import paddle.fluid as fluid import math def skip_gram_word2vec(dict_size, embedding_size): input_word = fluid.layers.data(name="input_word", shape=[1], dtype='int64') emb = fluid.layers.embedding( input=input_word, size=[dict_size, embedding_size], param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(dict_size)))) predict_word = fluid.layers.data( name='predict_word', shape=[1], dtype='int64') data_list = [input_word, predict_word] w_param_name = "nce_w" fluid.default_main_program().global_block().create_parameter( shape=[dict_size, embedding_size], dtype='float32', name=w_param_name) b_param_name = "nce_b" fluid.default_main_program().global_block().create_parameter( shape=[dict_size, 1], dtype='float32', name=b_param_name) cost = fluid.layers.nce(input=emb, label=predict_word, num_total_classes=dict_size, param_attr=fluid.ParamAttr(name=w_param_name), bias_attr=fluid.ParamAttr(name=b_param_name), num_neg_samples=5) avg_cost = fluid.layers.reduce_mean(cost) return avg_cost, data_list
