Computation has become a hot topic in recent times. Fans of technology and science that involves computation will find computational mindset to be the perfect site. It takes a close look at deep learning and neural networks. Visitors to the Computational Mindset site will come across information on quantum computing and much more. Tensor Flow is used when dealing with neural networks. This post aims to discuss Tensor Flow usage for the forecasting of a univariate equally spaced time. This link will provide you with access to the article. You need to read on to learn more about the purpose of this post and the methodologies that can be used. After you have gone over this post, you will find the summary. So, let’s get started.

Outline

As you must have already gotten an idea by now, this post deals with evenly distributed time series estimation and univariate estimation using Tensor Flow, while, trying different network categorizations. Some of network-style configurations include Bidirectional LTSM, Convolutional, ConvLSTM, LSTM, and different cascading combinations which help assess the code used by users. Thus, doing do would allow one to discover the command prompt functioning on programs that are Python-based. This will help ensure that the following characteristics are implemented.

• Dataset Generation
• Hyper Parameters Configuration and Definition of Network Taxonomy
• Prognosis/ Prediction
• Dispersion Graph Generation Depicting the Findings Clearly
• Video Generation using the Network Learning Process
• The Diagnosis

Python version 3 has to be used for the code listing. Besides this, Keras embedded into Tensor Flow 2 is used as it consists of libraries such as NumPy, Pandas, ImageIO, and MatPlotLib.

Main Features

The post discussed the following features.

1. Dataset Generation

The uvests_gen.py Python program is used for generating the datasets. The following steps will help you understand the process.

• The generator function of the time series will be used by the program from the command line syntax lambda body based on t (independent variable).
• Next, the discretization step will start and end relying on the independent variable period.
• The dataset would be constructed in CSV through the implementation of features of the previous interval.

2. Hyper Parameters Configuration and Definition of Network Taxonomy

The creation of a continuous neural network is done with the uvests_gen.py Python program as well as the training based on the parameters. Once the uvests_gen.py Python program has created the training sequences, a neural network model will be developed by passing the taxonomy command line (arguments) by using different types of layers like LTSM, Conv, Dense, and others.

3. Prognosis/ Prediction

Next, after completing the above steps, the prediction will be made. The uvests_gen.py Python program works on the determination of the time series forecast that it has learned through the training. Once the model has been developed with the uvests_gen.py Python program, then, the forecast would calculate and compare the sample time series. Thus, the uncertainty value of the series would be calculated.

4. Dispersion Graph Generation Depicting the Findings Clearly

Now, the uvests_gen.py Python program would view the test series geographically along with the training series and forecast series. After this, the forecast will be determined at last once the model has been created using the uvests_gen.py Python program. Finally, the expected prediction and the time series will be differentiated for calculating the error value of the series.

5. Video Generation using the Network Learning Process

After completing the above steps, a video will be made with the help of the uvests_gen.py Python program. It will showcase the training process forecast and the changing epochs. In order to create the video, the modelsnapout and the modelsnapfreq arguments must be transferred with the fc_uvestspredict.py command.

What needs to be done first is that the fc_uvestspredict.py function will be performed according to the parameters of the modelsnapfreq and modelsnapout. Then the function is performed. This would lead to an animated gift appearing at the ending of the video as found in the animated gif file. The forecast graph will be showcased by each frame in the series of frames that are depicted for distinguishing the model to be accessible through the nth interval.

Which Methodologies Would Be Used?

When working on the solution, there are four different testing methodologies that are available. These are clearly defined below for your information.

• To get started, the first testing methodology that is used is written user feedback. It primarily focuses on the standard performance and generic error streams that occur in various programs.
• Next, the second method that is used deals with video generation. It is used to monitor the neural net learning process as each epoch changes in the video.
• The third method that is used is Tensor Board. The logsout is where the argument is used. It is used for defining the repository log data which is written using the Tensor Board analysis throughout the training process. Once again the fc_uvestspredict.py program would need to be used for running it.
• Towards the end, the fourth method will be used. It involves fc_uvestspredict.py function usage for inspecting the parameters from the specifications of the metrics argument. Then, the metric values measured during each interval would be shown depicting the standard output as well as the loss function values. Now, you should know that the loss function values and the parameters get stored in the CSV file which can be found in a folder along with the directory path as set by the dumpout argument. Use the fc_uvestspredict.py program to run the dumpout argument to check if it has successfully passed dump files. After this, you have to run the nn_dumps_scatter.py command.
• The images are contained in the savefirdir directory. They show each and every epoch change. Keep in mind that a graph of metrics would be shown depicting the selected loss function.

Summary

Now that you are aware of using Tensor Flow for the forecast of a univariate equally spaced time, you can get to work. Make sure to check the results of every function on the site.