While op forward for sentimental analysis (#6140)
* Add DataFeeder A v2 API like data feeder for book demos. We can feed data directly from reader. * Fix CI * Add an unittest for while/rnn op forward * Add unittest for raw while op backward * Fix CIrelease/0.11.0
Yu Yang
8 years ago
committed by
GitHub
parent
1fe05c458f
commit
d5e3279451
@ -0,0 +1,87 @@
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import paddle.v2.fluid as fluid
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import paddle.v2 as paddle
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import unittest
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import numpy
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class TestDynRNN(unittest.TestCase):
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def setUp(self):
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self.word_dict = paddle.dataset.imdb.word_dict()
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self.BATCH_SIZE = 100
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self.train_data = paddle.batch(
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paddle.dataset.imdb.train(self.word_dict),
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batch_size=self.BATCH_SIZE)
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def test_plain_while_op(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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sentence = fluid.layers.data(
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name='word', shape=[1], dtype='int64', lod_level=1)
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sent_emb = fluid.layers.embedding(
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input=sentence, size=[len(self.word_dict), 32], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='float32')
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rank_table = fluid.layers.lod_rank_table(x=sent_emb)
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sent_emb_array = fluid.layers.lod_tensor_to_array(
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x=sent_emb, table=rank_table)
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seq_len = fluid.layers.max_sequence_len(rank_table=rank_table)
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i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
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i.stop_gradient = False
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boot_mem = fluid.layers.fill_constant_batch_size_like(
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input=fluid.layers.array_read(
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array=sent_emb_array, i=i),
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value=0,
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shape=[-1, 100],
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dtype='float32')
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boot_mem.stop_gradient = False
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mem_array = fluid.layers.array_write(x=boot_mem, i=i)
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cond = fluid.layers.less_than(x=i, y=seq_len)
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cond.stop_gradient = False
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while_op = fluid.layers.While(cond=cond)
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out = fluid.layers.create_array(dtype='float32')
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with while_op.block():
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mem = fluid.layers.array_read(array=mem_array, i=i)
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ipt = fluid.layers.array_read(array=sent_emb_array, i=i)
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mem = fluid.layers.shrink_memory(x=mem, i=i, table=rank_table)
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hidden = fluid.layers.fc(input=[mem, ipt], size=100, act='tanh')
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fluid.layers.array_write(x=hidden, i=i, array=out)
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fluid.layers.increment(x=i, in_place=True)
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fluid.layers.array_write(x=hidden, i=i, array=mem_array)
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fluid.layers.less_than(x=i, y=seq_len, cond=cond)
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all_timesteps = fluid.layers.array_to_lod_tensor(
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x=out, table=rank_table)
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last = fluid.layers.sequence_pool(
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input=all_timesteps, pool_type='last')
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logits = fluid.layers.fc(input=last, size=1, act=None)
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loss = fluid.layers.sigmoid_cross_entropy_with_logits(
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x=logits, label=label)
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loss = fluid.layers.mean(x=loss)
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sgd = fluid.optimizer.SGD(1e-4)
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sgd.minimize(loss=loss)
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cpu = fluid.CPUPlace()
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exe = fluid.Executor(cpu)
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exe.run(startup_program)
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feeder = fluid.DataFeeder(feed_list=[sentence, label], place=cpu)
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data = next(self.train_data())
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val = exe.run(main_program, feed=feeder.feed(data),
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fetch_list=[loss])[0]
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self.assertEqual((1, ), val.shape)
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print(val)
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self.assertFalse(numpy.isnan(val))
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if __name__ == '__main__':
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unittest.main()
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