batches = data_helpers.batch_iter(list(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 if y_test is defined
if y_test is not None:
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))))
# Save the evaluation to a csv
class_predictions = data_loader.class_labels(all_predictions.astype(int))
predictions_human_readable = np.column_stack(
(np.array(x_raw), class_predictions))
out_path = os.path.join(FLAGS.checkpoint_dir, "../../../", "prediction.csv")
print("Saving evaluation to {0}".format(out_path))
with open(out_path, 'w') as f:
csv.writer(f).writerows(predictions_human_readable)
# Tensors we want to evaluate
predictions = graph.get_operation_by_name("output/predictions").outputs[0]
# Make Server
sock=socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = '127.0.0.1'
port = int(3000)
sock.bind((host, port))
sock.listen(1)
print("text_predictor Start...\n")
while True:
connection, client_addr = sock.accept()
# print(connection, client_addr)
data = connection.recv(1024)
data = data.decode("utf-8")
print("data > "+data)
arr = []
arr.append(data.strip())
# print(arr)
x_test = np.array(list(vocab_processor.transform(arr)))
# print(x_test)
pred = sess.run(predictions, {input_x: x_test,dropout_keep_prob: 1.0})
# print(pred)
answer = np.concatenate([pred])
# print(answer)
answer = data_loader.class_labels(answer.astype(int))
print("answ > "+answer[0])
connection.sendall(answer[0].encode("utf-8"))
connection.close()
def startEval(ocrData):
# Parameters
# ==================================================
del_all_flags(tf.flags.FLAGS)
ocrData = json.loads(
'[{"location":"1018,240,411,87","text":"APEX"},{"location":"1019,338,409,23","text":"Partner of Choice"},{"location":"1562,509,178,25","text":"Voucher No"},{"location":"1562,578,206,25","text":"Voucher Date"},{"location":"206,691,274,27","text":"4153 Korean Re"},{"location":"208,756,525,34","text":"Proportional Treaty Statement"},{"location":"1842,506,344,25","text":"BV/HEO/2018/05/0626"},{"location":"1840,575,169,25","text":"01105/2018"},{"location":"206,848,111,24","text":"Cedant"},{"location":"206,908,285,24","text":"Class of Business"},{"location":"210,963,272,26","text":"Period of Quarter"},{"location":"207,1017,252,31","text":"Period of Treaty"},{"location":"206,1066,227,24","text":"Our Reference"},{"location":"226,1174,145,31","text":"Currency"},{"location":"227,1243,139,24","text":"Premium"},{"location":"226,1303,197,24","text":"Commission"},{"location":"226,1366,107,24","text":"Claims"},{"location":"227,1426,126,24","text":"Reserve"},{"location":"227,1489,123,24","text":"Release"},{"location":"227,1549,117,24","text":"Interest"},{"location":"227,1609,161,31","text":"Brokerage"},{"location":"233,1678,134,24","text":"Portfolio"},{"location":"227,1781,124,24","text":"Balance"},{"location":"574,847,492,32","text":": Solidarity- First Insurance 2018"},{"location":"574,907,568,32","text":": Marine Cargo Surplus 2018 - Inward"},{"location":"598,959,433,25","text":"01-01-2018 TO 31-03-2018"},{"location":"574,1010,454,25","text":": 01-01-2018 TO 31-12-2018"},{"location":"574,1065,304,25","text":": APEX/BORD/2727"},{"location":"629,1173,171,25","text":"JOD 1.00"},{"location":"639,1239,83,25","text":"25.53"},{"location":"639,1299,64,25","text":"5.74"},{"location":"639,1362,64,25","text":"0.00"},{"location":"639,1422,64,25","text":"7.66"},{"location":"639,1485,64,25","text":"0.00"},{"location":"639,1545,64,25","text":"0.00"},{"location":"639,1605,64,25","text":"0.64"},{"location":"648,1677,64,25","text":"0.00"},{"location":"641,1774,81,25","text":"11 .49"},{"location":"1706,1908,356,29","text":"APEX INSURANCE"}]'
)
# print('============startEval================')
# ocrData = json.loads(ocrData)
# print(ocrData)
# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "",
"Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", False,
"Evaluate on all training data")
# 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")
data_loader = MultiClassDataLoader(tf.flags, WordDataProcessor())
data_loader.define_flags()
FLAGS = tf.flags.FLAGS
# FLAGS._parse_flags()
FLAGS(sys.argv)
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{}={}".format(attr.upper(), value))
print("")
if FLAGS.eval_train:
x_raw, y_test = data_loader.load_data_and_labels()
y_test = np.argmax(y_test, axis=1)
else:
# x_raw, y_test = data_loader.load_dev_data_and_labels()
x_raw, y_test = data_loader.load_dev_data_and_labels_json(ocrData)
y_test = np.argmax(y_test, axis=1)
# checkpoint_dir이 없다면 가장 최근 dir 추출하여 셋팅
if FLAGS.checkpoint_dir == "":
all_subdirs = [
"./runs/" + d for d in os.listdir('./runs/.')
if os.path.isdir("./runs/" + d)
]
latest_subdir = max(all_subdirs, key=os.path.getmtime)
FLAGS.checkpoint_dir = latest_subdir + "/checkpoints/"
# Map data into vocabulary
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocab_processor = data_loader.restore_vocab_processor(vocab_path)
x_test = np.array(list(vocab_processor.transform(x_raw)))
print("\nEvaluating...\n")
# Evaluation
# ==================================================
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
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(list(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 if y_test is defined
if y_test is not None:
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))))
# Save the evaluation to a csv
class_predictions = data_loader.class_labels(all_predictions.astype(int))
predictions_human_readable = np.column_stack(
(np.array(x_raw), class_predictions))
out_path = os.path.join(FLAGS.checkpoint_dir, "../../../",
"prediction.csv")
print("Saving evaluation to {0}".format(out_path))
for i in predictions_human_readable:
for row in ocrData:
if i[0].lower() == row['text'].lower():
row['colLbl'] = i[1]
print(ocrData)
return ocrData