The models are developed in Python [46], using the Keras [47] and Tensorflow [48] libraries. Detailson the code and dependencies to run the experiments are listed in a Readme file available togetherwith the code in the Supplemental Material.

Here is an example run of the QnA model:

"dividing n-dimensional space with a hyperplane."

This dataset can not be classified by a single neuron, as the two groups of data points can't be divided by a single line.

TensorFlow.js provides theLayers API,which mirrors the Keras API as closely as possible, in-cluding the serialization format.

The key libraries of TFX are as follows

When we are providing our API endpoint to frontend team we need to ensure that we don’t overwhelm them with preprocessing technicalities.We might not always have a Python backend server (eg. Node.js server) so using numpy and keras libraries, for preprocessing, might be a pain.If we are planning to serve multiple models then we will have to create multiple TensorFlow Serving servers and will have to add new URLs to our frontend code. But our Flask server would keep the domain URL same and we only need to add a new route (a function).Providing subscription-based access, exception handling and other tasks can be carried out in the Flask app.

TFX and Tensorflow run anywhere Python runs, and that’s a lot of places

since TFX and Tensorflow were built by Google, it has first-class support in the Google Cloud Platform.

After consideration, you decide to use Python as your programming language, Tensorflow for model building because you will be working with a large dataset that includes images, and Tensorflow Extended (TFX), an open-source tool released and used internally at Google, for building your pipelines.

These components has built-in support for ML modeling, training, serving, and even managing deployments to different targets.

Most data scientists feel that model deployment is a software engineering task and should be handled by software engineers because the required skills are more closely aligned with their day-to-day work. While this is somewhat true, data scientists who learn these skills will have an advantage, especially in lean organizations. Tools like TFX, Mlflow, Kubeflow can simplify the whole process of model deployment, and data scientists can (and should) quickly learn and use them. 

TFX Component called TensorFlow Model Analysis (TFMA) allows you to easily evaluate new models against current ones before deployment. 

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.6.

NVIDIA's CUDA libraries

TensorFlow provides optimizers that slowly change each variable in order to minimize the loss function. The simplest optimizer is gradient descent. It modifies each variable according to the magnitude of the derivative of loss with respect to that variable

optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
sess.run(init)  # reset values to incorrect defaults.
for i in range(1000):
    sess.run(
        train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})
print("...", sess.run([W, b]))

这张图给出了谷歌在2015年提出的Inception-v3模型。这个模型在ImageNet数据集上可以达到95%的正确率。然而，这个模型中有2500万个参数，分类一张图片需要50亿次加法或者乘法运算。

J(t)NEG=logQθ(D=1|the, quick)+log(Qθ(D=0|sheep, quick))

If we write that out as equations, we get:

If you would like TensorFlow to automatically choose an existing and supported device to run the operations in case the specified one doesn't exist, you can set allow_soft_placement to True in the configuration option when creating the session.

Google is merely interested sharing the code. As Dean says, this will help the company improve this code. But at the same time, says Monga, it will also help improve machine learning as a whole, breeding all sorts of new ideas. And, well, these too will find their way back into Google. “Any advances in machine learning,” he says, “will be advances for us as well.”