import paddle.v2 as paddle import paddle.v2.framework.layers as layers import paddle.v2.framework.core as core import paddle.v2.framework.optimizer as optimizer from paddle.v2.framework.framework import Program from paddle.v2.framework.executor import Executor import numpy as np startup_program = Program() main_program = Program() embed_size = 32 hidden_size = 256 N = 5 batch_size = 32 is_sparse = True word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) first_word = layers.data( name='firstw', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) second_word = layers.data( name='secondw', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) third_word = layers.data( name='thirdw', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) forth_word = layers.data( name='forthw', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) next_word = layers.data( name='nextw', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) embed_first = layers.embedding( input=first_word, size=[dict_size, embed_size], data_type='float32', is_sparse=is_sparse, param_attr={'name': 'shared_w'}, main_program=main_program, startup_program=startup_program) embed_second = layers.embedding( input=second_word, size=[dict_size, embed_size], data_type='float32', is_sparse=is_sparse, param_attr={'name': 'shared_w'}, main_program=main_program, startup_program=startup_program) embed_third = layers.embedding( input=third_word, size=[dict_size, embed_size], data_type='float32', is_sparse=is_sparse, param_attr={'name': 'shared_w'}, main_program=main_program, startup_program=startup_program) embed_forth = layers.embedding( input=forth_word, size=[dict_size, embed_size], data_type='float32', is_sparse=is_sparse, param_attr={'name': 'shared_w'}, main_program=main_program, startup_program=startup_program) concat_embed = layers.concat( input=[embed_first, embed_second, embed_third, embed_forth], axis=1, main_program=main_program, startup_program=startup_program) hidden1 = layers.fc(input=concat_embed, size=hidden_size, act='sigmoid', main_program=main_program, startup_program=startup_program) predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax', main_program=main_program, startup_program=startup_program) cost = layers.cross_entropy( input=predict_word, label=next_word, main_program=main_program, startup_program=startup_program) avg_cost = layers.mean( x=cost, main_program=main_program, startup_program=startup_program) sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001) opts = sgd_optimizer.minimize(avg_cost, startup_program) train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), batch_size) place = core.CPUPlace() exe = Executor(place) # fix https://github.com/PaddlePaddle/Paddle/issues/5434 then remove # below exit line. exit(0) exe.run(startup_program, feed={}, fetch_list=[]) PASS_NUM = 100 for pass_id in range(PASS_NUM): for data in train_reader(): input_data = [[data_idx[idx] for data_idx in data] for idx in xrange(5)] input_data = map(lambda x: np.array(x).astype("int64"), input_data) input_data = map(lambda x: np.expand_dims(x, axis=1), input_data) first_data = input_data[0] first_tensor = core.LoDTensor() first_tensor.set(first_data, place) second_data = input_data[1] second_tensor = core.LoDTensor() second_tensor.set(second_data, place) third_data = input_data[2] third_tensor = core.LoDTensor() third_tensor.set(third_data, place) forth_data = input_data[3] forth_tensor = core.LoDTensor() forth_tensor.set(forth_data, place) next_data = input_data[4] next_tensor = core.LoDTensor() next_tensor.set(next_data, place) outs = exe.run(main_program, feed={ 'firstw': first_tensor, 'secondw': second_tensor, 'thirdw': third_tensor, 'forthw': forth_tensor, 'nextw': next_tensor }, fetch_list=[avg_cost]) out = np.array(outs[0]) if out[0] < 10.0: exit(0) # if avg cost less than 10.0, we think our code is good. exit(1)