Luo Tao
9 years ago
commit
f4729a241f
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import paddle.v2 as paddle
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import paddle.v2.framework.layers as layers
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import paddle.v2.framework.core as core
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import paddle.v2.framework.optimizer as optimizer
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from paddle.v2.framework.framework import g_main_program, g_startup_program
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from paddle.v2.framework.executor import Executor
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import numpy as np
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def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
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data = layers.data(
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name="words",
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shape=[seq_len * batch_size, 1],
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append_batch_size=False,
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data_type="int64")
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label = layers.data(
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name="label",
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shape=[batch_size, 1],
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append_batch_size=False,
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data_type="int64")
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emb = layers.embedding(input=data, size=[dict_dim, emb_dim])
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emb = layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
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emb = layers.transpose(x=emb, axis=[1, 0, 2])
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c_pre_init = layers.fill_constant(
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dtype=emb.data_type, shape=[batch_size, emb_dim], value=0.0)
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layer_1_out = layers.lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
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layer_1_out = layers.transpose(x=layer_1_out, axis=[1, 0, 2])
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prediction = layers.fc(input=layer_1_out, size=class_dim, act="softmax")
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cost = layers.cross_entropy(input=prediction, label=label)
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avg_cost = layers.mean(x=cost)
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adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
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opts = adam_optimizer.minimize(avg_cost)
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acc = layers.accuracy(input=prediction, label=label)
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return avg_cost, acc
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def to_lodtensor(data, place):
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seq_lens = [len(seq) for seq in data]
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cur_len = 0
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lod = [cur_len]
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for l in seq_lens:
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cur_len += l
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lod.append(cur_len)
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flattened_data = np.concatenate(data, axis=0).astype("int64")
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flattened_data = flattened_data.reshape([len(flattened_data), 1])
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res = core.LoDTensor()
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res.set(flattened_data, place)
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res.set_lod([lod])
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return res
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def chop_data(data, chop_len=80, batch_len=50):
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data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
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return data[:batch_len]
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def prepare_feed_data(data, place):
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tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
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label = np.array(map(lambda x: x[1], data)).astype("int64")
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label = label.reshape([50, 1])
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tensor_label = core.LoDTensor()
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tensor_label.set(label, place)
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return tensor_words, tensor_label
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def main():
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word_dict = paddle.dataset.imdb.word_dict()
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cost, acc = lstm_net(dict_dim=len(word_dict), class_dim=2)
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batch_size = 100
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train_data = paddle.batch(
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paddle.reader.buffered(
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paddle.dataset.imdb.train(word_dict), size=batch_size * 10),
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batch_size=batch_size)
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data = chop_data(next(train_data()))
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place = core.CPUPlace()
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tensor_words, tensor_label = prepare_feed_data(data, place)
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exe = Executor(place)
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exe.run(g_startup_program)
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while True:
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outs = exe.run(g_main_program,
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feed={"words": tensor_words,
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"label": tensor_label},
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fetch_list=[cost, acc])
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cost_val = np.array(outs[0])
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acc_val = np.array(outs[1])
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print("cost=" + str(cost_val) + " acc=" + str(acc_val))
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if acc_val > 0.9:
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break
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if __name__ == '__main__':
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main()
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