or FLAGS.model == None:
    print("Eval or Vocab filepaths are empty.")
    exit()

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x_test, y_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath,
                                     600, 5)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print checkpoint_file
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

Exemplo n.º 5

Arquivo: eval.py Projeto: maowenqiang1015/sigir

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

if FLAGS.eval_filepath == None or FLAGS.vocab_filepath == None or FLAGS.model == None:
    print("Eval or Vocab filepaths are empty.")
    exit()

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test, x2_test, ent_x1_test, ent_x2_test, y_test, x1_temp, x2_temp, add_fea_test = inpH.getTestDataSet(
    FLAGS.eval_filepath, FLAGS.eval_labeled_filepath, FLAGS.vocab_filepath,
    max_document_length)
#embedding_matrix = inpH.getEmbeddings(FLAGS.embedding_file,FLAGS.embedding_dim)
#entity_embedding_matrix = inpH.getEntityEmbeddings(FLAGS.entity_embedding_file,FLAGS.hidden_units)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print checkpoint_file
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)

Exemplo n.º 6

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

if FLAGS.eval_filepath==None or FLAGS.model==None :
    print("Eval or Vocab filepaths are empty.")
    exit()

w2v, model_dict, index_to_word = load_word_2vec_model.get_model_embeddings()
# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test,x2_test,ids = inpH.getTestDataSet(FLAGS.eval_filepath, 30, model_dict)

wr = open('submissions_train.csv', 'w')
print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print checkpoint_file
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():

Exemplo n.º 7

Arquivo: eval.py Projeto: alphanlp/deep-siamese-text-similarity

# Eval Parameters
batch_size = 64  # 批大小
vocab_filepath = './vocab/vocab'  #训练使使用的词表
model = './models/model-11000'  #加载训练模型
allow_soft_placement = True
log_device_placement = False
confidence = 0.8

if eval_file == None or vocab_filepath == None or model == None:
    print("Eval or Vocab filepaths are empty.")
    exit()

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test, x2_test = inpH.getTestDataSet(eval_file, vocab_filepath, 30)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = model
print checkpoint_file
graph = tf.Graph()
with graph.as_default():
    session_conf = tf.ConfigProto(allow_soft_placement=allow_soft_placement,
                                  log_device_placement=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))

Exemplo n.º 8

# 批大小
BATCH_SIZE = 64
# 验证集文件
EVAL_FILEPATH = 'validation.txt0'
# 词表（在训练过程中已生成）
VOCAB_FILEPATH = 'runs/1527909561/checkpoints/vocab'
# 模型文件
MODEL = 'runs/1527909561/checkpoints/model-20000'

# Misc Parameters
ALLOW_SOFT_PLACEMENT = True
LOG_DEVICE_PLACEMENT = False

inpH = InputHelper()

x1_test, x2_test, y_test = inpH.getTestDataSet(EVAL_FILEPATH, VOCAB_FILEPATH, 30)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = MODEL
print checkpoint_file
graph = tf.Graph()
with graph.as_default():
    session_conf = tf.ConfigProto(
        allow_soft_placement=ALLOW_SOFT_PLACEMENT,
        log_device_placement=LOG_DEVICE_PLACEMENT)
    sess = tf.Session(config=session_conf)
    with sess.as_default():
        # Load the saved meta graph and restore variables

Exemplo n.º 9

Arquivo: eval.py Projeto: kumamandala/deep-siamese-text-similarity-1

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

if FLAGS.eval_filepath==None or FLAGS.vocab_filepath==None or FLAGS.model==None :
    print("Eval or Vocab filepaths are empty.")
    exit()

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test,x2_test,y_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath, 30)

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = FLAGS.model
print checkpoint_file
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

Exemplo n.º 10

Arquivo: eval.py Projeto: kumamandala/deep-siamese-text-similarity

print (EVAL_FILE)
print (OUTPUT_FILE)

# Eval Parameters
BATCH_SIZE = 64  # 批大小
VOCAB_FILE = './vocab/vocab'  # 训练使使用的词表
MODEL = './models/model-4000'  # 加载训练模型
ALLOW_SOFT_PLACEMENT = True
LOG_DEVICE_PLACEMENT = False

# 语句最多长度(包含多少个词)
MAX_DOCUMENT_LENGTH = 40

# load data and map id-transform based on training time vocabulary
inpH = InputHelper()
x1_test, x2_test = inpH.getTestDataSet(EVAL_FILE, VOCAB_FILE, MAX_DOCUMENT_LENGTH)

# for index, _ in enumerate(x1_test):
#     print(index, x1_test[index], x2_test[index])

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = MODEL
print checkpoint_file
graph = tf.Graph()
with graph.as_default():
    session_conf = tf.ConfigProto(
        allow_soft_placement=ALLOW_SOFT_PLACEMENT,
        log_device_placement=LOG_DEVICE_PLACEMENT)