# CTC Beam Search Decoder to decode pred string from the prob map
decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits, SeqLens)
#Reading test data...
InputListTest, SeqLensTest, _ = ReadData(cfg.TEST_LOCATION, cfg.TEST_LIST,
cfg.TEST_NB, WND_HEIGHT, WND_WIDTH,
WND_SHIFT, VEC_PER_WND, '')
print('Initializing...')
session = tf.Session()
session.run(tf.global_variables_initializer())
LoadModel(session, cfg.SaveDir + '/')
try:
session.run(tf.assign(phase_train, False))
randIxs = range(0, len(InputListTest))
start, end = (0, cfg.BatchSize)
batch = 0
while end <= len(InputListTest):
batchInputs = []
batchSeqLengths = []
for batchI, origI in enumerate(randIxs[start:end]):
batchInputs.extend(InputListTest[origI])
batchSeqLengths.append(SeqLensTest[origI])
LogFile.flush()
session = tf.Session()
session.run(tf.global_variables_initializer())
LocalTrainSummary = tf.summary.merge([TrainLoss_s, TrainError_s])
OverallSummary = tf.summary.merge([
OverallTrainingLoss_s, OverallTrainingError_s, OverallValidationLoss_s,
OverallValidationError_s
])
SummaryWriter = tf.summary.FileWriter(cfg.LogDir, session.graph)
if cfg.StartingEpoch != 0: LoadModel(session, cfg.SaveDir + '/')
try:
for epoch in range(cfg.StartingEpoch, cfg.NEpochs):
LogFile.write(
"######################################################\n")
LogFile.write("Training Data\n")
LogFile.flush()
TrainingLoss = []
TrainingError = []
if cfg.RandomBatches == True:
randIxs = np.random.permutation(len(inputList))
else: