TrentBrick

""" 
PyTorch has pack_padded_sequence this doesn’t work with dense layers. For sequence data with high variance in its length 
the best way to minimize padding and masking within a batch is by feeding in data that is already grouped by sequence length 
(while still shuffling it somewhat). Here is my current solution in numpy. 
I will need to convert every function over to torch to allow it to run on the GPU and am sure there are many other 
ways to optimize it further. Hope this helps others and that maybe it can become a new PyTorch Batch Sampler someday.

General approach to how it works:

Decide what your bucket boundaries for the data are.

yoavg

NLP Reading

Books

• Deep Learning for NLP: Book by Yoav Goldberg, and a Primer version (without the NLP bits, without some of the advanced bits)

• Manning and Schutze Foundations of Statistical Natural Language Processing. Buy at Amazon

  • Classic book, a bit outdates by now, but some chapters are still worth reading today.

• Jurafsky and Martin Speech and Language Processing (3rd Edition)

Kajiyu

#!/usr/bin/env python3
# -*- coding: utf-8 -*-


"""
Most of this code is borrowed by niffler92's project.
https://github.com/niffler92/SNGAN
"""

jeasinema

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

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

juliensimon

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = "0"
set_session(tf.Session(config=config))

infinite-Joy

ximeg

#!/usr/bin/env python
# Implementation of algorithm from http://stackoverflow.com/a/22640362/6029703
import numpy as np
import pylab

def thresholding_algo(y, lag, threshold, influence):
    signals = np.zeros(len(y))
    filteredY = np.array(y)
    avgFilter = [0]*len(y)
    stdFilter = [0]*len(y)

roopalgarg

import tensorflow as tf
import numpy as np


def gaussian_noise_layer(input_layer, std):
    noise = tf.random_normal(shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32) 
    return input_layer + noise


inp = tf.placeholder(tf.float32, shape=[None, 8], name='input')

sono-bfio

Nvidia Repo Setup

NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/7fa2af80.pub && \
apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +2 > cudasign.pub && \
echo "$NVIDIA_GPGKEY_SUM  cudasign.pub" | sha256sum -c --strict - && rm cudasign.pub && \
echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64 /" > /etc/apt/sources.list.d/cuda.list