torch.view has existed for a long time. It will return a tensor with the new shape. The returned tensor will share the underling data with the original tensor. See the documentation here. On the other hand, it seems that torch.reshape has been introduced recently in version 0.4. According to the document, this method will Returns … Read more
Yes, you can get exact Keras representation, using the pytorch-summary package. Example for VGG16: from torchvision import models from torchsummary import summary vgg = models.vgg16() summary(vgg, (3, 224, 224)) —————————————————————- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 224, 224] 1,792 ReLU-2 [-1, 64, 224, 224] 0 Conv2d-3 [-1, 64, 224, 224] 36,928 … Read more
The problem is from my training loop: it doesn’t detach or repackage the hidden state in between batches? If so, then loss.backward() is trying to back-propagate all the way through to the start of time, which works for the first batch but not for the second because the graph for the first batch has been … Read more
you can use this formula [(W−K+2P)/S]+1. W is the input volume – in your case 128 K is the Kernel size – in your case 5 P is the padding – in your case 0 i believe S is the stride – which you have not provided. So, we input into the formula: Output_Shape = … Read more
I think the most crucial point to understand here is the difference between a torch.tensor and np.ndarray: While both objects are used to store n-dimensional matrices (aka “Tensors”), torch.tensors has an additional “layer” – which is storing the computational graph leading to the associated n-dimensional matrix. So, if you are only interested in efficient and … Read more
Yes, that is possible. Just create the objects by yourself, e.g. import torch.utils.data as data_utils train = data_utils.TensorDataset(features, targets) train_loader = data_utils.DataLoader(train, batch_size=50, shuffle=True) where features and targets are tensors. features has to be 2-D, i.e. a matrix where each line represents one training sample, and targets may be 1-D or 2-D, depending on whether … Read more
As of 1.8, PyTorch now has LazyLinear which infers the input dimension: A torch.nn.Linear module where in_features is inferred.
please look carefully at the indentation of your __init__ function: your forward is part of __init__ not part of your module.
I have stumbled upon this problem too and below is what I figured out. When training RNN (LSTM or GRU or vanilla-RNN), it is difficult to batch the variable length sequences. For example: if the length of sequences in a size 8 batch is [4,6,8,5,4,3,7,8], you will pad all the sequences and that will result … Read more
model.eval() is a kind of switch for some specific layers/parts of the model that behave differently during training and inference (evaluating) time. For example, Dropouts Layers, BatchNorm Layers etc. You need to turn off them during model evaluation, and .eval() will do it for you. In addition, the common practice for evaluating/validation is using torch.no_grad() … Read more