FloydHsiu

from tensorflow.python.eager import def_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras import backend as K
from tensorflow.python.keras import layers
from tensorflow.python.keras import initializers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops

class SpectralNormalization(layers.Wrapper):

illume

""" Shows how to use flask and matplotlib together.

Shows SVG, and png.
The SVG is easier to style with CSS, and hook JS events to in browser.

python3 -m venv venv
. ./venv/bin/activate
pip install flask matplotlib
python flask_matplotlib.py
"""

bogdan-kulynych

# WARNING: These steps seem to not work anymore!

#!/bin/bash

# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

erenon

# This example shows how to use keras TensorBoard callback
# with model.train_on_batch

import tensorflow.keras as keras

# Setup the model
model = keras.models.Sequential()
model.add(...) # Add your layers
model.compile(...) # Compile as usual

danijar

# Full example for my blog post at:
# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

tfd = tf.contrib.distributions

myurasov

joelthchao

import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()

saibotsivad

#!/bin/bash

wget http://link.springer.com/content/pdf/10.1007/978-1-4757-1779-2.pdf
wget http://link.springer.com/content/pdf/10.1007/978-1-4757-2103-4.pdf
wget http://link.springer.com/content/pdf/10.1007/978-1-4684-9884-4.pdf
wget http://link.springer.com/content/pdf/10.1007/978-3-662-02945-9.pdf
wget http://link.springer.com/content/pdf/10.1007/978-1-4612-9923-3.pdf
wget http://link.springer.com/content/pdf/10.1007/978-1-4757-3828-5.pdf
wget http://link.springer.com/content/pdf/10.1007/978-1-4684-9936-0.pdf
wget http://link.springer.com/content/pdf/10.1007/978-1-4419-8566-8.pdf

ericjang

TensorFlow development environment on Windows using Docker

Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.

Installation

First, install https://www.docker.com/docker-toolbox

Since this is Windows, creating the Docker group "docker" is not necessary.

MarcusDoubleYou

Supply Chain Management

Introduction

Globalization and outsourcing were the main drivers for increasing the complexity of supply chains. At the same time, natural catastrophes as well as economic, social and ethical aspects drove the importance of having a good overview and understanding of the entire supply chain rather than just focusing on your direct suppliers and distributors.

In order to model and understand supply chains better, more and more sources refer to modern supply chains as supply networks.