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.
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.
First, install https://www.docker.com/docker-toolbox
Since this is Windows, creating the Docker group "docker" is not necessary.
| #!/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 |
| 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() |
| # 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 |
| # 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 |
| # 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 |
| """ 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 | |
| """ |
| 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): |