I would like to generate a Lookup table in TensorFlow with key is string and value is list of strings. But it seems currently no classes in tf.lookup support th
Does PyTorch's nn.Embedding support manually setting the embedding weights for only specific values? I know I could set the weights of the entire embedding laye
My Code : h_table = tf.lookup.StaticHashTable( initializer=tf.lookup.KeyValueTensorInitializer( keys=[0, 1, 2, 3, 4, 5], values=[12.3,
Suppose, I have a 3D tensor A A = torch.arange(24).view(4, 3, 2) print(A) and require masking it using 2D tensor mask = torch.zeros((4, 3), dtype=torch.int6
I wish to create a custom pooling layer which can efficiently work on GPUs. For instance, I have following input tensor in = <tf.Tensor: shape=(4, 5), dtype=
I've been trying to generate a custom dataset from two arrays. One with the shape (128,128,6) (satellite data with 6 channels), and the other with the shape (12
I have a output tensor after convolution of dimensions [1,64,112,112]. Is there any way I can visualize this using matplotlib only, keeping in mind that imshow(
I am trying to initialize a tensor on Google Colab with GPU enabled. device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') t = torch.tensor([1,
I have a list of pytorch tensors as shown below: data = [[tensor([0, 0, 0]), tensor([1, 2, 3])], [tensor([0, 0, 0]), tensor([4, 5, 6])]] Now this is ju
Have tensor like :x.shape = [3, 2, 2]. import torch x = torch.tensor([ [[-0.3000, -0.2926],[-0.2705, -0.2632]], [[-0.1821, -0.1747],[-0.1526, -0.1453]
I would like to process text with tensorflow 2.8 on Jupyter notebook. my code: import re import string import tensorflow as tf from tensorflow import keras from
I'm trying to build a simple word generator. However, I encounter some difficulty with the sliding windows. here is my actual code: files = glob("transfdata/*")
I'm working with certian tensors with shape of (X,42) while X can be in a range between 50 to 70. I want to pad each tensor that I get until it reaches a size o
I have a PyTorch tensor of size (5, 1, 44, 44) (batch, channel, height, width), and I want to 'resize' it to (5, 1, 224, 224) How can I do that? What functions
I have these 2 tensors box_a = torch.randn(1,4) box_b = torch.randn(1,4) and i have a code in pytorch box_a[:, 2:].unsqueeze(1).expand(1, 1, 2) but i want to
Any efficient way to merge one tensor to another in Pytorch, but on specific indexes. Here is my full problem. I have a list of indexes of a tensor in below cod