The wrapper "with torch.no_grad()" temporarily set all the requires_grad flag to false. An example from the official PyTorch tutorial (https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#gradients) :
x = torch.randn(3, requires_grad=True)
print((x ** 2).requires_grad)
print((x ** 2)....
This small instability at the end of convergence is a feature of Adam (and RMSProp) due to how it estimates mean gradient magnitudes over recent steps and divides by them.
One thing Adam does is maintain a rolling geometric mean of recent gradients and squares of the gradients. The squares of the gradients are used to divide (another rolling mean of) the ...
What kind of loss function would I use here?
Cross-entropy is the go-to loss function for classification tasks, either balanced or imbalanced. It is the first choice when no preference is built from domain knowledge yet.
This would need to be weighted I suppose? How does that work in practice?
Yes. Weight of class $c$ is the size of largest class ...
There are a couple of good threads on Reddit right now (here and here).
I haven't used either of these frameworks, but from reading around and talking to users I gather that support for dynamic graphs in PyTorch is a 'top down design principle', whereas TensorFlow Fold is bolted on to the original Tensorflow framework, so if you're doing anything reasonably ...
At theoretical level, the embedding layer is a linear layer, there is not any difference at all. However, in practice, if you are building a deep learning software, you have to make a difference among them. This is because it does not make sense to apply an embedding layer using traditional matrix multiplication, as the input matrix is very sparse. For this ...
The answer may depend on the significance of the length of the input vector or how it originates.
However, the simplest solution is usually to know the largest size input and use that as number of vectors. If the given input has lesser length, you can do the padding with zeros or appropriate symbols. So instead of having a vector [1, 2, 3] and [1, 2, 2, 3] ...
As of August 14, 2017, you can install Pytorch from peterjc123's fork as follows. Currently, python 3.5 and 3.6 are supported.
# If your main Python version is not 3.5 or 3.6
conda create -n test python=3.6 numpy pyyaml mkl
# for CPU only packages
conda install -c peterjc123 pytorch
# for Windows 10 and Windows Server 2016, CUDA 8
conda install -c ...
As the Distributed GPUs functionality is only a couple of days old [in the v2.0 release version of Pytorch], there is still no documentation regarding that. So, I had to go through the source code's docstrings for figuring out the difference. So, the docstring of the DistributedDataParallel module is as follows:
Implements distributed data parallelism at ...
will make all the operations in the block have no gradients.
In pytorch, you can't do inplacement changing of w1 and w2, which are two variables with require_grad = True. I think that avoiding the inplacement changing of w1 and w2 is because it will cause error in back propagation calculation. Since inplacement change will totally ...
You can build the same model in pytorch. Then extract weights from tensorflow and assign them manually to each layer in pytorch. Depending on the amount of layers it could be time consuming. Building the model depends on the model and I think not everything is possible in pytorch that is possible in tensorflow. Examples how to assign weights in pytorch and ...
in_channels is the number of channels of the input to the convolutional layer. So, for example, in the case of the convolutional layer that applies to the image, in_channels refers to the number of channels of the image. In the case of an RGB image, in_channels == 3 (red, green and blue); in the case of a gray image, in_channels == 1.
out_channels is the ...
model.cuda() by default will send your model to the "current device", which can be set with torch.cuda.set_device(device).
An alternative way to send the model to a specific device is model.to(torch.device('cuda:0')).
This, of course, is subject to the device visibility specified in the environment variable CUDA_VISIBLE_DEVICES.
You can check GPU usage ...
If you look at the Module implementation of pyTorch, you'll see that forward is a method called in the special method __call__ :
def __call__(self, *input, **kwargs):
result = self.forward(*input, **kwargs)
As you construct a Net class by inheriting from the Module class and you override the default behavior ...
There is actually an academic paper for doing so. It is called S-BERT or Sentence-BERT.
They also have a github repo which is easy to work with:
I am using tensorboardX. It supports most of the features of TensorBoard. I am using the Scalar, Images, Distributions, Histograms and Text. Haven't tried the rest like audio and graph. But the repo also contains examples for those usecases. Installation can be done easily with pip. It's all explained in the readme.
There are also other software which ...
If you are looking for something easy to use and to read, definitely go for Keras.
Example of CNN in Keras :
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
model.add(Conv2D(64, (3, 3), activation='relu'))
Assuming both of x_data and labels are lists or numpy arrays,
train_data = 
for i in range(len(x_data)):
trainloader = torch.utils.data.DataLoader(train_data, shuffle=True, batch_size=100)
i1, l1 = next(iter(trainloader))
DataParallel is easier to debug, because your training script is contained in one process. DataParallel may also cause poor GPU-utilization, because one master GPU must hold the model, combined loss, and combined gradients of all GPUs.
For a more detailed explanation, see here.
I think you might have misunderstood the fixed number of inputs for the RNN. This is the number of inputs per timestep. All your examples have a fixed number of inputs per timestep: 1! You feed them one at a time to your neural network, finishing with a special "end" token (you could always have a second input for this). Teach it to give no output until it ...
There can be some other factors that affect this, such as using simulated annealing (in a NN context) or other learning rate schedules. Are you using a specific LR schedule?
A schedule might be that the LR decreases by 50%, every time the validation loss of 5 epochs in a row does not decrease. This will help get closer and closer to a minimum of the loss. ...
Hello and welcome to Stack Exchange!
The answer to your question is quite simple: you did not use the correct formula.
The formula you used is (assuming we are working with square inputs)
but the correct formula is
Now if we redo your calculations starting with $(1 \times 28 \times 28)$ inputs:
If you know that your output are positive, I think it makes more sense to enforce the positivity in your neural network by applying relu function or softplus $\ln(1. + \exp(x))$. You could also have a look at Generalized models which extend linear regresssion to cases where the variable to predict is only positive (Gamma regression) or between 0 and 1 (...
An r in front of a string makes it a raw string literal. In short it means that any \ in that string will not be seen as an escape character, like when you write \n for newline.
Some more info can be found here:
What exactly do “u” and “r” string flags do, and what are raw string literals?
That's correct, you need a NVIDIA GPU compatible with CUDA 8, 9 or 10: https://pytorch.org/get-started/locally/
NVIDIA has a list of compatible cards here: https://developer.nvidia.com/cuda-gpus#compute
Alternatively you could work on a GPU equipped cloud instance (or install pytorch without GPU).
I was surfing around at PyTorch's website and found a calculation of perplexity. You can examine how they calculated it as ppl as follows:
criterion = nn.CrossEntropyLoss()
total_loss = 0.
for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
loss = criterion(output.view(-1, ntokens), targets)
Knowing that the first list is pretty much invariant (just describing a certain geometry) you could also try creating many different, specialized NN for every distinct in_1 configuration and use only in_2 for feeding the network.
So in_1 could drive different networks.i.e.
in_1=[1,1]? --> NN #1 (n1) --> (o1)
in_1=[2,1]? --> NN #2 (n1,n2) --> (...
I am also using Tensorboard-PyTorch (TensorboardX). Over all I am quite happy with it.
But don't try to visualize graphs. At least none with a bit of complexity (e.g. a resnet50 won't work). There are some issues about it on their github page.
But better check out the Pytorch forum frequently. Pytorch seems to move quite fast. And a direct tensorboard ...
You have (at least) two options. You can either:
Use a bottleneck architecture where you use pooling layers to reduce the dimensions of your data and then upcast it again using deconvolutional/upsampling layers to go back to the original dimensions. The advantage of this approach is that you use a bigger receptive field for the final output, which means it ...