PetrochukM

"""
We implement additional hyperparameter optimization methods not present in
https://scikit-optimize.github.io/.

Gist: https://gist.github.com/Deepblue129/2c5fae9daf0529ed589018c6353c9f7b
"""

import math
import logging
import random

ilblackdragon

import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

teknikqa

// On the Last.FM website go to the page which lists the tracks that you have loved.
// Open Chrome DevTools (or Firefox or any modern browser that has a built in Javacript Console)
// and run the following command.
// This basically clicks on all the delete buttons on the page and reloads the page.
jQuery('.love-button--loved').each(function(_, b) {
  b.click();
});
location.reload();

noname01

from pydoc import locate
import tensorflow as tf
import numpy as np
from seq2seq import tasks, models
from seq2seq.training import utils as training_utils
from seq2seq.tasks.inference_task import InferenceTask, unbatch_dict


class DecodeOnce(InferenceTask):
  '''

spitis

"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
import tensorflow as tf, numpy as np
RNNCell = tf.nn.rnn_cell.RNNCell

class BNLSTMCell(RNNCell):
    '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
    def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
        """
        * max bn steps is the maximum number of steps for which to store separate population stats
        """

greydanus

"Dynamic plotting in matplotlib. Copy and paste into a Jupyter notebook."
# written October 2016 by Sam Greydanus
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import time

def plt_dynamic(x, y, ax, colors=['b']):
    for color in colors:
        ax.plot(x, y, color)

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

seominjoon

# On Ubuntu 14.04 with Titan X (compute capability 5.2)
# There are some missing parts that you have to specify, and some files you have to download manaully from web, so don't run this script file as it is.
# Configure CUDA paths
echo export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" >> ~/.bashrc
echo export CUDA_HOME="/usr/local/cuda" >> ~/.bashrc
source ~/.bashrc

# Set up java
# Dependencies for Bazel
# Download jdk 8

shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

inverse

// ==UserScript==
// @name         Last.fm Extra
// @namespace    https://malachisoord.com/
// @version      0.1.3
// @description  Provide missing extra functionality to Last.fm
// @author       Malachi Soord
// @match        http://www.last.fm/*
// @require      https://code.jquery.com/jquery-2.2.0.min.js
// @run-at       document-idle
// ==/UserScript==