TensorFlow Docker 이미지는 이미 TensorFlow를 실행하도록 구성되어 있습니다. Docker 컨테이너는 가상 환경에서 실행되며 GPU 지원을 설정하는 가장 쉬운 방법입니다. Docker pull tensorflow/tensorflow:latest-py3 # Download latest stable image docker run -it -p 8888:8888 tensorflow/tensorflow:latest-py3-jupyter # Start Jupyter server. Create a virtual environment in Python. Why we want a virtual environment? A virtual environment is. Jul 15, 2020.
Source: Deep Learning on Medium

Due its ease-of-use, efficiency, and cross-compatibility TensorFlow 2.0 is going to change the landscape of Deep Learning. Here we will learn installing and setting it up. We will also implement MNIST classification with TF 2.0.

TensorFlow 2.0 is going to change the landscape of Deep Learning. It has made,

  • model building simpler,
  • production deployment on any platform more robust, and
  • enables powerful experimentation for research.

With these, Deep Learning is going to become more mainstream in various areas in research and industry.

TF 2.0 has Keras API integrated in it. Keras is an extremely popular high-level API for building and training deep learning models. Before going forward it is important to know,

  • TF 1.x also supports Keras, but in 2.0 Keras is integrated tightly with the rest of the TensorFlow platform. 2.0 is providing a single high-level API to reduce confusion and enable advanced capabilities.
  • The Keras commonly used now is an independent open source project found at www.keras.io (June, 2019). However, Keras is an API spec that is now also available in TensorFlow (see [1] for details).

I recommend reading [1] and [2] to know more details on the benefits of TensorFlow 2.0. In summary, TF 2.0 has brought the ease-of-implementation along with immense computation efficiency, and compatibility with any platform, such as, Android, iOS and embedded systems like a Raspberry Pi and Edge TPUs.

Achieving these were difficult before and required investing time on finding alternate ways. As TensorFlow 2.0 has brought all of them, it is imperative to migrate to it sooner than later.

To that end, here we will learn installing and setting up TensorFlow 2.0.

Prerequisites

Option 1: Python (not through Anaconda)

a. Install Python 3.4+

Check your current versions.

or,

I have different Python on my Mac (Anaconda) and Ubuntu. The output I see on them are,

Either Python within Anaconda or otherwise will work.

If your version is not 3.4+, install it as follows.

b. Install virtualenv

virtualenv is required to create a virtual environment. Download old mac os image. Its requirement is explained in the next section.

Mac OS

Ubuntu

Note: pip (instead of pip3) is also used sometimes. If unsure between the two, use pip3. You will not go wrong with pip3. If you want to know whether you could use pip , run the following

In my system, the versions are the same for both pip and pip3. Therefore, I can use either of them.

Option 2: Python through Anaconda

Anaconda with Jupyter provides a simpler approach for installing Python and working on it.

Installing Anaconda is relatively straightforward. Follow this link with the latest Python3.x: https://jupyter.org/install

Similar to pip3 above, with Anaconda we have conda for creating virtual environments and installing packages. In the following, we will look at the installations steps with both.

Step 1. Create a virtual environment in Python.

Why we want a virtual environment?

A virtual environment is an isolated environment for Python projects. Inside a virtual environment we can have a completely independent set of packages (dependencies) and settings that will not conflict with anything in other virtual environment or with the default local Python environment.

This means we can keep different versions of the same package, e.g. we can use scikit-learn 0.1 for one project, and scikit-learn 0.22 for another project on the same system but in different virtual environments.

Instantiate a virtual environment

Ubuntu/Mac (Python without Anaconda)

Snipping tool download free mac. The above command will create a virtual environment tf_2. Understanding the command,

Conda on Ubuntu/Mac (Python from Anaconda)

If you are using Conda, you can create the virtual environment as,

Tensorflow Download Ans Setip Mac

The above command will also create a virtual environment tf_2. Unlike before, we do not require to install a different package for creating a virtual environment. The in-built conda command provides this.

Understanding the command,

Step 2. Activate the virtual environment.

Activate the virtual environment.

Ubuntu/Mac (Python without Anaconda)

Conda on Ubuntu/Mac (Python from Anaconda)

After the activation, the terminal will change to this (tf_2) $ .

Step 3. Install TensorFlow 2.0.

Before starting the TensorFlow installation, we will update pip.

Now, let’s install TensorFlow.

The tensorflow argument above installs a CPU-only version. This is recommended for beginners. You can change the argument to one of the following based on your requirement.

Note: we will use pip install for conda as well. TensorFlow is not available with conda.

Tensorflow Download Mac

Step 4. Test the installation.

To quickly test the installation through the terminal, use

The output will be (ignoring the system messages), https://yellowez642.weebly.com/download-seamonkey-for-mac-free.html.

Pay attention on the TensorFlow version output. If it is not the version you installed (2.0.0-beta0, in this case), then something went wrong. Most likely, there is a prior installed TensorFlow and/or the current installation failed.

TensorFlow 2.0 Example

We will test and learn the TensorFlow 2.0 with MNIST (fashion_mnist) image classification example. Imagemagick download mac os x.

Make sure the tf.__version__ outputs 2.x. If the version is older, check your installation or the virtual environment.

Tensorflow Download Ans Setup Macro

Download the fashion_mnist data from the tf open datasets and pre-process it.

To get familiarized with the data, we will plot a few examples from it.

Now we will build the model layer-by-layer.

Note that this model is only for demonstration and, therefore, trained on only five epochs.

We will now test the model accuracy on the test data.

We will visualize one of the predictions. We will use some UDFs from [3].

We will now get the prediction, i.e. the probability of each image belonging to each of the 10 classes, for the test images.

As we can see in the plot above, the prediction probability of ‘Ankle boot’ is the highest. To further confirm, we output the predicted label as,

Tensorflow Download Ans Setup Mac Os

Step 5. Deactivate the virtual environment

Tensorflow download mac

Before closing, we will deactivate the virtual environment.

Tensorflow Install On Mac

For virtualenv use,

For conda use,

Tensorflow Download Ans Setup Mac Download

Github repository

The GitHub repository with the MNIST example on TensorFlow 2.0 is here.

Conclusion

References