I've written my own tensor library and a corresponding Python binding. And I've made sure iterating through my tensor implementation works exactly like how NumPy works. I also made sure important method calls like __len__, __getitem__, __setitem__, etc... all works like how NumPy expected it to be. And so I expect
t = my_tensor.ones((4, 4))
print(t) # works
a = np.array(t)
print(a) # becomes a 32 dimension array
to give me a 4x4 matrix. But instead it gave me a 4x4x1x1.... (32 dims in total) array. I'm out of ways to debug this problem without knowing how NumPy performs the conversion internally. How does np.array works internally? I'm unable to locate the function within NumPy's source code nor I can find useful information on the web.
Have you tried looking at the official Numpy's documentation? https://numpy.org/doc/stable/contents.html
Questions specific as this one are usually solved by looking at the original library documentation (e.g. https://numpy.org/doc/stable/user/quickstart.html#array-creation)
Related
For example, I want to do a matrix multiplication, and in doing so, I use the tf.matmul operation inside the tensorflow. And, i want to optimize matrix mulptiplication in tf. However, I cannot reach where the matrix Multiplication is made exactly in tf_matmul. Is there any people who can help me to do this ?
We need to do some code tracing to figure out what is being called and what is happening
1) tensorflow.python.ops.math_ops called via tf.matmul
2) tf.matmul returns either a sparse_matmul (which calls gen_math_ops.sparse_matmul) or gen_math_ops.batch_mat_mul
3) The gen_math_ops script is automatically generated but the underlying code is math_ops.cc
All the best!
I am new to TensorFlow and just went through the eager execution tutorial and came across the tf.decode_csv function. Not knowing about it, I read the documentation. https://www.tensorflow.org/api_docs/python/tf/decode_csv
I don't really understand it.
The documentation says 'records: A Tensor of type string.'
So, my question is: What qualifies as a 'Tensor'?
I tried the following code:
dec_res = tf.decode_csv('0.1,0.2,0.3', [[0.0], [0.0], [0.0]])
print(dec_res, type(dec_res))
l = [[1,2,3],[4,5,6],[7,8,9]]
r = tf.reshape(l, [9,-1])
print(l, type(l))
print(r, type(r))
So the list dec_res contains tf.tensor objects. That seems reasonable to me. But is an ordinary string also a 'Tensor' according to the documentation?
Then I tried something else with the tf.reshape function. In the documentation https://www.tensorflow.org/api_docs/python/tf/reshape it says that 'tensor: A Tensor.' So, l is supposed to be a tensor. But it is not of type tf.tensor but simply a python list. This is confusing.
Then the documentation says
Returns:
A Tensor. Has the same type as tensor.
But the type of l is list where the type of r is tensorflow.python.framework.ops.Tensor. So the types are not the same.
Then I thought that TensorFlow is very generous with things being a tensor. So I tried:
class car(object):
def __init__(self, color):
self.color = color
red_car = car('red')
#test_reshape = tf.reshape(red_car, [1, -1])
print(red_car.color) # to check, that red_car exists.
Now, the line in comments results in an error.
So, can anyone help me to find out, what qualifies as a 'Tensor'?
P.S.: I tried to read the source code of tf.reshape as given in the documentation
Defined in tensorflow/python/ops/gen_array_ops.py.
But this file does not exist in the Github repo. Does anyone know how to read it?
https://www.tensorflow.org/programmers_guide/tensors
TensorFlow, as the name indicates, is a framework to define and run
computations involving tensors. A tensor is a generalization of
vectors and matrices to potentially higher dimensions. Internally,
TensorFlow represents tensors as n-dimensional arrays of base
datatypes.
What you are observing commes from the fact that tensorflow operations (like reshape) can be built from various python types using the function tf.convert_to_tensor:
https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor
All standard Python op constructors apply this function to each of
their Tensor-valued inputs, which allows those ops to accept numpy
arrays, Python lists, and scalars in addition to Tensor objects
Suppose I have a PyTorch Variable in GPU:
var = Variable(torch.rand((100,100,100))).cuda()
What's the best way to copy (not bridge) this variable to a NumPy array?
var.clone().data.cpu().numpy()
or
var.data.cpu().numpy().copy()
By running a quick benchmark, .clone() was slightly faster than .copy(). However, .clone() + .numpy() will create a PyTorch Variable plus a NumPy bridge, while .copy() will create a NumPy bridge + a NumPy array.
This is a very interesting question. According to me, the question is little bit opinion-based and I would like to share my opinion on this.
From the above two approaches, I would prefer the first one (use clone()). Since your goal is to copy information, essentially you need to invest extra memory. clone() and copy() should take a similar amount of storage since creating numpy bridge doesn't cause extra memory. Also, I didn't understand what you meant by, copy() will create two numPy arrays. And as you mentioned, clone() is faster than copy(), I don't see any other problem with using clone().
I would love to give a second thought on this if anyone can provide some counter arguments.
Because clone() is recorded by AD second options is less intense. There are few options you may also consider.
TensorFlow provides a tf.summary.tensor_summary() function that appears to be a multidimensional variant of tf.summary.scalar():
tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None)
I thought it could be useful for summarizing inferred probabilities per class ... somewhat like
op_summary = tf.summary.tensor_summary('classes', some_tensor)
# ...
summary = sess.run(op_summary)
writer.add_summary(summary)
However it appears that TensorBoard doesn't provide a way to display these summaries at all. How are they meant to be used?
I cannot get it to work either. It seems like that feature is still under development. See this video from the TensorFlow Dev Summit that states that the tensor_summary is still under development (starting at 9:17): https://youtu.be/eBbEDRsCmv4?t=9m17s. It will probably be better defined and examples should be provided in the future.
I'm trying to solve a large eigenvalue problem with Scipy where the matrix A is dense but I can compute its action on a vector without having to assemble A explicitly. So in order to avoid memory issues when the matrix A gets big I'd like to use the sparse solver scipy.sparse.linalg.eigs with a LinearOperator that implemements this action.
Applying eigs to an explicit numpy array A works fine. However, if I apply eigs to a LinearOperator instead then the iterative solver fails to converge. This is true even if the matvec method of the LinearOperator is simply matrix-vector multiplication with the given matrix A.
A minimal example illustrating the failure is attached below (I'm using shift-invert mode because I am interested in the smallest few eigenvalues). This computes the eigenvalues of a random matrix A just fine, but fails when applied to a LinearOperator that is directly converted from A. I tried to fiddle with the parameters for the iterative solver (v0, ncv, maxiter) but to no avail.
Am I missing something obvious? Is there a way to make this work? Any suggestions would be highly appreciated. Many thanks!
Edit: I should clarify what I mean by "make this work" (thanks, Dietrich). The example below uses a random matrix for illustration. However, in my application I know that the eigenvalues are almost purely imaginary (or almost purely real if I multiply the matrix by 1j). I'm interested in the 10-20 smallest-magnitude eigenvalues, but the algorithm doesn't behave well (i.e., never stops even for small-ish matrix sizes) if I specify which='SM'. Therefore I'm using shift-invert mode by passing the parameters sigma=0.0, which='LM'. I'm happy to try a different approach so long as it allows me to compute a bunch of smallest-magnitude eigenvalues.
from scipy.sparse.linalg import eigs, LinearOperator, aslinearoperator
import numpy as np
# Set a seed for reproducibility
np.random.seed(0)
# Size of the matrix
N = 100
# Generate a random matrix of size N x N
# and compute its eigenvalues
A = np.random.random_sample((N, N))
eigvals = eigs(A, sigma=0.0, which='LM', return_eigenvectors=False)
print eigvals
# Convert the matrix to a LinearOperator
A_op = aslinearoperator(A)
# Try to solve the same eigenproblem again.
# This time it produces an error:
#
# ValueError: Error in inverting M: function gmres did not converge (info = 1000).
eigvals2 = eigs(A_op, sigma=0.0, which='LM', return_eigenvectors=False)
I tried running your code, but not passing the sigma parameter to eigs() and it ran without problems (read eigs() docs for its meaning). I didn't see the benefit of it in your example.
Eigs can already find the smallest eigenvalues first. Set which = 'SM'