def evaluate():
# Load model
weight_path = 'model/09031344_epoch_4_train_loss_3.7933.h5'
# Load data
X, Sources, Targets = load_test_data()
de2idx, idx2de = load_de_vocab()
en2idx, idx2en = load_en_vocab()
model = TransformerModel(in_vocab_len=len(idx2de),
out_vocab_len=len(idx2en),
max_len=hp.maxlen)
model.load_model(weight_path)
for i in range(len(X) // hp.batch_size):
x = X[i * hp.batch_size:(i + 1) * hp.batch_size]
sources = Sources[i * hp.batch_size:(i + 1) * hp.batch_size]
targets = Targets[i * hp.batch_size:(i + 1) * hp.batch_size]
preds = model.translate(x, idx2en)
for source, target, pred in zip(sources, targets, preds):
print('source:', source)
print('expected:', target)
print('pred:', pred)
print()
def evaluate_train():
# Load model
weight_path = 'model/09031925_epoch_0_train_loss_5.9855.h5'
# Load data
Sources, Targets = load_train_data()
de2idx, idx2de = load_de_vocab()
en2idx, idx2en = load_en_vocab()
batch_size = 5
model = TransformerModel(in_vocab_len=len(idx2de),
out_vocab_len=len(idx2en),
max_len=hp.maxlen)
model.load_model(weight_path)
for i in range(5 // batch_size):
x = Sources[i * batch_size:(i + 1) * batch_size]
sources = Sources[i * batch_size:(i + 1) * batch_size]
targets = Targets[i * batch_size:(i + 1) * batch_size]
preds = model.translate_with_ans(sources, targets, idx2en)
# preds = model.translate(x, idx2en)
for source, target, pred in zip(sources, targets, preds):
print('source:', ' '.join(idx2de[idx] for idx in source))
print('expected:', ' '.join(idx2en[idx] for idx in target))
print('pred:', pred)
print()