currentStep = tf.train.global_step(sess, globalStep)
print(
"train: step: {}, loss: {}, acc: {}, recall: {}, precision: {}, f_beta: {}"
.format(currentStep, loss, acc, recall, prec, f_beta))
if currentStep % config.training.evaluateEvery_BiLSTM == 0:
print("\nEvaluation:")
losses = []
accs = []
f_betas = []
precs = []
recalls = []
for batchEval in nextBatch(evalContents, evalLabels,
config.batchSize):
loss, acc, precision, recall, f_beta = devStep(
batchEval[0], batchEval[1])
losses.append(loss)
accs.append(acc)
precs.append(precision)
recalls.append(recall)
f_betas.append(f_beta)
time_str = datetime.datetime.now().isoformat()
print(
"{}, step: {}, loss: {}, acc: {},precision: {}, recall: {}, f_beta: {}"
.format(time_str, currentStep, mean(losses),
mean(accs), mean(precs), mean(recalls),
mean(f_betas)))
saver.save(sess, savedModelPath)
losses=[]
accs=[]
f_betas=[]
precs=[]
recalls=[]
for batchEval in nextBatch(evalContents,evalLabels,config.batchSize):
loss,acc,precision,recall,f_beta=devStep(batchEval[0],batchEval[1])
losses.append(loss)
accs.append(acc)
precs.append(precision)
recalls.append(recall)
f_betas.append(f_beta)
time_str=datetime.datetime.now().isoformat()
print("{}, step: {}, loss: {}, acc: {},precision: {}, recall: {}, f_beta: {}".format(time_str,
currentStep,
mean(losses),
mean(accs),
mean(precs),
mean(recalls),
mean(f_betas)))
saver.save(sess, savedModelPath)