我正在尝试在注意层上执行行方式和列方式最大池化,如下面的链接所述: http://www.dfki.de/~neumann/ML4QAseminar2016/presentations/Attentive-Pooling-Network.pdf(slide-15)
我正在使用文本数据集,其中一个句子被送到CNN。句子的每个单词都已嵌入。它的代码如下:
model.add(Embedding(MAX_NB_WORDS, emb_dim, weights=[embedding_matrix],input_length=MAX_SEQUENCE_LENGTH, trainable=False))
model.add(Conv1D(k, FILTER_LENGTH, border_mode = "valid", activation = "relu"))
CNN的输出形状(无,256)。这充当了关注层的输入。 任何人都可以建议如何在keras中使用tensorflow作为后端实现行方式或列方式最大池化吗?
答案 0 :(得分:1)
如果您的模型中的图像具有形状(batch, width, height, channels)
,则可以重新整形数据以隐藏其中一个空间维度并使用一维合并:
宽度:
model.add(Reshape((width, height*channels)))
model.add(MaxPooling1D())
model.add(Reshape((width/2, height, channels))) #if you had an odd number, add +1 or -1 (one of them will work)
高度:
#Here, the time distributed will consider that "width" is an extra time dimension,
#and will simply think of it as an extra "batch" dimension
model.add(TimeDistributed(MaxPooling1D()))
工作示例,具有两个分支的功能API模型,每个分支对应一个:
import numpy as np
from keras.layers import *
from keras.models import *
inp = Input((30,50,4))
out1 = Reshape((30,200))(inp)
out1 = MaxPooling1D()(out1)
out1 = Reshape((15,50,4))(out1)
out2 = TimeDistributed(MaxPooling1D())(inp)
model = Model(inp,[out1,out2])
model.summary()
除了Reshape
之外,如果您不想打扰这些数字:
#swap height and width
model.add(Permute((2,1,3)))
#apply the pooling to width
model.add(TimeDistributed(MaxPooling1D()))
#bring height and width to the correct order
model.add(Permute((2,1,3)))