<commit_before>from model import CRNN, CtcCriterion
from dataset import DatasetLmdb, SynthLmdb
import os
import tensorflow as tf
import numpy as np
import signal
import utility
import sys
import time

class Conf:
	def __init__(self):
		self.nClasses = 36
		self.trainBatchSize = 64
		self.evalBatchSize = 200
		self.testBatchSize = 10
		self.maxIteration = 2000000
		self.displayInterval = 1
		self.evalInterval = 1000
		self.testInterval = 2000
		self.saveInterval = 50000
		self.modelDir = os.path.abspath(os.path.join('..', 'model', 'ckpt'))
		self.dataSet = os.path.join('..', 'data', 'Synth')
		self.auxDataSet = os.path.join('..', 'data', 'aux_Synth')
		# self.dataSet = os.path.join('..', 'data', 'IIIT5K')
		self.maxLength = 24


if __name__ == '__main__':
	gConfig = Conf()
	sess = tf.InteractiveSession()

	ckpt = utility.checkPointLoader(gConfig.modelDir)
	imgs = tf.placeholder(tf.float32, [None, 32, 100])
	labels = tf.sparse_placeholder(tf.int32)
	batches = tf.placeholder(tf.int32, [None])
	isTraining = tf.placeholder(tf.bool)
	keepProb = tf.placeholder(tf.float32)
	pred_labels = tf.placeholder(tf.string, [None])
	true_labels = tf.placeholder(tf.string, [None])

	# trainSeqLength = [gConfig.maxLength for i in range(gConfig.trainBatchSize)]
	# testSeqLength = [gConfig.maxLength for i in range(gConfig.testBatchSize)]
	# evalSeqLength = [gConfig.maxLength for i in range(gConfig.evalBatchSize)]<commit_msg>Ready for test again<commit_after>from model import CRNN, CtcCriterion
from dataset import DatasetLmdb, SynthLmdb
import os
import tensorflow as tf
import numpy as np
import signal
import utility
import sys
import time

class Conf:
	def __init__(self):
		self.nClasses = 36
		self.trainBatchSize = 64
		self.evalBatchSize = 200
		self.testBatchSize = 10
		self.maxIteration = 2000000
		self.displayInterval = 100
		self.evalInterval = 1000
		self.testInterval = 2000
		self.saveInterval = 50000
		self.modelDir = os.path.abspath(os.path.join('..', 'model', 'ckpt'))
		self.dataSet = os.path.join('..', 'data', 'Synth')
		self.auxDataSet = os.path.join('..', 'data', 'aux_Synth')
		# self.dataSet = os.path.join('..', 'data', 'IIIT5K')
		self.maxLength = 24


if __name__ == '__main__':
	gConfig = Conf()
	sess = tf.InteractiveSession()

	ckpt = utility.checkPointLoader(gConfig.modelDir)
	imgs = tf.placeholder(tf.float32, [None, 32, 100])
	labels = tf.sparse_placeholder(tf.int32)
	batches = tf.placeholder(tf.int32, [None])
	isTraining = tf.placeholder(tf.bool)
	keepProb = tf.placeholder(tf.float32)
	pred_labels = tf.placeholder(tf.string, [None])
	true_labels = tf.placeholder(tf.string, [None])

	# trainSeqLength = [gConfig.maxLength for i in range(gConfig.trainBatchSize)]
	# testSeqLength = [gConfig.maxLength for i in range(gConfig.testBatchSize)]
	# evalSeqLength = [gConfig.maxLength for i in range(gConfig.evalBatchSize)]
{'fraction_non_alphanumeric': 0.13151451556488283, 'fraction_numerical': 0.03287862889122071, 'mean_word_length': 4.72, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5852212', 'n_tokens_mistral': 1116, 'n_tokens_neox': 997, 'n_words': 232}