total_steps, warmup_steps = calc_train_steps(
num_example=len(train_data),
batch_size=batch_size,
epochs=epochs,
warmup_proportion=0.1,
)
model, test_model = Graph(total_steps,
warmup_steps,
lr=learning_rate,
min_lr=min_learning_rate)
if is_test:
test_model.load_weights('output/subject_model.weights')
model.load_weights('output/subject_model.weights')
test(test_data, class2id, test_model)
# acc = dev(dev_data, class2id, test_model)
# print('acc: ', acc)
else:
# test_model.load_weights('output/subject_model.weights')
# model.load_weights('output/subject_model.weights')
evaluator = Evaluate(dev_data, model, test_model, class2id)
X = data_generator(train_data, batch_size)
steps = int((len(train_data) + batch_size - 1) / batch_size)
model.fit_generator(X,
steps_per_epoch=100,
epochs=epochs,
callbacks=[evaluator])
batch_size = 16
learning_rate = 1e-3
min_learning_rate = 1e-5
epochs = 100
is_test = False
train_data, val_data, test_data, id2class, class2id = read_data()
total_steps, warmup_steps = calc_train_steps(num_example=len(train_data),
batch_size=batch_size,
epochs=epochs,
warmup_proportion=0.1)
model, test_model = Graph(total_steps,
warmup_steps,
lr=learning_rate,
min_lr=min_learning_rate)
if is_test:
test_model.load_weights('models/subject_model.weights')
model.load_weights('models/subject_model.weights')
test(test_data, class2id, test_model)
else:
evaluator = Evaluate(val_data, model, test_model, class2id)
X = data_generator(train_data, batch_size)
steps = int((len(train_data) + batch_size - 1) / batch_size)
model.fit_generator(X,
steps_per_epoch=100,
epochs=epochs,
callbacks=[evaluator])