autograd | Efficiently computes derivatives of numpy code | Machine Learning library
kandi X-RAY | autograd Summary
kandi X-RAY | autograd Summary
Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization. For more information, check out the tutorial and the examples directory.
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autograd Key Features
autograd Examples and Code Snippets
Lines of Code : 43License : Permissive (Apache-2.0)
Copyfrom jax import random, pmap
import jax.numpy as jnp
# Create 8 random 5000 x 6000 matrices, one per GPU
keys = random.split(random.PRNGKey(0), 8)
mats = pmap(lambda key: random.normal(key, (5000, 6000)))(keys)
# Run a local matmul on each device i from jax import grad
import jax.numpy as jnp
def tanh(x): # Define a function
y = jnp.exp(-2.0 * x)
return (1.0 - y) / (1.0 + y)
grad_tanh = grad(tanh) # Obtain its gradient function
print(grad_tanh(1.0)) # Evaluate it at x = 1.0
# prints 0 import jax.numpy as jnp
from jax import grad, jit, vmap
def predict(params, inputs):
for W, b in params:
outputs = jnp.dot(inputs, W) + b
inputs = jnp.tanh(outputs) # inputs to the next layer
return outputs # no activatio """Convolutional neural net on MNIST, modeled on 'LeNet-5',
http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf"""
from __future__ import absolute_import
from __future__ import print_function
from builtins import range
import autograd.numpy as np
imp # Implements black-box variational inference, where the variational
# distribution is a mixture of Gaussians.
#
# This trick was written up by Alex Graves in this note:
# http://arxiv.org/abs/1607.05690
from __future__ import absolute_import
from __ from __future__ import absolute_import
from __future__ import print_function
from builtins import range
import autograd.numpy as np
from autograd import value_and_grad
from scipy.optimize import minimize
import matplotlib.pyplot as plt
import os
r #
top_k_history = np.array(top_k_history) # new
jac(top_k_history) # original last line
if dis_loss is not None:
dis_loss.backward()
self.dis_optimizer.step()
if gen_loss is not None:
gen_loss.backward()
self.gen_optimizer.step()
class NN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(10, 4)
self.fc2 = nn.Linear(4, 2)
def forward(self, x)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(xX = torch.tensor([[ 2., 1., -3],
[ -3, 4., 2.]], requires_grad=True)
W = torch.tensor([[ 3., 2., 1., -1],
[ 2., 1., 3., 2.],
[ 3., 2., 1., -2]], requires_grad=True)
Z = torch.matmuCommunity Discussions
Trending Discussions on autograd
QUESTION
I am trying to load a GPT2 fine tuned model in flask initially. The model is being loaded during the init functions using:
...ANSWER
Answered 2021-Nov-20 at 11:21This issue is found to be occurring only if the framework is run using venv or deployment frameworks like uWSGI or gunicorn. It is resolved when transformers version 4.10.0 is used instead of the latest package.
QUESTION
I have a loss function that depends on an "exponential moving average" Z. A minimal example (pay special attention to the getUpdatedZ function):
ANSWER
Answered 2022-Mar-07 at 16:38After some trials, I think that the error arises because you are computing a recursive function (Z = getUpdatedZ(X, Z)) but you are changing some of its parameters (the weights of the Linear modules) at each iteration through optimizer.step().
You can backward() just at the end of the for cycle, or you may want to break the autodifferentiation graph, for example by calling Z.detach() after loss.backward(). Sometimes this trick is used to avoid too complex and inefficient backpropagations (check, for example this).
However in both cases, this will change the structure of the optimized function, so be sure of what you are doing.
QUESTION
Can someone enlighten me why the following code to compute the jacobian of kernel matrix doesn't work:
...ANSWER
Answered 2022-Mar-01 at 15:14There is a datatype problem. I your code top_k_history is of type list and contains 10 1D-arrays, each of length 10. If you convert this into 1 2D-array of shape (10, 10), then the error should vanish:
QUESTION
I'm trying to implement a loss function that depends on the gradient of the network with respect to its inputs. That is, the loss function has a term like
sum(u - grad_x(network(x)))
where u is computed by forward propagating x through the network.
I'm able to compute the gradient by calling
...ANSWER
Answered 2022-Feb-28 at 19:27As per comment by @aretor, setting retain_graph=True, create_graph=False in the grad call in the loss function, and retain_graph=True in backward solves the issue.
QUESTION
I am new to PyTorch and Machine Learning so I try to follow the tutorial from here: https://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118
By copying the code step by step I got the following error for no reason. I tried the program on another computer and it gives syntax error. However, my IDE didn't warn my anything about syntax. I am really confused how I can fix the issue. Any help is appreciated.
...ANSWER
Answered 2022-Feb-25 at 06:42If you are working on jupyter notebook. The problem is more likely to be num_worker. You should set num_worker=0. You can find here some solutions to follow. Because unfortunately, jupyter notebook has some issues with running multiprocessing.
QUESTION
I am currently working on an neuronal network that can classify cats and dog and everything thats not cat nor dog. And my programm has this: error i can't solve:
" File "/home/johann/Schreibtisch/NN_v0.01/classification.py", line 146, in train(epoch) File "/home/johann/Schreibtisch/NN_v0.01/classification.py", line 109, in train loss = criterion(out, target) File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py", line 1047, in forward return F.cross_entropy(input, target, weight=self.weight, File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 2693, in cross_entropy return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction) File "/home/johann/.local/lib/python3.8/site-packages/torch/nn/functional.py", line 2388, in nll_loss ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: 1D target tensor expected, multi-target not supported"
The code:
...ANSWER
Answered 2022-Feb-16 at 15:35The reason behind this error is that your targets list are list of lists like that:
QUESTION
I'm using Julia to autograde students' work. I have all of their files Student1.jl, Student2.jl, etc. as separate modules Student1, Student2, etc in a directory that is part of LOAD_PATH. What I want to be able to do works completely fine in the REPL, but fails in a file.
ANSWER
Answered 2022-Feb-08 at 10:53The code just as you have written works for me on julia versions 1.1.0, 1.3.1, 1.5.1, 1.6.0 and 1.7.0. By that I mean, if I add an inputs variable and put your first code block in a file Autograder.jl and run JULIA_LOAD_PATH="modules:$JULIA_LOAD_PATH" julia Autograder.jl with the student modules in the modules directory I get the output of the call_function function in the Student1 module.
However if Autograder.jl actually contains a module then the Student$number module is not required into Main and your macro needs to be modified accordingly:
QUESTION
I am building a simple autoencoder followed by an MLP neural nets. Regarging the autoencoder I am not running into any problem
...ANSWER
Answered 2022-Jan-18 at 20:45You should be able to disconnect the output of the auto-encoder from the model by calling embbeding.detach(), before appending it to outputs.
QUESTION
I ran into this weird behavior when trying to "manually" optimize a network's parameters via SGD. When attempting to update the model's parameters using the following way, it works just fine:
...ANSWER
Answered 2022-Jan-12 at 14:27It took me a while to figure out, but the problem was in loss.backward(). Unlike autograd.grad() which computes and returns the gradients, the inplace backward() computes and accumulates the gradients of participating nodes in the computation graph. In other words, the two will have the same effect when used to back-prop once, but every repetition of backward() will add the currently computed gradients to all previous ones (hence the divergence). Resetting the gradients using model.zero_grad() fixes stuff.
QUESTION
I'm new to automatic differentiation programming, so this maybe a naive question. Below is a simplified version of what I'm trying to solve.
I have two input arrays - a vector A of size N and a matrix B of shape (N, M), as well a parameter vector theta of size M. I define a new array C(theta) = B * theta to get a new vector of size N. I then obtain the indices of elements that fall in the upper and lower quartile of C, and use them to create a new array A_low(theta) = A[lower quartile indices of C] and A_high(theta) = A[upper quartile indices of C]. Clearly these two do depend on theta, but is it possible to differentiate A_low and A_high w.r.t theta?
My attempts so far seem to suggest no - I have using the python libraries of autograd, JAX and tensorflow, but they all return a gradient of zero. (The approaches I have tried so far involve using argsort or extracting the relevant sub-arrays using tf.top_k.)
What I'm seeking help with is either a proof that the derivative is not defined (or cannot be analytically computed) or if it does exist, a suggestion on how to estimate it. My eventual goal is to minimize some function f(A_low, A_high) wrt theta.
ANSWER
Answered 2021-Dec-03 at 16:44This is the JAX computation that I wrote based on your description:
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