benjaminalt

#!/bin/bash

# Define colors and styling
RED='\e[1;31m'
GREEN='\e[1;32m'
BLUE='\e[1;34m'
YELLOW='\e[1;33m'
CYAN='\e[1;36m'
PURPLE='\e[1;35m'
NC='\e[0m' # No Colora

benjaminalt

from xmlrpc.server import SimpleXMLRPCServer
from typing import Tuple

class RPCServer(SimpleXMLRPCServer):
    def __init__(self, addr: Tuple[str, int], **kwargs):
        super().__init__(addr, **kwargs)
        self.quit = False

    def serve_forever(self):
        self.quit = False

benjaminalt

def plot_grad_flow(named_parameters):
    '''Plots the gradients flowing through different layers in the net during training.
    Can be used for checking for possible gradient vanishing / exploding problems.
    
    Usage: Plug this function in Trainer class after loss.backwards() as 
    "plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow'''
    ave_grads = []
    max_grads= []
    layers = []

benjaminalt

import numpy as np
import matplotlib.pyplot as plt
import time
import multiprocessing as mp
from queue import Empty


class PlotterProcess(mp.Process):
    def __init__(self, queue, series_labels, shutdown_event, output_filename):
        super(PlotterProcess, self).__init__()

benjaminalt

import argparse
import multiprocessing
import os
import time
import random

import tables
import numpy as np
import pandas as pd
from tqdm import tqdm

benjaminalt

std::string indent(int level)
    {
        std::string s;
        for (int i = 0; i<level; i++) s += "  ";
        return s;
    }

void printTree(boost::property_tree::ptree &pt, int level)
{
    if (pt.empty())

benjaminalt

{"class_name": "Model", "keras_version": "2.0.6", "config": {"layers": [{"class_name": "InputLayer", "inbound_nodes": [], "config": {"dtype": "float32", "batch_input_shape": [null, 5, 3], "name": "input_1", "sparse": false}, "name": "input_1"}, {"class_name": "LSTM", "inbound_nodes": [[["input_1", 0, 0, {}]]], "config": {"recurrent_activation": "tanh", "trainable": true, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"seed": null, "gain": 1.0}}, "use_bias": true, "bias_regularizer": null, "return_state": false, "unroll": false, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "unit_forget_bias": true, "dropout": 0.0, "recurrent_dropout": 0.0, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "kernel_constraint": null, "activation": "tanh", "stateful": false, "activity_regularizer": null, "recurrent_regularizer": null, "name": "lstm_1", "bias_constraint": null, "go_backwards": f

benjaminalt

cmake_minimum_required(VERSION 2.8.11)

project(restfs_client)

set(CMAKE_INCLUDE_CURRENT_DIR ON)

include_directories(
  include
  usr/include/Poco/Net
)

benjaminalt

#include <cstdio>
#include <iostream>
#include <memory>
#include <stdexcept>
#include <string>

std::string exec(const char* cmd) {
    char buffer[128];
    std::string result = "";
    std::shared_ptr<FILE> pipe(popen(cmd, "r"), pclose);

benjaminalt

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Author: Benjamin Alt (benjamin_alt@outlook.com)

Calculate the character-level edit distance between a reference string and one or multiple hypotheses.
"""

from __future__ import print_function