我想根据索引在张量中指定值。
例如， 根据{{3}}的池化值和相应的索引输出，我想将这些池化值重新放回到带索引的原始拆分Tensor中。
我发现tf.nn.max_pool_with_argmax的输出索引是平坦的。
一个问题：如何将它们解开回Tensorflow中的坐标？
另一个问题：如果给出索引，如何将池化张量的每个值分配给Tensorflow中原始解拼张张的位置？
非常感谢。
我尝试制作代码来实现这一点，但我可以使用numpy。我不知道如何在tf.nn.max_pool_with_argmax之后获得平坦的索引并将其分配到Tensorflow中的解开张量。
ksize = 3
stride = 1
input_image = tf.placeholder(tf.float32, name='input_image')
#conv1
kernel = tf.Variable(tf.truncated_normal([ksize, ksize, 3, 16],stddev=0.1),
name='kernel')
conv = tf.nn.conv2d(input_image, kernel, [1,stride,stride,1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape = [16]), name = 'biases')
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name='conv1')
#pool1
pool1, pool1_indices = tf.nn.max_pool_with_argmax(conv1, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME', name='pool1')
#upsample by assigning the values of pool1 to the position in unpooling Tensor according to pool1_indices
indices = pool1_indices
unravel_pool1_indices = np.unravel_index(indices,[4,32,32,16])
unravel_pool1_coordinates = np.array(unravel_pool1_indices)
coor_shape = np.shape(unravel_pool1_coordinates)
unravel_pool1_coordinates = np.reshape(unravel_pool1_coordinates,(coor_shape[0],coor_shape[1]*coor_shape[2]*coor_shape[3]*coor_shape[4]))
unravel_pool1_coordinates = unravel_pool1_coordinates.T
values = pool1
values = np.reshape(values,(np.size(values)))
up1 = tf.constant(0.0, shape = [4,32,32,16])
delta = tf.SparseTensor(unravel_pool1_coordinates, values, shape = [4,32,32,16])
result = up1 + tf.sparse_tensor_to_dense(delta)
with tf.Session() as session:
session.run(tf.initialize_all_variables())
test_image = np.random.rand(4,32,32,3)
sess_outputs = session.run([pool1, pool1_indices],
{input_image.name: test_image})
答案 0 :(得分：0)
有一个待解决的PR应该解决这个问题：
答案 1 :(得分：0)