briancroom

I am trying to determine if it is possible to build a Swift dynamic library which is itself composed of one of more private modules, without needing to expose to that fact to outside users. My hope was that I could build the private module as a static library, which would be linked into the primary (dynamic) library. The dylib could then be deployed together with its swiftmodule and swiftdoc and be imported, with the private module and its symbols not being exposed at all.

Unfortunately, what I'm currently observing seems to indicate that the private module's swiftmodule also has to be available for the primary library to be successfully imported.

This can be reproduced as follows. I have the following directory structure:

./Greeter/Logger/Logger.swift:

public func log(_ message: String) {

IevaZarina

# -*- coding: utf-8 -*-
# Ieva Zarina, 2016, licensed under the Apache 2.0 licnece

import re

def is_digit(word):
    try:
        int(word)
        return True
    except ValueError:

ericjang

def sample_gumbel(shape, eps=1e-20): 
  """Sample from Gumbel(0, 1)"""
  U = tf.random_uniform(shape,minval=0,maxval=1)
  return -tf.log(-tf.log(U + eps) + eps)

def gumbel_softmax_sample(logits, temperature): 
  """ Draw a sample from the Gumbel-Softmax distribution"""
  y = logits + sample_gumbel(tf.shape(logits))
  return tf.nn.softmax( y / temperature)

bjinwright

import boto3
import datetime
import json
from requests_aws4auth import AWS4Auth
import requests

boto3.setup_default_session(region_name='us-east-1')
identity = boto3.client('cognito-identity', region_name='us-east-1')

account_id='XXXXXXXXXXXXXXX'

ChristopherA

Uninstall brew package and dependencies

Remove package's dependencies (does not remove package):

brew deps [FORMULA] | xargs brew remove --ignore-dependencies

Remove package:

kharrison

import Foundation

struct Country {
    let name: String
    let capital: String
    var visited: Bool
}

extension Country: Equatable {
    static func == (lhs: Country, rhs: Country) -> Bool {

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

rudolfbyker

#!/usr/bin/env python

from scipy.io import wavfile
import os
import numpy as np
import argparse
from tqdm import tqdm

# Utility functions

mjdietzx

from keras import layers
from keras import models
import tensorflow as tf


#
# generator input params
#

rand_dim = (1, 1, 2048)  # dimension of the generator's input tensor (gaussian noise)

mjdietzx

import torch
from torch.autograd import Variable
import torch.nn as nn


class Bottleneck(nn.Module):
    cardinality = 32  # the size of the set of transformations

    def __init__(self, nb_channels_in, nb_channels, nb_channels_out, stride=1):
        super().__init__()