kouroshHakha

import datasets
import re
import json
import tqdm

ds = datasets.load_dataset("glaiveai/glaive-function-calling-v2", split="train")

out_ds_size = 100

class UserAssistantNotFoundError(Exception):

mvoelk

# -*- coding: utf-8 -*-

import cv2
import numpy as np

from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, AvgPool2D, Activation
from tensorflow.keras.layers import Layer, BatchNormalization, ZeroPadding2D, Flatten, add
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.models import Model
from tensorflow.keras import initializers

tam17aki

# -*- coding: utf-8 -*-

# Copyright (C) 2017 by Akira TAMAMORI

# Copyright (C) 2016 by hardmaru

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

flyyufelix

ResNet-152 in Keras

This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.

ResNet Paper:

Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385

m00nlight

class KMP:
    def partial(self, pattern):
        """ Calculate partial match table: String -> [Int]"""
        ret = [0]
        
        for i in range(1, len(pattern)):
            j = ret[i - 1]
            while j > 0 and pattern[j] != pattern[i]:
                j = ret[j - 1]
            ret.append(j + 1 if pattern[j] == pattern[i] else j)

leehsueh

"""
Grammar:
========
Expression --> AndTerm { OR AndTerm}+
AndTerm --> Condition { AND Condition}+
Condition --> Terminal (>,<,>=,<=,==) Terminal | (Expression)
Terminal --> Number or String or Variable

Usage:
======