# print("Val Gened: " + str(time.time()-start_time))
# print("Validating: " + str(time.time()-start_time))
batch_error = dev_model(input_batch_x, input_batch_y, input_batch_mask)
# print("#%i cost: %f" % (index, batch_error))
batch_costs.append(batch_error)
# print("Validated: " + str(time.time()-start_time))
# print "Batch Costs: " + str(batch_costs)
val_acc = 1 - np.mean(batch_costs)
if val_acc > first:
print("!!!!!!!!!!FIRST!!!!!!!!!!")
third = second
second = first
first = val_acc
classifier.save_model(args.model_out)
elif val_acc > second:
print("!!!!!!!!!!SECOND!!!!!!!!!!")
third = second
second = val_acc
classifier.save_model(args.model_out + ".2")
elif val_acc > third:
print("!!!!!!!!!!THIRD!!!!!!!!!!")
third = val_acc
classifier.save_model(args.model_out + ".3")
dev_acc.append(val_acc)
now_time = time.time()
print("Dev accuracy: " + str(dev_acc[-1]))
print("Current time: " + str(now_time - start_time))
classifier.save_model("models/temp.mdl")
# print("#%i cost: %f" % (index, batch_error))
for s, e in reversed(pred_mask):
print "s = %i, e = %i" % (s, e)
batch_pred = np.delete(batch_pred, [i for i in range(s, e)])
batch_preds.extend(batch_pred.tolist())
print len(batch_preds)
# print("Validated: " + str(time.time()-start_time))
# print "Batch Costs: " + str(batch_costs)
val_acc = 100.0 - calculate_PER(batch_preds, val_y)
if val_acc > first:
print ("!!!!!!!!!!FIRST!!!!!!!!!!")
third = second
second = first
first = val_acc
classifier.save_model(args.model_out)
elif val_acc > second:
print ("!!!!!!!!!!SECOND!!!!!!!!!!")
third = second
second = val_acc
classifier.save_model(args.model_out + ".2")
elif val_acc > third:
print ("!!!!!!!!!!THIRD!!!!!!!!!!")
third = val_acc
classifier.save_model(args.model_out + ".3")
dev_acc.append(val_acc)
now_time = time.time()
print ("100 - Phone Error Rate: " + str(dev_acc[-1]))
print ("Current time: " + str(now_time - start_time))
classifier.save_model("models/temp.mdl")
