I have a rank 3 tensor and another empty tensor of same shape. I am trying to find the maximum values across 3rd(Z) axis for all X,Y locations and insert 1 into the corresponding location in the empty tensor. I have achieved this in numpy in the following way
a = np.random.rand(5,5,3)>=0.5
empty_tensor = np.zeros((5,5,3))
max_z_indices = a.argmax(axis=-1)
empty_tensor[np.arange(a.shape[0])[:,None],np.arange(a.shape[1]),max_z_indices] = 1
In tensorflow I have
a_tf = tf.Variable(a)
empty_tensor_tf = tf.Variable(np.zeros((5,5,3)))
max_z_indices = sess.run(tf.argmax(a_tf,axis=-1))
I know that I can explicitly write the X,Y,Z indices of max values along 3rd dimension of tensor a_tf and use tf.scatter_nd_update to update empty_tensor_tf but I was hoping to find a better way (broadcasting) as in the last line of numpy code.
You can use tf.reduce_max to get the max value for each z-index, then use tf.where to convert it to 1 or 0 according to the cond.
import tensorflow as tf
# tf_a is (5,5,3) tensor
max_val = tf.reduce_max(tf_a, axis=-1,keepdims=True)
cond = tf.equal(tf_a, max_val)
res = tf.where(cond, tf.ones_like(tf_a), tf.zeros_like(tf_a))
Related
I am trying to put two NumPy arrays into a matrix or horizontally stack them. Each array is 76 elements long, and I want the ending matrix to have 76 rows and 2 columns. I basically have a velocity/frequency model and want to have two columns with corresponding frequency/velocity values in each row.
Here is my code ('f' is frequency and 'v' the velocity values, previously already defined):
print(f.shape)
print(v.shape)
print(type(f))
print(type(v))
x = np.concatenate((f, v), axis = 1)
This returns
(76,)
(76,)
<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
And an error about the concatenate line that says:
AxisError: axis 1 is out of bounds for array of dimension 1
I've also tried hstack except for concatenate, as well as vstack and transposing .T, and have the same error. I've also tried using Pandas, but I need to use NumPy, because when I save it into a txt/dat file, Pandas gives me an extra column with numbering that I do not need to have.
Your problem is that your vectors are one-dimensional, like in this example:
f_1d = np.array([1,2,3,4])
print(f_1d.shape)
> (4,)
As you can see, only the first dimension is given. So instead you could create your vectors like this:
f = np.expand_dims(np.array([1,2,3,4]), axis=1)
v = np.expand_dims(np.array([5,6,7,8]), axis=1)
print(f.shape)
print(v.shape)
>(4,1)
>(4,1)
As you may notice, the second dimension is equal to one, but now your vector is represented in matrix form.
It is now possible to transpose the matrix-vectors:
f_t = f.T
v_t = v.T
print(f_t)
> (1,4)
Instead of using concatenate, you could use vstack or hstack to create cleaner code:
x = np.hstack((f,v))
x_t = np.vstack((f_t,v_t))
print(x.shape)
print(x_t.shape)
>(4,2)
>(2,4)
I am trying to update very specific indices of a multidimensional tensor in Pytorch, and I am not sure how to access the correct indices. I can do this in a very straightforward way in Numpy:
import numpy as np
#set up the array containing the data
data = 100*np.ones((10,10,2))
data[5:,:,:] = 0
#select the data points that I want to update
idxs = np.nonzero(data.sum(2))
#generate the updates that I am going to do
updates = np.random.randint(5,size=(idxs[0].shape[0],2))
#update the data
data[idxs[0],idxs[1],:] = updates
I need to implement this in Pytorch but I am not sure how to do this. It seems like I need the scatter function but that only works along a single dimension instead of the multiple dimensions that I need. How can I do this?
These operations work exactly the same in their PyTorch counterparts, except for torch.nonzero, which by default returns a tensor of size [z, n] (where z is the number of non-zero elements and n the number of dimensions) instead of a tuple of n tensors with size [z] (as NumPy does), but that behaviour can be changed by setting as_tuple=True.
Other than that you can directly translate it to PyTorch, but you need to make sure that the types match, because you cannot assign a tensor of type torch.long (default of torch.randint) to a tensor of type torch.float (default of torch.ones). In this case, data is probably meant to have type torch.long:
#set up the array containing the data
data = 100*torch.ones((10,10,2), dtype=torch.long)
data[5:,:,:] = 0
#select the data points that I want to update
idxs = torch.nonzero(data.sum(2), as_tuple=True)
#generate the updates that I am going to do
updates = torch.randint(5,size=(idxs[0].shape[0],2))
#update the data
data[idxs[0],idxs[1],:] = updates
I have the following TensorFlow tensors.
tensor1 = tf.constant(np.random.randint(0,255, (2,512,512,1)), dtype='int32') #All elements in range [0,255]
tensor2 = tf.constant(np.random.randint(0,255, (2,512,512,1)), dtype='int32') #All elements in range [0,255]
tensor3 = tf.keras.backend.flatten(tensor1)
tensor4 = tf.keras.backend.flatten(tensor2)
tensor5 = tf.constant(np.random.randint(0,255, (255,255)), dtype='int32') #All elements in range [0,255]
I wish to use the values stored in tensor 3 and tensor 4 to make a tuple and query the element at position given by the tuple in tensor 5. For example, let's say 0th element in tensor 3, that is tensor3[0]=5 and tensor4[0]=99.
So the tuple becomes (5,99). I wish to look up the value of element (5,99) in tensor 5. I wish to do it for all elements in Tensor3 and Tensor4 in a batch processing manner. That is I do not want to loop over all values in the range of (len(Tensor3)). I did the following to achieve this.
tensor6 = tensor5[tensor3[0],tensor4[0]]
But tensor6 has the shape (255,255) where as I was hoping to get a tensor of shape (len(tensor3),len(tensor3)). I wanted to evaluate tensor5 at all possible locations in len(tensor3). That is at (0,0),...(1000,1000),....(2000,2000),.... I am using TensorFlow version 1.12.0. How can I achieve this?
I have managed to get something working in Tensorflow v 1.12, but do let me know if it is the expected code:
import tensorflow as tf
print(tf.__version__)
import numpy as np
tensor1 = tf.constant(np.random.randint(0,255, (2,512,512,1)), dtype='int32') #All elements in range [0,255]
tensor2 = tf.constant(np.random.randint(0,255, (2,512,512,1)), dtype='int32') #All elements in range [0,255]
tensor3 = tf.keras.backend.flatten(tensor1)
tensor4 = tf.keras.backend.flatten(tensor2)
tensor5 = tf.constant(np.random.randint(0,255, (255,255)), dtype='int32') #All elements in range [0,255]
elems = (tensor3, tensor4)
a = tf.map_fn(lambda x: tensor5[x[0], x[1]], elems, dtype=tf.int32)
print(tf.Session().run(a))
Based on the comment below I'd like to add an explanation for the map_fn used in the code. Since for loops are not supported without eager_execution, map_fn is (sort of) equivalent to for loops.
A map_fn has the following parameters: operation_performed, input_arguments, optional_dtype. What happens under the hood is that a for loop is run along the length of the values in input_arguments (which must contain an iterable object) and then for each value obtained operation_performed is performed. For further clarification please refer docs.
The names given to the arguments of the function is my way of interpreting them, as I'd like understand it, and is not given in the official docs. :)
Good afternoon.
I continue to have issues with updating random elements in tensorflow by index.
I want to randomly choose indices (half of all, for instance), and then set to zero elements correspond to that indices.
Here's the problematic part:
with tf.variable_scope("foo", reuse=True):
temp_var = tf.get_variable("W")
size_2a = tf.get_variable("b")
s1 = tf.shape(temp_var).eval()[0]
s2 = tf.shape(size_2a).eval()[0]
row_indices = tf.random_uniform(dtype=tf.int32, minval=0, maxval = s1 - 1, shape=[s1]).eval()
col_indices = tf.random_uniform(dtype=tf.int32, minval=0, maxval = s2 - 1, shape=[s2]).eval()
ones_mask = tf.ones([s1,s2])
# turn 'ones_mask' into 1d variable since "scatter_update" supports linear indexing only
ones_flat = tf.Variable(tf.reshape(ones_mask, [-1]))
# no automatic promotion, so make updates float32 to match ones_mask
updates = tf.zeros(shape=(s1,), dtype=tf.float32)
# get linear indices
linear_indices = row_indices*s2 + tf.reshape(col_indices,s1*s2)
ones_flat = tf.scatter_update(ones_flat, linear_indices/2, updates)
#I want to set to zero only half of all elements,that's why linear_indices/2
# convert back into original shape
ones_mask = tf.reshape(ones_flat, ones_mask.get_shape())
It gives me ValueError: Cannot reshape a tensor with 10 elements to shape [784,10] (7840 elements) for 'foo_1/Reshape_1' (op: 'Reshape') with input shapes: [10], [2]., but I don't know how to be here without reshaping (I tried to reshape to both s1 and s2, no use)
I have already read these topics:Update values of a matrix variable in tensorflow, advanced indexing (feed_dict doesn't seem to work in my case), python numpy ValueError: operands could not be broadcast together with shapes and practically everything on the subject on stackoverflow =(
I would like to use a generic filter to calculate the mean of values within a given window (or kernel), for values that fulfill a couple of conditions. I expected the following code to produce a mean filter of the first array in a 3-layer window, using the other two arrays to mask values from the mean calculation.
from scipy import ndimage
import numpy as np
#some test data
tstArr = np.random.rand(3,7,7)
tstArr = tstArr*10
tstArr = np.int_(tstArr)
tstArr[1] = tstArr[1]*100
tstArr[2] = tstArr[2] *1000
#mean function
def testFun(tstData,processLayer,nLayers,kernelSize):
funData= tstData.reshape((nLayers,kernelSize,kernelSize))
meanLayer = funData[processLayer]
maskedData = meanLayer[(funData[1]>1)&(funData[2]<9000)]
returnMean = np.mean(maskedData)
return returnMean
#number of layers in the array
nLayers = np.shape(tstArr)[0]
#window size
kernelSize = 5
#create a sampling window of 5x5 elements from each array
footprnt = np.ones((nLayers,kernelSize,kernelSize),dtype = np.int)
# calculate the mean of the first layer in the array (other two are for masking)
processLayer = 0
tstOut = ndimage.generic_filter(tstArr, testFun, footprint=footprnt, extra_arguments = (processLayer,nLayers,kernelSize))
I thought this would yield a 7x7 array of masked mean values from the first layer in the input array. The output is a 3x7x7 array, and I don't understand what the values represent. I'm not sure how to produce the "masked" mean-filtered array, or how to interpret the output as given.
Your code produce a mean filter of the first array in a 3-layer window, using the over two arrays to mask values from the mean calculation. You will find the result in tstOut[1].
What is going on ? When you call ndimage.generic_filter with tstArr of shape (3, 7, 7) and footprint=np.ones((3, 5, 5)) then for all i from 0 to 2, for all j from 0 to 6 and for all k from 0 to 6, testFun is called with the subarray of tstArr centered in (i, j, k) and of shape (3, 5, 5) (the array is reflected at the boundary to supply missing values).
In the end:
tstOut[0] is the mean filter of tstArr[0] with tstArr[0] and tstArr[1] as masks
tstOut[1] is the mean filter of tstArr[0] with tstArr[1] and tstArr[2] as masks
tstOut[2] is the mean filter of tstArr[1] with tstArr[2] and tstArr[2] as masks
Again, the wanted result is in tstOut[1].
I hope this will help you.