def modelTrainAndSave(path):
if config.GPU == True:
ptrNet = PointerNetwork(config.HIDDEN_SIZE).cuda()
else:
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
optimizer = optim.Adam(ptrNet.parameters())
program_starts = time.time()
for epoch in range(EPOCHS):
train(ptrNet, optimizer, epoch + 1)
evaluateWordSort(ptrNet, epoch + 1)
torch.save(ptrNet.state_dict(), path)
now = time.time()
print("It has been {0} seconds since the loop started".format(
now - program_starts))
for i in range(out.size(0)):
print("=============================================")
print(
"yref",
y_val[i],
out[i],
y_val[i] - out[i],
)
xv = convertToWordSingle(x_val[i])
print("orig", xv)
v = out[i].numpy()
print("[", end="")
for index in v:
print(xv[index] + ", ", end="")
print("]")
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
optimizer = torch.optim.Adam(ptrNet.parameters())
program_starts = time.time()
for epoch in range(EPOCHS):
train(ptrNet, optimizer, epoch + 1)
evaluateWordSort(ptrNet, epoch + 1)
now = time.time()
print("It has been {0} seconds since the loop started".format(now -
program_starts))
print('Epoch [{}] -- Evaluate'.format(epoch))
x_val, y_val = batch(4)
out, _ = model(x_val, y_val, teacher_force_ratio=0.)
out = out.permute(1, 0)
sumVal = x_val.sum(dim=2)
for i in range(out.size(0)):
print('{} --> {} --> {} --> {}'.format(
sumVal[i],
sumVal[i].gather(0, out[i]),
sumVal[i].gather(0, y_val[i]),
sumVal[i].gather(0, y_val[i]) - sumVal[i].gather(0, out[i])
))
ptrNet = PointerNetwork(config.HIDDEN_SIZE)
optimizer = optim.Adam(ptrNet.parameters())
program_starts = time.time()
for epoch in range(EPOCHS):
train(ptrNet, optimizer, epoch + 1)
evaluate(ptrNet, epoch + 1)
now = time.time()
print("It has been {0} seconds since the loop started".format(now - program_starts))