Is their a way using tf functions to multiply certain columns of a 2D tensor by a scaler?
e.g. multiply the second and third column of a matrix by 2:
[[2,3,4,5],[4,3,4,3]] -> [[2,6,8,5],[4,6,8,3]]
Thanks for any help.
EDIT:
Thank you Psidom for the reply. Unfortunately I am not using a tf.Variable, so it seems I have to use tf.slice.
What I am trying to do is to multiply all components by 2 of a single-sided PSD, except for the DC component and the Nyquist frequency component, to conserve the total power when going from a double-sided spectrum to a single-sided spectrum.
This would correspond to: 2*PSD[:,1:-1] if it was a numpy array.
Here is my attempt with tf.assign and tf.slice:
x['PSD'] = tf.assign(tf.slice(x['PSD'], [0, 1], [tf.shape(x['PSD'])[0], tf.shape(x['PSD'])[1] - 2]),
tf.scalar_mul(2, tf.slice(x['PSD'], [0, 1], [tf.shape(x['PSD'])[0], tf.shape(x['PSD'])[1] - 2]))) # single-sided power spectral density.
However:
AttributeError: 'Tensor' object has no attribute 'assign'
If the tensor is a variable, you can do this by slicing the columns you want to update and then use tf.assign:
x = tf.Variable([[2,3,4,5],[4,3,4,3]])
x = tf.assign(x[:,1:3], x[:,1:3]*2) # update the second and third columns and assign
# the new tensor to x
with tf.Session() as sess:
tf.global_variables_initializer().run()
print(sess.run(x))
#[[2 6 8 5]
# [4 6 8 3]]
Ended up taking 3 different slices and concatenating them together, with the middle slice multiplied by 2. Probably not the most efficient way, but it works:
x['PSD'] = tf.concat([tf.slice(x['PSD'], [0, 0], [tf.shape(x['PSD'])[0], 1]),
tf.scalar_mul(2, tf.slice(x['PSD'], [0, 1], [tf.shape(x['PSD'])[0], tf.shape(x['PSD'])[1] - 2])),
tf.slice(x['PSD'], [0, tf.shape(x['PSD'])[1] - 1], [tf.shape(x['PSD'])[0], 1])], 1) # single-sided power spectral density.
Related
Both tf.scatter_add and tf.scatter_nd allow indices to be a matrix. It is clear from the documentation of tf.scatter_nd that the last dimension of indices contains values that are used to index a tensor of shape shape. The other dimensions of indices define the number of elements/slices to be scattered. Suppose updates has a rank N. First k dimensions of indices (except the last dimension) should match with first k dimensions of updates. The last (N-k) dimensions of updates should match with the last (N-k) dimensions of shape.
This implies that tf.scatter_nd can be used to perform an N-dimensional scatter. However, tf.scatter_add also takes matrices as indices. But, its not clear which dimensions of indices correspond to the number of scatters to be performed and how do these dimensions align with updates. Can someone provide a clear explanation possibly with examples?
#shaunshd , I finally fully understand the 3 tensors relationship in tf.scatter_nd_*() arguments, especially when the indices have multi-demensions. e.g:
indices = tf.constant([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [3,3,2]], dtype=tf.int32)
Please don't expect tf.rank(indices)>2, tf.rank(indices)==2 is permanently true;
The following is my test codes to show more complex test case than the examples provided in tensroflow's official website:
def testScatterNDUpdate(self):
ref = tf.Variable(np.zeros(shape=[4, 4, 4], dtype=np.float32))
indices = tf.constant([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [3,3,2]], dtype=tf.int32)
updates = tf.constant([1,2,3,4,5], dtype=tf.float32)
#shape = (4,4,4)
print(tf.tensor_scatter_nd_update(ref, indices, updates))
print(ref.scatter_nd_update(indices, updates))
#print(updates.shape[-1]==shape[-1], updates.shape[0]<=shape[0])
#conditions are:
# updates.shape[0]==indices[0]
# indices[1]<=len(shape)
# tf.rank(indices)==2
You also could understand the indices with the following psudo codes:
def scatter_nd_update(ref, indices, updates):
for i in range(tf.shape(indices)[0]):
ref[indices[i]]=updates[i]
return ref
Comapring with numpy's fancy indexing feature, tensorflow's indexing features are still very difficult to use and have different using style, not unified as same as numpy yet. Hope the situation could be better in tf3.x
tensorflow.reduce_sum(..) computes the sum of elements across dimensions of a tensor. it is Ok.
But one thing is not clear to me , what is the purpose of saying reduce in the function name ?
Is it related to map_reduce of parallel computation?
Let's say, it distributes the required computation to
different cores , and collect the result from the cores , finally delivers the sum of the collected results ?
Because you can compute the sum along a given dimension (and therefore reduce it). And no it has nothing to do with map-reduce.
Quoting the documentation string of the method:
Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.
Example from the API:
x = tf.constant([[1, 1, 1], [1, 1, 1]])
tf.reduce_sum(x) # 6
tf.reduce_sum(x, 0) # [2, 2, 2]
tf.reduce_sum(x, 1) # [3, 3]
tf.reduce_sum(x, 1, keepdims=True) # [[3], [3]]
tf.reduce_sum(x, [0, 1]) # 6
Suppose I have a 2D tensor with shape (size, size), and I want to get 2 new tensors that containing the original tensors row index and column index.
So if size is 2, I want to get
[[0, 0], [1, 1]] and [[0, 1], [0, 1]]
What's tricky is that size is another tensor whose value can only be known when running the graph in a tensorflow Session.
How can I do this in tensorflow?
Seems like you are looking for tf.meshgrid.
Here's an example:
shape = tf.shape(matrix)
R, C = tf.meshgrid(tf.range(shape[0]), tf.range(shape[1]), indexing='ij')
matrix is your 2D tensor, R and C contain your row and column indices, respectively. Note that this can be slightly simplified if your matrix is square (only one tf.range).
I have two following tensors (note that they are both Tensorflow tensors which means they are still virtually symbolic at the time I construct the following slicing op before I launch a tf.Session()):
params: has shape (64,784, 256)
indices: has shape (64, 784)
and I want to construct an op that returns the following tensor:
output: has shape (64,784) where
output[i,j] = params_tensor[i,j, indices[i,j] ]
What is the most efficient way in Tensorflow to do so?
ps: I tried with tf.gather but couldn't make use of it to perform the operation I described above.
Many thanks.
-Bests
You can get exactly what you want using tf.gather_nd. The final expression is:
tf.gather_nd(params, tf.stack([tf.tile(tf.expand_dims(tf.range(tf.shape(indices)[0]), 1), [1, tf.shape(indices)[1]]), tf.transpose(tf.tile(tf.expand_dims(tf.range(tf.shape(indices)[1]), 1), [1, tf.shape(indices)[0]])), indices], 2))
This expression has the following explanation:
tf.gather_nd does what you expected and uses the indices to gather the output from the params
tf.stack combines three separate tensors, the last of which is the indices. The first two tensors specify the ordering of the first two dimensions (axis 0 and axis 1 of params/indices)
For the example provided, this ordering is simply 0, 1, 2, ..., 63 for axis 0, and 0, 1, 2, ... 783 for axis 1. These sequences are obtained with tf.range(tf.shape(indices)[0]) and tf.range(tf.shape(indices)[1]), respectively.
For the example provided, indices has shape (64, 784). The other two tensors from the last point above need to have this same shape in order to be combined with tf.stack
First, an additional dimension/axis is added to each of the two sequences using tf.expand_dims.
The use of tf.tile and tf.transpose can be shown by example: Assume the first two axes of params and index have shape (5,3). We want the first tensor to be:
[[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]]
We want the second tensor to be:
[[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]]
These two tensors almost function like specifying the coordinates in a grid for the associated indices.
The final part of tf.stack combines the three tensors on a new third axis, so that the result has the same 3 axes as params.
Keep in mind if you have more or less axes than in the question, you need to modify the number of coordinate-specifying tensors in tf.stack accordingly.
What you want is like a custom reduction function. If you are keeping something like index of maximum value at indices then I would suggest using tf.reduce_max:
max_params = tf.reduce_max(params_tensor, reduction_indices=[2])
Otherwise, here is one way to get what you want (Tensor objects are not assignable so we create a 2d list of tensors and pack it using tf.pack):
import tensorflow as tf
import numpy as np
with tf.Graph().as_default():
params_tensor = tf.pack(np.random.randint(1,256, [5,5,10]).astype(np.int32))
indices = tf.pack(np.random.randint(1,10,[5,5]).astype(np.int32))
output = [ [None for j in range(params_tensor.get_shape()[1])] for i in range(params_tensor.get_shape()[0])]
for i in range(params_tensor.get_shape()[0]):
for j in range(params_tensor.get_shape()[1]):
output[i][j] = params_tensor[i,j,indices[i,j]]
output = tf.pack(output)
with tf.Session() as sess:
params_tensor,indices,output = sess.run([params_tensor,indices,output])
print params_tensor
print indices
print output
I know I'm late, but I recently had to do something similar, and was able to to do it using Ragged Tensors:
output = tf.gather(params, tf.RaggedTensor.from_tensor(indices), batch_dims=-1, axis=-1)
Hope it helps
Suppose a Tensor containing :
[[0 0 1]
[0 1 0]
[1 0 0]]
How to get the dense representation in a native way (without using numpy or iterations) ?
[2,1,0]
There is tf.one_hot() to do the inverse, there is also tf.sparse_to_dense() that seems to do it but I was not able to figure out how to use it.
tf.argmax(x, axis=1) should do the job.
vec = tf.constant([[0, 0, 1], [0, 1, 0], [1, 0, 0]])
locations = tf.where(tf.equal(vec, 1))
# This gives array of locations of "1" indices below
# => [[0, 2], [1, 1], [2, 0]])
# strip first column
indices = locations[:,1]
sess = tf.Session()
print(sess.run(indices))
# => [2 1 0]
TensorFlow does not have a native dense to sparse conversion function/helper. Given that the input array is a dense tensor, such as the one you provided, you can define a function to convert a dense tensor to a sparse tensor.
def dense_to_sparse(dense_tensor):
where_dense_non_zero = tf.where(tf.not_equal(dense_tensor, 0))
indices = where_dense_non_zero
values = tf.gather_nd(dense_tensor, where_dense_non_zero)
shape = dense_tensor.get_shape()
return tf.SparseTensor(
indices=indices,
values=values,
shape=shape
)
This helper function finds the indices and values where the Tensor is non-zero and outputs a Sparse tensor with those indices and values. Additionally, the shape is effectively copied over.
You do not want to use tf.sparse_to_dense as that gives you the opposite representation. If you want your output to be [2, 1, 0] instead, you'll need to index the indices. First, you'll need the indices where the array isn't 0:
indices = tf.where(tf.not_equal(dense_tensor, 0))
Then, you'll need to access the tensor using slicing/indicing:
output = indices[:, 1]
You might notice that 1 in the slice above is equivalent to the dimension of the tensor - 1. Therefore, to make these value generic, you could do something like:
output = indices[:, len(dense_tensor.get_shape()) - 1]
Although I'm not exactly sure what you'd do with these values (the value of the column where the value is). Hope this helped!
EDIT: Yaroslav's answer is better if you're looking for the indices/locations of where the input tensor if 1; it won't be extensible for tensors with non-1/0 values if that is required.