在 Tensorflow 中，顶部 k 个池元素而不是仅最大元素的最佳方法是什么？

现在张量流中的最大池化函数是

tf.nn.max_pool(value, ksize, strides, padding, name=None)
Returns:
A Tensor with type tf.float32. The max pooled output tensor.
  

我想要 max_pool 的扩展版本，例如

tf.nn.top_k_pool(value, ksize, strides, padding, k=1, name=None)

Performs the top k pooling on the input.

Args:

value: A 4-D Tensor with shape [batch, height, width, channels] and type tf.float32.
ksize: A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
strides: A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
padding: A string, either 'VALID' or 'SAME'. The padding algorithm.
k: 0-D int32 Tensor. Number of top elements to look in each pool.
name: Optional name for the operation.
Returns:

A Tensor with type tf.float32. The max pooled output tensor. There will be an additional dimension saving the top k values.
  

我知道我可以花费以下张量流操作https://www.tensorflow.org/versions/r0.7/how_tos/adding_an_op/index.html https://www.tensorflow.org/versions/r0.7/how_tos/adding_an_op/index.html

我想知道是否有更简单的方法来实现这一目标。

这是一个使用的函数top_k获取通道的最大 k 个激活值。您可以修改它以满足您的目的：

def make_sparse_layer(inp_x,k, batch_size=None):

    in_shape = tf.shape(inp_x)
    d = inp_x.get_shape().as_list()[-1]
    matrix_in = tf.reshape(inp_x, [-1,d])
    values, indices = tf.nn.top_k(matrix_in, k=k, sorted=False)
    out = []
    vals = tf.unpack(values, axis=0, num=batch_size)
    inds = tf.unpack(indices, axis=0, num=batch_size)
    for i, idx in enumerate(inds):
        out.append(tf.sparse_tensor_to_dense(tf.SparseTensor(tf.reshape(tf.cast(idx,tf.int64),[-1,1]),vals[i], [d]), validate_indices=False ))
    shaped_out = tf.reshape(tf.pack(out), in_shape)
    return shaped_out