In Tensorflow, how to assign values in Tensor according to the indices?

Question

I want to assign values in a tensor according to the indices.

For example, According to the pooling values and the corresponding indices output of tf.nn.max_pool_with_argmax, I want to put these pooling values back into the original unpooling Tensor with the indices.

I find the output indices of tf.nn.max_pool_with_argmax is flattened. One question: How to unravel them back into the coordinates in Tensorflow?

Another question: How to assign each value of the pooling tensor to the position of the original unpooling tensor in Tensorflow, given the indices?

Thank you very much.

I tried to make the codes to achieve that, but I can just use numpy. I do not how to obtain the flattened indices after the tf.nn.max_pool_with_argmax and assigning into the unpooling tensor in Tensorflow.

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}) 

Answer 1

There's a pending PR that should fix this:

https://github.com/tensorflow/tensorflow/issues/1793