|
|
|
|
@ -82,21 +82,21 @@ def train(use_cuda, train_program, params_dirname):
|
|
|
|
|
avg_cost, acc = trainer.test(
|
|
|
|
|
reader=test_reader, feed_order=['words', 'label'])
|
|
|
|
|
|
|
|
|
|
print(("avg_cost: %s" % avg_cost))
|
|
|
|
|
print(("acc : %s" % acc))
|
|
|
|
|
print("avg_cost: %s" % avg_cost)
|
|
|
|
|
print("acc : %s" % acc)
|
|
|
|
|
|
|
|
|
|
if acc > 0.2: # Smaller value to increase CI speed
|
|
|
|
|
trainer.save_params(params_dirname)
|
|
|
|
|
trainer.stop()
|
|
|
|
|
|
|
|
|
|
else:
|
|
|
|
|
print(('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
|
|
|
|
|
event.epoch + 1, avg_cost, acc)))
|
|
|
|
|
print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
|
|
|
|
|
event.epoch + 1, avg_cost, acc))
|
|
|
|
|
if math.isnan(avg_cost):
|
|
|
|
|
sys.exit("got NaN loss, training failed.")
|
|
|
|
|
elif isinstance(event, fluid.EndStepEvent):
|
|
|
|
|
print(("Step {0}, Epoch {1} Metrics {2}".format(
|
|
|
|
|
event.step, event.epoch, list(map(np.array, event.metrics)))))
|
|
|
|
|
print("Step {0}, Epoch {1} Metrics {2}".format(
|
|
|
|
|
event.step, event.epoch, list(map(np.array, event.metrics))))
|
|
|
|
|
if event.step == 1: # Run 2 iterations to speed CI
|
|
|
|
|
trainer.save_params(params_dirname)
|
|
|
|
|
trainer.stop()
|
|
|
|
|
@ -123,14 +123,14 @@ def infer(use_cuda, inference_program, params_dirname=None):
|
|
|
|
|
place=place)
|
|
|
|
|
|
|
|
|
|
# Setup input by creating LoDTensor to represent sequence of words.
|
|
|
|
|
# Here each word is the basic element of the LoDTensor and the shape of
|
|
|
|
|
# each word (base_shape) should be [1] since it is simply an index to
|
|
|
|
|
# Here each word is the basic element of the LoDTensor and the shape of
|
|
|
|
|
# each word (base_shape) should be [1] since it is simply an index to
|
|
|
|
|
# look up for the corresponding word vector.
|
|
|
|
|
# Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
|
|
|
|
|
# which has only one level of detail. Then the created LoDTensor will have only
|
|
|
|
|
# one higher level structure (sequence of words, or sentence) than the basic
|
|
|
|
|
# element (word). Hence the LoDTensor will hold data for three sentences of
|
|
|
|
|
# length 3, 4 and 2, respectively.
|
|
|
|
|
# which has only one level of detail. Then the created LoDTensor will have only
|
|
|
|
|
# one higher level structure (sequence of words, or sentence) than the basic
|
|
|
|
|
# element (word). Hence the LoDTensor will hold data for three sentences of
|
|
|
|
|
# length 3, 4 and 2, respectively.
|
|
|
|
|
# Note that recursive_sequence_lengths should be a list of lists.
|
|
|
|
|
recursive_seq_lens = [[3, 4, 2]]
|
|
|
|
|
base_shape = [1]
|
|
|
|
|
@ -138,7 +138,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
|
|
|
|
|
tensor_words = fluid.create_random_int_lodtensor(
|
|
|
|
|
recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
|
|
|
|
|
results = inferencer.infer({'words': tensor_words})
|
|
|
|
|
print(("infer results: ", results))
|
|
|
|
|
print("infer results: ", results)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def main(use_cuda):
|
|
|
|
|
|
minqiyang
7 years ago