def run_epoch(self, sess, saver, train, dev):
prog = Progbar(target=int(len(train) / self.config.batch_size))
losses, grad_norms = [], []
for i, batch in enumerate(minibatches(train, self.config.batch_size)):
loss, grad_norm, summ = self.train_on_batch(sess, *batch)
losses.append(loss)
grad_norms.append(grad_norm)
prog.update(i + 1, [("train loss", loss)])
print("\nEvaluating on dev set...")
predictions = []
references = []
for batch in minibatches(dev, self.config.batch_size):
inputs_batch, targets_batch = batch
prediction = list(self.predict_on_batch(sess, inputs_batch))
predictions += prediction
references += list(targets_batch)
predictions = [
tokens_to_sentences(pred, self.config.idx2word)
for pred in predictions
]
references = [
tokens_to_sentences(ref, self.config.idx2word)
for ref in references
]
f1, _, _ = rouge_n(predictions, references)
print("- dev rouge f1: {}".format(f1))
return losses, grad_norms, summ, predictions, f1
def run_epoch(self, sess, saver, train, dev):
prog = Progbar(target=int(len(train) / self.config.batch_size))
train_preds, losses, accs, refs = [], [], [], []
for i, batch in enumerate(minibatches(train, self.config.batch_size)):
_, targets_batch = batch
train_pred, loss, acc, loss_summ, acc_summ = self.train_on_batch(
sess, *batch)
train_pred = list(train_pred)
losses.append(loss)
accs.append(acc)
train_preds += train_pred
refs += list(targets_batch)
prog.update(i + 1, [("train loss", loss), ("train acc", acc)])
train_preds = [
tokens_to_sentences(pred, self.config.idx2word)
for pred in train_preds
]
refs = [tokens_to_sentences(ref, self.config.idx2word) for ref in refs]
train_f1, _, _ = rouge_n(train_preds, refs)
print("- train rouge f1: {}".format(train_f1))
print("\nEvaluating on dev set...")
dev_preds, refs, dev_losses, dev_accs = [], [], [], []
prog_dev = Progbar(target=int(len(dev) / self.config.batch_size))
for i, batch in enumerate(minibatches(dev, self.config.batch_size)):
_, targets_batch = batch
dev_pred, dev_loss, dev_acc, dev_loss_summ, dev_acc_summ = self.predict_on_batch(
sess, *batch)
dev_pred = list(dev_pred)
dev_losses.append(dev_loss)
dev_accs.append(dev_acc)
dev_preds += dev_pred
refs += list(targets_batch)
prog_dev.update(i + 1, [("dev loss", dev_loss),
("dev_acc", dev_acc)])
dev_preds = [
tokens_to_sentences(pred, self.config.idx2word)
for pred in dev_preds
]
refs = [tokens_to_sentences(ref, self.config.idx2word) for ref in refs]
dev_f1, _, _ = rouge_n(dev_preds, refs)
print("- dev rouge f1: {}".format(dev_f1))
return losses, accs, dev_losses, dev_accs, loss_summ, acc_summ, dev_loss_summ, dev_acc_summ, dev_f1
refs = []
test_losses = []
test_accs = []
for batch in minibatches(test, model.config.batch_size):
inputs_batch, targets_batch = batch
pred, test_loss, test_acc, _, _ = model.predict_on_batch(
sess, *batch)
pred = list(pred)
preds += pred
refs += list(targets_batch)
test_losses.append(test_loss)
test_accs.append(test_acc)
mean_test_loss = np.mean(np.asarray(test_losses))
preds = [
tokens_to_sentences(pred, model.config.idx2word) for pred in preds
]
refs = [
tokens_to_sentences(ref, model.config.idx2word) for ref in refs
]
f1, _, _ = rouge_n(preds, refs)
print("- test ROUGE: {}".format(f1))
print("- test loss: {}".format(mean_test_loss))
print("Writing predictions")
fname = './data/predictions' + str(date.today()) + '.txt'
with open(fname, 'w') as f:
for pred, ref in zip(preds, refs):
f.write(pred + '\t' + ref)
f.write('\n')
print("Done!")
print(80 * "=")
print("TESTING")
print(80 * "=")
print("Restoring the best model weights found on the dev set")
saver.restore(sess, './data/weights/model.weights')
print("Final evaluation on test set")
predictions = []
references = []
for batch in minibatches(test, model.config.batch_size):
inputs_batch, targets_batch = batch
prediction = list(model.predict_on_batch(sess, inputs_batch))
predictions += prediction
references += list(targets_batch)
predictions = [
tokens_to_sentences(pred, model.config.idx2word)
for pred in predictions
]
references = [
tokens_to_sentences(ref, model.config.idx2word)
for ref in references
]
f1, _, _ = rouge_n(predictions, references)
print("- test ROUGE: {}".format(f1))
print("Writing predictions")
fname = 'predictions' + str(date.today()) + '.txt'
with open(fname, 'w') as f:
for pred, ref in zip(predictions, references):
f.write(pred + '\t' + ref)
f.write('\n')