I am trying to deploy a web app to Heroku. The free tier is limited to 500 MB. I am using my resnet34 model as a .pkl file. I create model with it using the fastai library. This project requires torch and torchvision as dependencies. But not specifying the dependency will download the latest version of torch which alone is 750 MB and exceeds the memory limit. So, I specify torchvision version as 0.2.2 and specify the wheel for torch …
I have trained Text classifier using simpleTranformer.ai I am struggling to save and load the model in docker container. Please let me know how can I save the trained model and then load it into different environment smoothly. I am using this library to : https://simpletransformers.ai/ to train a text model using these commands model = ClassificationModel('xlmroberta', 'xlm-roberta-base',use_cuda=cuda_available, num_labels=78, args={'learning_rate':1e-5, 'num_train_epochs': 1,'train_batch_size':256,'eval_batch_size':1048, 'n_gpu':4, 'reprocess_input_data': True, 'overwrite_output_dir':True, 'overwrite_output_dir': True}) model.train_model(train_df) I am saving the trained model using pytorch function: torch.save(model, 'classifier') …
I want my Mx2xN float tensor to become MxN complex tensor. In this minimal example I supply the the 10x2 matrix which should become the vector [0+10j,1+11j,2+12k,...] x = torch.stack([torch.arange(10.),torch.arange(10.,20)]) torch.view_as_complex(x.T) gives the error RuntimeError: Tensor must have a last dimension with stride 1 because x.T.stride() is (1,10) Trying >>> x.T.as_strided(size=(2,10),stride=(10,1)).T.stride() (1, 10) Didn't get me any further, how do efficiently convert these?
Let us say I have the loading images from my local files using the pytorch torchvision datasets.ImageFolder as follows: train_data = datasets.ImageFolder( os.path.join(out_dir, "Training"), transform=transforms.Compose([ transforms.Resize([224, 224]), # alenet image size transforms.ToTensor() # so that we will be able to calculate mean and std ]) ) How can I efficiently calculate the means and stds for each color channel I know when loading dataset from torchvision.dataset I can do it as follows: train_data = datasets.CIFAR10('.', train=True, download=True ) means = …
The following code is the KL divergence between a Gaussian posterior and mixture of Gaussian priors and it is part of the model described in this paper. The published code is written in torch language function KLDivergence(D, M) -- KL = 1/2( logvar2 - logvar1 + (var1 + (m1-m2)^2)/var2 - 1 ) local mean1_in = - nn.Identity() local logVar1_in = - nn.Identity() local mean2_in = - nn.Identity() -- [(MxN)xD] local logVar2_in = - nn.Identity() -- [(MxN)xD] local mean1 = mean1_in …
I am trying to train my model. My model outputs a [4,2] tensor where 4 is the batch size and 2 because of binary classification. After receiving the outputs I found the index of the maximum element for each row.so , now the shape is [4,1] and the shape of my label is [4,1] as well.I could not understand why am I still getting this error.Could someone please help me to solve it out.Also, the optimizer I am using is …
does anyone have any advice on how to implement this loss in order to use it with a convolutional neural network? Also, how should I encode the labels of my training data? We were using one hot encoding with bce loss before and I was wandering if I should keep it that way also for the hinge loss, since the label itself is not used in the formula of the loss other than for indicating which one is the true …
Documentaion for Spatial Convolution define it as module = nn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, [dW], [dH], [padW], [padH]) nInputPlane: The number of expected input planes in the image given into forward(). nOutputPlane: The number of output planes the convolution layer will produce. I don't have any experience with torch but i guess i have used a similar function in keras Convolution2D(64, 3, 3, border_mode='same', input_shape=(3, 256, 256)) which takes as input the shape of the image that is 256*256 in rgb. …
I am trying to write a neural network that will train on plays by Shakespeare and then write its own passages. I am using pytorch. For some reason, my GPU immediately runs out of memory. Note I am not running it on my own GPU; I am running it using the free GPU acceleration from Google Colab. I've tried running a different notebook using the GPU and it works, so I know it's not because I ran into some GPU …
When I use a learning rate higher than 0.001, I get this: Assertion `input_val >= zero && input_val <= one` failed. This means that the input I gave to BCE is above 1 or below 0 right? Why does changing the learning rate cause this error? Also, I am passing the input through a sigmoid activation function before passing it to the BCE so it should be between 0 and 1.
I am trying to run a github deep learning repository in Colab but I can not connect the python files to colab GPU. I can connect to GPU when writing a script in the colab notebook e.g. when I run this cod from a notebook cell : import os, torch print('Torch', torch.__version__, 'CUDA', torch.version.cuda) print('Device:', torch.device('cuda:0')) print(torch.cuda.is_available()) I get: Torch 1.4.0 CUDA 10.1 Device: cuda:0 True but when I run it from a file called myExample.py e.g. using !python myExample.py …
I have a TabularDataset and i would like to add some examples to the dataset. dataset = TabularDataset(path=path, format="csv", fields=[('label', l_f),('data', d_f)]) # I tried this dataset.examples = new_examples # new_examples is a list of examples len(dataset) == len(new_examples) # This is True I can see that the length of the dataset has changed and in examples i have the examples from new_exampels, but i don't think this is the wright way to do it because i think that it …
this code works with a VGG caffe model (VGG_ILSVRC_19_layers.caffemodel). When I tried to change the model to a LeNet caffemodel (below the prototxt file), I got the following error. In 6 module of nn.Sequential: xx/.luarocks/share/lua/5.1/nn/Linear.lua:66: size mismatch, m1: [1 x 594050], m2: [140450 x 500] at xx/torch/pkg/torch/lib/TH/generic/THTensorMath.c:1293 Any suggestions? Many thanks! =========================== Code net = loadcaffe.load(opt.cnn_proto, opt.cnn_model, opt.backend); --for i = 1, 9 do -- Lines removed when using LeNet -- net:remove() --end print(net) if opt.gpuid >= 0 then require …
I was wondering if it was okay to use torch.cat within my forward function. I am doing so because I want the first two columns of my input to skip the middle hidden layers and go directly to the final layer. Here is my code: you can see that I use torch.cat at the last moment to make xcat. Does the gradient propagate back? or does the torch.cat cover up what happened to my hidden variables? class LinearRegressionForce(nn.Module): def __init__(self, …
I am new to machine learning and have quite good understanding of basic concepts. I was implementing a 3 Layer neural network on MNIST dataset with 784, 100, 10 neurons in input, hidden, output layer respectively. I did not use any regularization here. Firstly I trained the network on Intel i5 4th generation quad-core CPU with 4GB of ram which gave me 64% of accuracy. Then I trained the exact same network, with exact same code on Intel i5 7th …
I want to train a word predictability task to generate word embeddings. The document collection contains 243k documents. The code implementation is in torch. I am struggling with the huge size of the dataset and need ideas on how to train word embeddings on such a large dataset which is a collection of 243 thousand full article documents. The research computing resource is timed so get short access to GPU node and so opted for Incremental model training: Incremental Model …