sindresorhus

Pure ESM package

The package that linked you here is now pure ESM. It cannot be require()'d from CommonJS.

This means you have the following choices:

1. Use ESM yourself. (preferred)
   Use import foo from 'foo' instead of const foo = require('foo') to import the package. You also need to put "type": "module" in your package.json and more. Follow the below guide.
2. If the package is used in an async context, you could use await import(…) from CommonJS instead of require(…).
3. Stay on the existing version of the package until you can move to ESM.

keunwoochoi

import keras
from keras import backend as K
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers import Input, Dense, Activation
from keras.layers import concatenate  # functional interface
from keras.models import Model
from keras.layers.advanced_activations import LeakyReLU

N_INPUT = 512

mrry

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

PatWie

#!/bin/bash
# Patrick Wieschollek

# =============================================================
# UPDATE SOURCE
# =============================================================
git checkout -- .
git pull origin master

soumith

import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Container):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

akiross

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

nzw0301

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. ast2vec https://arxiv.org/abs/2103.11614
5. attribute2vec https://arxiv.org/abs/2004.01375
6. author2vec http://dl.acm.org/citation.cfm?id=2889382
7. baller2vec https://arxiv.org/abs/2102.03291
8. bb2vec https://arxiv.org/abs/1809.09621

kurubushi--rm

# coding: utf-8

# # MonkeyBench : an interactive environment for library research
# 
# 
# MonkeyBenchは、Jupyterの上に構築中のライブラリーリサーチのための対話環境です。
# 
# ライブラリーリサーチの過程と成果を、そのためのツールの作成と利用込みで、そのまま保存／共有／再利用できるという利点を活かし、調査プロセスの振り返りから質の向上、ノウハウ化を支援する環境となることを目論んでいます。
# 

EderSantana

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures