| import os | |
| import sys | |
| import functools | |
| import logging | |
| from termcolor import colored | |
| from typing import Optional | |
| __all__ = ["setup_logger", "get_logger"] | |
| import numpy as np | |
| from collections import namedtuple | |
| topk_namedtuple = namedtuple('topk_namedtuple', ['values', 'indices']) | |
| def topk(array: np.ndarray, k: int, largest: bool = True) -> topk_namedtuple: | |
| """Returns the k largest/smallest elements and corresponding indices | |
| from an array-like input. |
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
| import numpy as np | |
| import torch | |
| import tensorflow as tf | |
| import numpy as np | |
| import torch | |
| import tensorflow as tf | |
| # Numpy | |
| np.random.seed(42) | |
| x = np.random.randn(10, 10) |
| def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor: | |
| '''Calculate F1 score. Can work with gpu tensors | |
| The original implmentation is written by Michal Haltuf on Kaggle. | |
| Returns | |
| ------- | |
| torch.Tensor | |
| `ndim` == 1. 0 <= val <= 1 | |
| import json | |
| import requests | |
| def translate(word): | |
| url = 'http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule&smartresult=ugc&sessionFrom=null' | |
| key = { | |
| 'type': "AUTO", | |
| 'i': word, | |
| "doctype": "json", | |
| "version": "2.1", |
| import torch | |
| import tensorflow as tf | |
| ########## PyTorch Version 1 ################ | |
| x = torch.randn(5, 6) | |
| norm_th = x/torch.norm(x, p=2, dim=1, keepdim=True) | |
| norm_th[torch.isnan(norm_th)] = 0 # to avoid nan | |
| ########## PyTorch Version 2 ################ | |
| norm_th = torch.nn.functional.normalize(x, p=2, dim=1) |
| import torch | |
| import tensorflow as tf | |
| def softmax_cross_entropy_with_logits(labels, logits, dim=-1): | |
| return (-labels * F.log_softmax(logits, dim=dim)).sum(dim=dim) | |
| logits = [[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]] | |
| labels = [[1.0, 0.0, 0.0], [0.0, 0.8, 0.2]] |