| import torch | |
| from torch import nn | |
| from torch.autograd import Variable | |
| import torch.nn.functional as F | |
| import torch.optim as optim | |
| # toy feed-forward net | |
| class Net(nn.Module): | |
| def __init__(self): |
| import torch | |
| from torch import LongTensor | |
| from torch.nn import Embedding, LSTM | |
| from torch.autograd import Variable | |
| from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
| ## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium'] | |
| # | |
| # Step 1: Construct Vocabulary | |
| # Step 2: Load indexed data (list of instances, where each instance is list of character indices) |
This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.
| # Working example for my blog post at: | |
| # https://danijar.github.io/structuring-your-tensorflow-models | |
| import functools | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| def doublewrap(function): | |
| """ | |
| A decorator decorator, allowing to use the decorator to be used without |
