I have a tensor which which is composed of 2D arrays representing audio frames with shape 121*400. I want to shuffle the rows of individual arrays and not the arrays contained in the tensor. Is this possible without iterating through the tensor and shuffling each array.
To shuffle each array you could use this:
def tf_shuffle_second_axis(t):
# Uniquely random along second axis
rnd = tf.argsort(tf.random.uniform(t.shape),axis=1)
# Add batch dimension for gathering
rnd = tf.concat([tf.repeat(tf.range(t.shape[0])[...,tf.newaxis,tf.newaxis],tf.shape(rnd)[1],axis=1),rnd[...,tf.newaxis]],axis=2)
# Return shuffled tensor
return tf.gather_nd(t,rnd,batch_dims=0)
For example
a = tf.reshape(tf.range(16), [4,4])
<tf.Tensor: shape=(4, 4), dtype=int32, numpy=
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]], dtype=int32)>
tf_shuffle_second_axis(a)
Output
<tf.Tensor: shape=(4, 4), dtype=int32, numpy=
array([[ 2, 1, 0, 3],
[ 4, 6, 5, 7],
[ 9, 11, 10, 8],
[14, 12, 13, 15]], dtype=int32)>
UPDATE
To shuffle the whole row use tf.shuffle
tf.random.shuffle(a)
Output
<tf.Tensor: shape=(4, 4), dtype=int32, numpy=
array([[ 8, 9, 10, 11],
[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[12, 13, 14, 15]], dtype=int32)>
Related
I have a numpy array of 2D shape
a=np.array([[1,2,3,4,5,6],
[7,8,9,10,11,12],
[13,14,15,16,17,18]])
and trying to convert into 3D shape of dimension (3,3,2) i.e,
np.array([[ 1,2,3],
[7,8,9],
[13,14,15]])
in 3rd dimension with index 1 and
np.array([[4,5,6],
[10,11,12],
[16,17,18]])
in 3rd dimension with index 2.
I tried to reshape as a.reshape(3,3,2) and getting this
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]],
[[13, 14, 15],
[16, 17, 18]]])
Any suggestions to convert this?
Use swapaxes:
a.reshape(3,2,3).swapaxes(0,1)
output:
array([[[ 1, 2, 3],
[ 7, 8, 9],
[13, 14, 15]],
[[ 4, 5, 6],
[10, 11, 12],
[16, 17, 18]]])
Using tensorflow.stack what does it mean to have axis=-1 ?
I'm using tensorflow==1.14
Using axis=-1 simply means to stack the tensors along the last axis (as per the python list indexing syntax).
Let's take a look at how this looks like using these tensors of shape (2, 2):
>>> x = tf.constant([[1, 2], [3, 4]])
>>> y = tf.constant([[5, 6], [7, 8]])
>>> z = tf.constant([[9, 10], [11, 12]])
The default behavior for tf.stack as described in the documentation is to stack the tensors along the first axis (index 0) resulting in a tensor of shape (3, 2, 2)
>>> tf.stack([x, y, z], axis=0)
<tf.Tensor: shape=(3, 2, 2), dtype=int32, numpy=
array([[[ 1, 2],
[ 3, 4]],
[[ 5, 6],
[ 7, 8]],
[[ 9, 10],
[11, 12]]], dtype=int32)>
Using axis=-1, the three tensors are stacked along the last axis instead, resulting in a tensor of shape (2, 2, 3)
>>> tf.stack([x, y, z], axis=-1)
<tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
array([[[ 1, 5, 9],
[ 2, 6, 10]],
[[ 3, 7, 11],
[ 4, 8, 12]]], dtype=int32)>
Question
Please confirm if the below is as designed and expected, or an issue of tf. slice, or a mistake in the usage of tf. slice. If a mistake, kindly suggest how to correct it.
Background
Introduction to tensor slicing - Extract tensor slices says Numpy-like slice syntax is an alternative of tf. slice.
Perform NumPy-like tensor slicing using tf. slice.
t1 = tf.constant([0, 1, 2, 3, 4, 5, 6, 7])
print(tf.slice(t1,
begin=[1],
size=[3]))
Alternatively, you can use a more Pythonic syntax. Note that tensor slices are evenly spaced over a start-stop range.
print(t1[1:4])
Problem
To Update the dark orange region.
TYPE = tf.int32
N = 4
D = 5
shape = (N,D)
# Target to update
Y = tf.Variable(
initial_value=tf.reshape(tf.range(N*D,dtype=TYPE), shape=shape),
trainable=True
)
print(f"Target Y: \n{Y}\n")
---
Target Y:
<tf.Variable 'Variable:0' shape=(4, 5) dtype=int32, numpy=
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]], dtype=int32)>
tf. slice does not work.
# --------------------------------------------------------------------------------
# Slice region in the target to be updated
# --------------------------------------------------------------------------------
S = tf.slice( # Error "EagerTensor' object has no attribute 'assign'"
Y,
begin=[0,1], # Coordinate (n,d) as the start point
size=[3,2] # Shape (3,2) -> (n+3, n+2) as the end point
)
print(f"Slice to update S: \n{S}\n")
# Values to set
V = tf.ones(shape=tf.shape(S), dtype=TYPE)
print(f"Values to set V: \n{V}\n")
# Assing V to S region of T
S.assign(V)
---
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-17-e5692b1750c8> in <module>
24
25 # Assing V to S region of T
---> 26 S.assign(V)
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'assign'
However, slice syntax works.
S = Y[
0:3, # From coordinate (n=0,d), slice rows (0,1,2) or 'size'=3 -> shape (3,?)
1:3 # From coordinate (n=0,d=1), slice columns (1,2) or 'size'=2 -> shape (3,2)
]
print(f"Slice to update S: \n{S}\n")
# Values to set
V = tf.ones(shape=tf.shape(S), dtype=TYPE)
print(f"Values to set V: \n{V}\n")
# Assing V to S region of T
S.assign(V)
---
<tf.Variable 'UnreadVariable' shape=(4, 5) dtype=int32, numpy=
array([[ 0, 1, 1, 3, 4],
[ 5, 1, 1, 8, 9],
[10, 1, 1, 13, 14],
[15, 16, 17, 18, 19]], dtype=int32)>
In my understanding, the above behavior is expected or not a bug at least. As the error said, there is no attribute called assign in tf. Tensor (EagerTensor for eager execution) but there is in tf. Variable. And generally, tf. slice returns a tensor as its output and thus it doesn't possess assign attribute.
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'assign'
But when we do np like slicing and use it to modify the original tf. Variable, it seamlessly works.
Possible Solution
A workaround is to use tf.strided_slice instead of tf.slice. If we follow the source code of it, we will see, it takes the var argument which is a variable corresponding to input_
#tf_export("strided_slice")
#dispatch.add_dispatch_support
def strided_slice(input_,
begin,
end,
..........
var=None,
name=None):
And when we pass a parameter for var that basically corresponding to the input_, it then calls assign function that is defined within it
def assign(val, name=None):
"""Closure that holds all the arguments to create an assignment."""
if var is None:
raise ValueError("Sliced assignment is only supported for variables")
else:
if name is None:
name = parent_name + "_assign"
return var._strided_slice_assign(
begin=begin,
end=end,
strides=strides,
value=val,
name=name,
begin_mask=begin_mask,
end_mask=end_mask,
ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask)
So, when we pass var in the tf.strided_slice, it will return an assignable object.
Code
Here is the full working code for reference.
import tensorflow as tf
print(tf.__version__)
TYPE = tf.int32
N = 4
D = 5
shape = (N,D)
# Target to update
Y = tf.Variable(
initial_value=tf.reshape(tf.range(N*D,dtype=TYPE), shape=shape),
trainable=True
)
Y
2.4.1
<tf.Variable 'Variable:0' shape=(4, 5) dtype=int32, numpy=
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]], dtype=int32)>
Now, we use tf.stried_slice instead of tf.slice.
S = tf.strided_slice(
Y,
begin = [0, 1],
end = [3, 3],
var = Y,
name ='slice_op'
)
S
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[ 1, 2],
[ 6, 7],
[11, 12]], dtype=int32)>
Update the variables with no attribution error.
# Values to set
V = tf.ones(shape=tf.shape(S), dtype=TYPE)
print(V)
print()
# Assing V to S region of T
S.assign(V)
tf.Tensor(
[[1 1]
[1 1]
[1 1]], shape=(3, 2), dtype=int32)
<tf.Variable 'UnreadVariable' shape=(4, 5) dtype=int32, numpy=
array([[ 0, 1, 1, 3, 4],
[ 5, 1, 1, 8, 9],
[10, 1, 1, 13, 14],
[15, 16, 17, 18, 19]], dtype=int32)>
Using np like slicing.
# slicing
S = Y[
0:3,
1:3
]
S
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[ 1, 2],
[ 6, 7],
[11, 12]], dtype=int32)>
# Values to set
V = tf.ones(shape=tf.shape(S), dtype=TYPE)
print(V)
# Assing V to S region of T
S.assign(V)
tf.Tensor(
[[1 1]
[1 1]
[1 1]], shape=(3, 2), dtype=int32)
<tf.Variable 'UnreadVariable' shape=(4, 5) dtype=int32, numpy=
array([[ 0, 1, 1, 3, 4],
[ 5, 1, 1, 8, 9],
[10, 1, 1, 13, 14],
[15, 16, 17, 18, 19]], dtype=int32)>
Materials
tf.Variable - tf.Tensor.
PyTorch doesn't seem to have documentation for tensor.stride().
Can someone confirm my understanding?
My questions are three-fold.
Stride is for accessing an element in the storage. So stride size will be the same as the dimension of the tensor. Correct?
For each dimension, the corresponding element of stride tells how much it takes to move along the 1-dimensional storage. Correct?
For example:
In [15]: x = torch.arange(1,25)
In [16]: x
Out[16]:
tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
, 19, 20, 21, 22, 23, 24])
In [17]: a = x.view(4,3,2)
In [18]: a
Out[18]:
tensor([[[ 1, 2],
[ 3, 4],
[ 5, 6]],
[[ 7, 8],
[ 9, 10],
[11, 12]],
[[13, 14],
[15, 16],
[17, 18]],
[[19, 20],
[21, 22],
[23, 24]]])
In [20]: a.stride()
Out[20]: (6, 2, 1)
How does having this information help perform tensor operations efficiently? Basically this is showing the memory layout. So how does it help?
trainX.size == 43120000
trainX = trainX.reshape([-1, 28, 28, 1])
(1)Does reshape accept a list as an argment instead of a tuple?
(2)Are the following two statements equivalent?
trainX = trainX.reshape([-1, 28, 28, 1])
trainX = trainX.reshape((55000, 28, 28, 1))
Try the variations:
In [1]: np.arange(12).reshape(3,4)
Out[1]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [2]: np.arange(12).reshape([3,4])
Out[2]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [3]: np.arange(12).reshape((3,4))
Out[3]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
With the reshape method, the shape can be arguments, a tuple or a list. In the reshape function is has to be in a list or tuple, to separate them from the first array argument
In [4]: np.reshape(np.arange(12), (3,4))
Out[4]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
and yes, one -1 can be used. The total size of the reshape is fixed, so one value can be deduced from the others.
In [5]: np.arange(12).reshape(-1,4)
Out[5]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
The method documentation has this note:
Unlike the free function numpy.reshape, this method on ndarray allows
the elements of the shape parameter to be passed in as separate arguments.
For example, a.reshape(10, 11) is equivalent to
a.reshape((10, 11)).
It's a builtin function, but the signature looks like x.reshape(*shape), and it tries to be flexible as long as the values make sense.
From the numpy documentation:
newshape : int or tuple of ints
The new shape should be compatible with the original shape. If an
integer, then the result will be a 1-D array of that length. One shape
dimension can be -1. In this case, the value is inferred from the
length of the array and remaining dimensions.
So yes, -1 for one dimension is fine and your two statements are equivalent. About the tuple requirement,
>>> import numpy as np
>>> a = np.arange(9)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8])
>>> a.reshape([3,3])
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>>
So apparently a list is good as well.