If you are interested in technological and scientific topics involving computation, chances are that you have heard of Computational Mindset. It is an amazing website that focuses mainly on deep learning and neural networks. The website also shares information about quantum computing. When working on neural networks, Tensor Flow is the technology which needs to be used. The goal of this post is to discuss the use of Tensor Flow for the forecast of a univariate equally spaced time. One can access the article by clicking on this link. The purpose of this post will be presented here including the methodologies that will need to be used. Once you reach the end of this post, you will come across the summary. Without further ado, let’s begin this post.

Outline

This post covers the estimation of evenly distributed time series as well as a univariate with Tensor Flow using different network categorizations. Different network-style configurations including LSTM, Convolutional, Bidirectional LTSM, ConvLSTM, and other cascading combinations will be assessed by the users using the code. It would enable the users to clearly determine the functioning of the command prompt on programs that use Python. By doing so, they would be able to implement the characteristics as mentioned below.

● Generation of Dataset
● Network taxonomy description and configuration of hyper parameters
● The prognosis (Prediction)
● Generating of a dispersion graph that explicitly shows the findings
● Generation of a video involving the network learning process
● The Diagnosis

In order to list the code, Python version 3 is required. In addition to this, Keras is also used as it is embedded into Tensor Flow 2 and contains libraries of MatPlotLib, Pandas, ImageIO, and NumPy.

 

Features

The following functions are discussed in the post.

1. Generate Dataset

To effectively generate the datasets, the steps mentioned below will be used on the uvests_gen.py Python program.

• The program will use the generator function in the time series from the syntax lambda body in the command line based on the independent variable (t).
• The step involving discretization will begin and finish from the independent variable period.
• A dataset in CSV would be constructed by implementing the features from the prior interval.

2. Configure Hyper Parameters and Network Taxonomy Definition

The uvests_gen.py Python program mainly deals with the building of a continuous neural network and performing the training as set by the parameters. After the training sequences are created on the uvests_gen.py Python program, a neural network model would be built through the passing of the taxonomy command line arguments such as by going through the required types of layers including Conv, Dense, LTSM, and the like.

3. Prediction (Prognosis)

Once you have passed the above steps, you will move on to prediction. The determination of the time series forecast is dealt with by the uvests_gen.py Python program which has learned through training. After the uvests_gen.py Python program has been used to create the model, the forecast will then be calculated and compared to the sample time series. It would help ensure that the uncertainty value between the series is calculated.

4. Generate a Dispersion Graph of the Findings

The test series are viewed geographically by the uvests_gen.py Python program as well as the forecast series and training series. The forecast would be finally determined after the model is created with the uvests_gen.py Python program. The expected prediction along with the time series would be contrasted for the calculation of the error value in the series.

5. Generate a Video Using Network Learning Process

Next, a video will be created by the uvests_gen.py Python program which will depict the forecast of the training process and will showcase the changing epochs. Now, to make the video, the modelsnapout, the arguments, and the modelsnapfreq would need to be transferred through the fc_uvestspredict.py command.

Firstly, the fc_uvestspredict.py function must be executed. It has to be in accordance with the modelsnapout and modelsnapfreq parameters. Then, an animated gif will appear towards the end of the video in the animated gif file . A series of frames would be displayed in it and each frame will showcase the forecast graph for determining the model that would be accessible for the nth interval.

 

Which Methodologies Are Used?

When it comes to conducting the diagnosis, you have four testing methodologies available as mentioned below.

• Written user feedback is the first testing methodology as you must know. It concerns the standard performance along with the generic error streams as found in different programs.
• Moving on to the second methodology, it involves video generation as already mentioned above. You will be able to keep track of the neural net learning process through each epoch change with the video.
• As for the third methodology, it is Tensor Board. The argument that is used is Logsout. It helps define the program’s repository of the log data which has been written by the Tensor Board analysis before and by the end of the training process. The fc_uvestspredict.py program would once again be used to run it.
• At last, you will use the fourth method which involves the use of fc_uvestspredict.py function to inspect the parameters from the metrics argument specification. All the metric values as measured at each interval will then by depicted on the standard output and loss function values. It is vital to bear in mind that the loss function values along with the parameters will be stored on the CSV files. The files are located in the folder where the directory path as specified as the dumpout argument is found. All you will need to do is use the fc_uvestspredict.py program once again for running the dumpout argument to ensure that it has passed from the dump files. Finally, you need to run the command nn_dumps_scatter.py.
• The savefirdir directory will contain all the images which deal with each epoch change. A graph of metrics will be displayed which have been selected including the loss function.

 

Summary

Once you have gone over this post, you will know how to use Tensor Flow for the forecast of a univariate equally spaced time series. Remember, the results of each function are available the site.