paddle lookalike 实现(paddle reshape)

需求：算 batch*1*32 与 batch*10*32 attention

    def local_attention_unit(self, target_user, user_seeds):
        user_target_reshape = fluid.layers.unsqueeze(target_user,axes=[1]) 
        user_seeds_reshape = fluid.layers.reshape(user_seeds, shape=[-1, 10, 32])
        out = fluid.layers.matmul(user_target_reshape, user_seeds_reshape, transpose_y=True)  # -1,1,10
        out = fluid.layers.softmax(out) #-1,1,10
        out = fluid.layers.matmul(out,user_seeds_reshape) #-1,1,32
        out = fluid.layers.reduce_sum(out, dim=1)  # batch_size * emb_size
        return out

self.lookalike_cluster = fluid.layers.data(name="lookalike_cluster", shape=[-1,320], dtype="float32", lod_level=0, append_batch_size=False)

 self.user_gcf_vec = fluid.layers.data(name="user_gcf_vec", shape=[-1,32], dtype="float32", lod_level=0, append_batch_size=False)

attention_unit_out = self.local_attention_unit( self.user_gcf_vec, self.lookalike_cluster)

reshape 具体逻辑：

import paddle
import paddle.fluid as fluid
import numpy as np
#2*6
data_x = np.array([[1.0, 1.0, 1.0,3.0, 3.0, 3.0],[1.0, 2.0, 1.0,4.0, 3.0, 5.0]])
print data_x
with fluid.dygraph.guard():
    x = fluid.dygraph.to_variable(data_x)
    out_z2 = fluid.layers.reshape(x, shape=[-1,2,3])
    print(out_z2.numpy())