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Stack LSTM Net for Paddle Book6 (#5503)
* add lstm layer * set hidden shape * rename input parameter * add dynamic lstm * refine dynamic lstm layer * change parameter using XavierInitializer by default * refine dynamic lstm layer
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python/paddle/v2/framework/tests/test_understand_sentiment_dynamic_lstm.py
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| import paddle.v2 as paddle | ||
| import paddle.v2.framework.layers as layers | ||
| import paddle.v2.framework.nets as nets | ||
| import paddle.v2.framework.core as core | ||
| import paddle.v2.framework.optimizer as optimizer | ||
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| from paddle.v2.framework.framework import Program, g_main_program, g_startup_program | ||
| from paddle.v2.framework.executor import Executor | ||
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| import numpy as np | ||
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| def stacked_lstm_net(input_dim, | ||
| class_dim=2, | ||
| emb_dim=128, | ||
| hid_dim=512, | ||
| stacked_num=3): | ||
| assert stacked_num % 2 == 1 | ||
| data = layers.data(name="words", shape=[1], data_type="int64") | ||
| label = layers.data(name="label", shape=[1], data_type="int64") | ||
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| emb = layers.embedding(input=data, size=[input_dim, emb_dim]) | ||
| # add bias attr | ||
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| # TODO(qijun) linear act | ||
| fc1 = layers.fc(input=emb, size=hid_dim) | ||
| lstm1, cell1 = layers.dynamic_lstm(input=fc1, size=hid_dim) | ||
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| inputs = [fc1, lstm1] | ||
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| for i in range(2, stacked_num + 1): | ||
| fc = layers.fc(input=inputs, size=hid_dim) | ||
| lstm, cell = layers.dynamic_lstm( | ||
| input=fc, size=hid_dim, is_reverse=(i % 2) == 0) | ||
| inputs = [fc, lstm] | ||
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| fc_last = layers.sequence_pool(input=inputs[0], pool_type='max') | ||
| lstm_last = layers.sequence_pool(input=inputs[1], pool_type='max') | ||
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| prediction = layers.fc(input=[fc_last, lstm_last], | ||
| size=class_dim, | ||
| act='softmax') | ||
| cost = layers.cross_entropy(input=prediction, label=label) | ||
| avg_cost = layers.mean(x=cost) | ||
| adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002) | ||
| opts = adam_optimizer.minimize(avg_cost) | ||
| acc = layers.accuracy(input=prediction, label=label) | ||
| return avg_cost, acc | ||
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| def to_lodtensor(data, place): | ||
| seq_lens = [len(seq) for seq in data] | ||
| cur_len = 0 | ||
| lod = [cur_len] | ||
| for l in seq_lens: | ||
| cur_len += l | ||
| lod.append(cur_len) | ||
| flattened_data = np.concatenate(data, axis=0).astype("int64") | ||
| flattened_data = flattened_data.reshape([len(flattened_data), 1]) | ||
| res = core.LoDTensor() | ||
| res.set(flattened_data, place) | ||
| res.set_lod([lod]) | ||
| return res | ||
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| def main(): | ||
| BATCH_SIZE = 100 | ||
| PASS_NUM = 5 | ||
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| word_dict = paddle.dataset.imdb.word_dict() | ||
| print "load word dict successfully" | ||
| dict_dim = len(word_dict) | ||
| class_dim = 2 | ||
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| cost, acc = stacked_lstm_net(input_dim=dict_dim, class_dim=class_dim) | ||
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| train_data = paddle.batch( | ||
| paddle.reader.shuffle( | ||
| paddle.dataset.imdb.train(word_dict), buf_size=1000), | ||
| batch_size=BATCH_SIZE) | ||
| place = core.CPUPlace() | ||
| exe = Executor(place) | ||
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| exe.run(g_startup_program) | ||
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| for pass_id in xrange(PASS_NUM): | ||
| for data in train_data(): | ||
| 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") | ||
| label = label.reshape([BATCH_SIZE, 1]) | ||
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| tensor_label = core.LoDTensor() | ||
| tensor_label.set(label, place) | ||
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| outs = exe.run(g_main_program, | ||
| feed={"words": tensor_words, | ||
| "label": tensor_label}, | ||
| fetch_list=[cost, acc]) | ||
| cost_val = np.array(outs[0]) | ||
| acc_val = np.array(outs[1]) | ||
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| print("cost=" + str(cost_val) + " acc=" + str(acc_val)) | ||
| if cost_val < 1.0 and acc_val > 0.7: | ||
| exit(0) | ||
| exit(1) | ||
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| if __name__ == '__main__': | ||
| main() |