May 04, 2017 · Hi, Is there a way to declare output = [] in your code as torch.tensor and append in it similar to python numpy list? 1 Like akib62 (Akib Rahman) October 9, 2020, 10:02pm
30.07.2021 · When I use PyTorch to build a model, I often feel at a loss as to how to add the data to the end of the sequence when processing the data.. The append() function which is quite handy to use in python list data, but we can use it in torch tensor.. I found a useful method on the Internet. It is use torch.cat() to add the data in the sequence.
04.05.2017 · Hi, Is there a way to declare output = [] in your code as torch.tensor and append in it similar to python numpy list? 1 Like. akib62 (Akib Rahman) October 9, 2020, 10:02pm #11. Hi @michaelklachko, I am trying to do something like that though I am not sure (I am new in this field) and trying to learn. I wrote something ...
04.07.2018 · Expand the dimensions on the scalar tensor that you want to append to make it rank 2. Use tf.tile to repeat the rows. Concatenate both tensors along the last axis. import tensorflow as tf a = tf.placeholder (tf.int32, shape= [None, 2]) c = tf.constant (5) [None, None] # Expand dims.
09.03.2017 · The easiest way to expand tensors with dummy dimensions is by inserting None into the axis you want to add. For example, say you have a feature vector with 16 elements. To add a dummy batch dimension, you should index the 0th axis with None: import torch x = torch.randn (16) x = x [None, :] x.shape # Expected result # torch.Size ( [1, 16]) The ...
24.01.2021 · I am trying to get a history of values of x with ‘for’ statement. For python floats, I get the expected list. The result is surprising with tensors: all entries of the list are the same! Please let me know why this happens with tensors. Thanks. — code — import torch # when x is a python float x_hist = [] x = 1.1 for i in range(3): x -= 0.1 x_hist.append(x) print(x_hist) # out [1.0, 0.9 ...
Mar 25, 2020 · If you know the resulting batch_* shape a priori, you can preallocate the final Tensor and simply assign each sample into their corresponding positions in the batch. It would be more memory efficient.
23.12.2020 · Your B tensor is zero dimesional, so you can’t use torch.cat() function to concatenate them.. 1)Concatenate them as python array and convert them to tensor 2)Add dimension with unsqueeze() method and use torch.cat()
Feb 21, 2021 · 1. I have a tensor matrix that i simply want to append a tensor vector as another column to it. For example: X = torch.randint (100, (100,5)) x1 = torch.from_numpy (np.array (range (0, 100))) I've tried torch.cat ( [x1, X) with various numbers for both axis and dim but it always says that the dimensions don't match. python pytorch.
20.02.2021 · 1. I have a tensor matrix that i simply want to append a tensor vector as another column to it. For example: X = torch.randint (100, (100,5)) x1 = torch.from_numpy (np.array (range (0, 100))) I've tried torch.cat ( [x1, X) with various numbers for both axis and dim but it always says that the dimensions don't match. python pytorch.
08.04.2020 · I have a tensor inps, which has a size of [64, 161, 1] and I have some new data d which has a size of [64, 161]. How can I add d to inps such that the new size is [64, 161, 2]?
Dec 23, 2020 · Your B tensor is zero dimesional, so you can’t use torch.cat() function to concatenate them.. 1)Concatenate them as python array and convert them to tensor 2)Add dimension with unsqueeze() method and use torch.cat()