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我曾希望实现keras中的PointNet(https://arxiv.org/pdf/1612.00593.pdf)的变体,但我无法重复上下文向量(g)的次数可变,所以我可以将它连接起来与前一层缺少上下文(前)的行。我尝试了Repeat()和keras.backend.Tile()。将张量与keras中的向量合并为一个向量
input = Input(shape=(None,3))
x = TimeDistributed(Dense(128, activation = 'relu'))(input)
pre = TimeDistributed(Dense(256, activation = 'relu'))(x)
g = GlobalMaxPooling1D()(pre)
x = Lambda(merge_on_single, output_shape=(None,512))([pre,g])
print(x.shape)
这是我想出的lambda定义。
def merge_on_single(v):
#v[0] is variable length tensor, v[1] is the single vector
return Concatenate()([K.repeat(v[1],K.get_variable_shape(v[0])),v[0]])
但出现以下错误:
类型错误:在列表张量传递给“包”作品的“价值”有类型[INT32,INT32]并不都匹配。
UPDATE:
所以我能得到的层不是做给错误如下:
input = Input(shape=(None,3))
num_point = K.placeholder(input.get_shape()[1].value, dtype=tf.int32)
#first global feature layer
x = TimeDistributed(Dense(512, activation = 'relu'))(input)
x = TimeDistributed(Dense(256, activation = 'relu'))(x)
g = GlobalMaxPooling1D()(x)
g = K.reshape(g,(-1,1,256))
g = K.tile(x, [1,num_point,1])
concat_feat = K.concatenate([x, g])
,但现在,我得到以下错误:
AttributeError: 'Tensor' object has no attribute '_keras_history'