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43 lines
1.2 KiB
43 lines
1.2 KiB
8 years ago
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#!/usr/bin/env python
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from paddle.trainer_config_helpers import *
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import imdb
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num_class = 2
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vocab_size = 30000
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fixedlen = 100
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batch_size = get_config_arg('batch_size', int, 128)
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lstm_num = get_config_arg('lstm_num', int, 1)
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hidden_size = get_config_arg('hidden_size', int, 128)
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# whether to pad sequence into fixed length
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pad_seq = get_config_arg('pad_seq', bool, True)
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imdb.create_data('imdb.pkl')
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args={'vocab_size':vocab_size, 'pad_seq':pad_seq, 'maxlen':fixedlen}
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define_py_data_sources2("train.list",
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None,
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module="provider",
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obj="process",
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args=args)
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settings(
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batch_size=batch_size,
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learning_rate=2e-3,
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learning_method=AdamOptimizer(),
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25
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)
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net = data_layer('data', size=vocab_size)
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net = embedding_layer(input=net, size=128)
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for i in xrange(lstm_num):
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net = simple_lstm(input=net, size=hidden_size)
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net = last_seq(input=net)
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net = fc_layer(input=net, size=2, act=SoftmaxActivation())
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lab = data_layer('label', num_class)
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loss = classification_cost(input=net, label=lab)
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outputs(loss)
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