The size of the hidden layer is 512 and the number of layers is 3. Improving Students’ Daily Life Stress Forecasting using LSTM Neural Networks Terumi Umematsu*y, Akane Sanoz*, Sara Taylor*, Rosalind W. Picard* *Massachusetts Institute of Technology, Media Lab 75 Amherst Street, Cambridge, MA, USA, 02139 fterumi,sataylor,picardg@media.mit.edu, akane.sano@rice.edu Stock price data have the characteristics of time series. Typically more improves model performance unless overfitting occurs at which time the validation accuracy/loss will not improve; Taking this in, we can come up with a plan. we can improve the stress forecasting accuracy using non-personalized temporal machine learning models. At the first pass model will learn some statistics and at the second pass make an actual correction. The two sub-networks, respectively, make predictions on inputs at different time intervals to capture the heterogeneity of rainfall data. How to improve the accuracy of LSTM network Conclusion. [CrossRef] 25. GitHub - Tarik4Rashid4/lstm-hs: Using Accuracy Measure for ... I'm looking for any ideas to improve the model. One-to-One:Where there is one input and one output. Training accuracy only changes from 1st to 2nd epoch and then it stays at 0.3949. Epoch 3/100 391/390 [=====] - ETA: 0s - loss: 1.1611 - accuracy: 0.5838 Epoch 00003: val_accuracy improved from 0.49350 to 0.51020, saving model to LSTM/weights-03-0.5102.h5 390/390 [=====] - 182s 466ms/step - loss: 1.1611 - accuracy: 0.5838 - val_lo ss: 1.5844 - val_accuracy: 0.5102 - lr: 0.0100 Epoch 00004: LearningRateScheduler setting learning rate to … How do I increase accuracy with Keras using LSTM via IEEE BHI. But, my test accuracy starts to fluctuate wildly. Text classification is a representative research topic in the field of natural-language processing that categorizes … Conclusion. The Keras Python deep learning library supports both stateful and stateless Long Short-Term Memory (LSTM) networks. LSTM The LSTM_RBF_ZTD of 62 stations is more accurate than GPT3_ZTD, with the maximum improvement reaching 76.3%. The reason for this behavior is that this fixed input length allows for the creation of fixe… The above code is running. The problem is that training accuracy is increasing while validation accuracy is almost constant. LSTM outperforms the other models when we want our model to learn from long term dependencies. BUT I am not able to get the accuracy above 50 percent. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. python - Low accuracy of LSTM model tensorflow - Code ... The first on the input sequence as-is and the second on a reversed copy of the input sequence. Predictions. None yet 5 … They can predict an arbitrary number of steps into the future. Could be a simple error you did in the code (maybe while extracting the dataset) that we can't see in your code sample. The constant loss you showe... LSTM In this paper, a new hybrid prediction model based on LSTM neural network and GPR is proposed, called LSTM-GPR model, which adopts the idea of two-step prediction. In this study, the performance of the LSTM method demonstrates the capability to improve the short-term prediction accuracy model for algal blooms. LSTM Cell with differently-drawn input gate. Solutions to this are t… Long Short-Term Memory models are extremely powerful time-series models. Back to overview. thank you sir for these awesome tutorials,it have been a great help me to me…. One of the most challenging tasks in time-series prediction is a model’s capability to accurately learn the repeating granular trends in the data’s structure to generate effective predictions. Ask Question Asked 1 year, 6 months ago. During training, the training loss keeps decreasing and training accuracy keeps increasing slowly. The other is to combine LSTM model with other models, which typically includes the CNN-LSTM model 22 and the SVM-LSTM model 23, to improve the prediction accuracy of LSTM model. Diagnostic of 500 Epochs. Therefore, an Attention-LSTM method is proposed to improve the evaluation and prediction of the performance degradation of … However, the degradation stage division of the rolling bearing performance is not obvious in traditional methods, and the prediction accuracy is low. $\begingroup$ The output is based on your last dense layer's neuron, and not the LSTM's neuron. A Hybrid CNN-LSTM Model for Improving Accuracy of Movie. But, if still it doesn't changes anything, then have a look here. I am working on forecasting time-series data using LSTM network, but the forecasting of the test data seems very inaccurate. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short … There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. The complete code listing for this diagnostic is listed below. nlp. Typical example of a one-to-one sequence problems is the case where you have an image and you want to predict a single label for the image. In our recent work, we proposed a layer trajectory long short-term memory (ltLSTM) model which decouples the tasks of temporal modeling and senone classification with time-LSTMs and depth-LSTMs. We can learn on flight while making corrections, or we can make a two-passes correction. The issue is that my validation accuracy stagnate around 35%. Then we propose a new method called Multi-Order BiLSTM (MO-BiLSTM) which combines low order and high order LSTMs together. The first one is a Long Short Term Memory (LSTM) type recurrent neural network (RNN) and the second one is a method based on sparse regression, where in both methods the pose data obtained from a laser tracker is used as the ground truth. References and other useful resources: According to Table 1, it can be concluded that, from the results of the 15-minute prediction window, compared with the traditional models, HA, ARIMA, and SVR, the RMSE value of the AST-GCN-LSTM model decreases by 3.07%, 44.43%, and 2.95%.Compared with the HA model and the SVR model, the accuracy value is increased by 14.69% and 1.56%, respectively We can filter the specific businesses like restaurants and then use LSTM for sentiment analysis. We can use much larger dataset with more epochs to increase the accuracy. More hidden dense layers can be used to improve the accuracy. We can tune other hyper parameters as well. With Softmax as activation in final layer, you should have n neurons, where n is the number of classes. Hi, I know this problem have been addressed many times but I cannot find any answers so I’m trying again. Possibly some of that applies directly to your case. 2019, 78, 26597–26613. I wrote this yesterday and it took some 10s per epoch, but then I edited the code in places - I was running on the colab and forgot to save a copy- and now it gives the same accuracy but the first epoch takes around 60s and each epoch takes ~45s. An LSTM module (or cell) has 5 essential components which allows it to model both long-term and short-term data. Accurate short-term motion attitude prediction of large ships plays an essential role in decision making related to ship-borne maritime operations. 1. The model takes a few hours to train, but once complete, has not been able to pass the 50% accuracy marker. The model will use a batch size of 4, and a single neuron. This oscillation means that the training accuracy is not improving and the training loss is not decreasing. Energy consumption prediction is a popular research field in computational intelligence. Amazon Musical Instruments Reviews . LSTM & Machine Learning models (89% accuracy) ... Notebook contains abusive content that is not suitable for this platform. So the output shape of the final dense layer will be affected by the number of neuron / … With both Conv LSTM and UNet LSTM performing pretty well on the validation data, we can combine their softmax outputs by taking the average. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. You can add regularizers and/or dropout to decrease the learning capacity of your model. Diagnostic of 500 Epochs. ... Long Short Term Memory or LSTM is used and well structured for resolving the above concerns. Add more lstm layers and increase no of epochs or batch size see the accuracy results. Originally, I was working with only 27 training signals and 27 testing signals and was getting an accuracy of only 62% and I thought that maybe I just didn't have enough data but after adding more data, my training accuracy went down. accelerator GPU makes the model not only high accuracy, but also faster. More hidden dense layers can be used to improve the accuracy. may some adding more epochs also leads to overfitting the model ,due to this testing accuracy will be decreased. Later I've found that this issue is usually the case when there are too few (e.g. I have tried changing the learning rate, reduce the number of layers. It is not constrained to predict between the interval [0, 1]. Many-to-One:In many-to-one sequence problems, we have a sequence of data as input and we have to predict a single output. I am using a bi-directional encoder-decoder RNN with an attention mechanism. When using stateful LSTM networks, we have fine-grained control over when the internal state of the LSTM network is reset. 7 comments Comments. A Long Short-Term Memory (LSTM) model is a powerful type of recurrent neural network (RNN). LSTM & Machine Learning models (89% accuracy) | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from Amazon Musical Instruments Reviews Traditionally specially tuned statistical models and deep learning models like recurrent neural networks and long short-term memory networks are used to tackle such problem of … To this end, an LSTM neural network based on transfer learning for stress prediction is proposed. https://developer... 1. improving accuracy of logistic model. In particular: 1. Try to normalize every feature dimension into [-1, 1] or [0, 1]. LSTM’s ability to forget, remember and update the information pushes it one step ahead of RNNs. CNN with LSTM provides a better test accuracy as compared to LSTM with approximately same weights and lesser training time. The main purpose of the current study is to improve the current accuracy rate using pre-trained word embeddings. The ltLSTM model achieved significant accuracy improve-ment over the traditional multi-layer LSTM models from our previ-ous study. I have been getting poor results on my time series predictions with a LSTM network. Validation accuracy is same throughout the training. In results, the mean prediction accuracy (F1) was 68.6% and 55.2% for the Chinese and the American stock markets, respectively. Two Questions: (1) Since the loss is decreasing, why is the accuracy not improving beyond 50%? 4, the high accuracy of the TF–TS LSTM training could be reached while the training of the LSTM with raw time series could not improve much in accuracy. After using the attention in the model we increased the accuracy to 99% and also the loss has decreased to 0.0285. We will explore the effect of training this configuration for different numbers of training epochs. Results show that … The True Data is smooth zig zag shaped, from 0 … There're couple of options to increase the accuracy: 1) Increase the hidden layers in the LSTM node. and/or 2) add another layer of the LSTM. Only 1 hidden layer may not be sufficient for the training of your data. After making changes in the model as above, you will probably see the stabilization of the accuracy in some range. The accuracy rate was shown to have improved by between 5% and 7% on almost all models using the presented method. Assignees No one assigned Labels None yet Projects None yet Milestone No milestone Linked pull requests Successfully merging a pull request may close this issue. This immediately increases the validation accuracy to 0.765! So I increased the learning rate and loss started around 5.1 and then dropped of to 0.02 after the 6th Epoch. Here’s the plot of the Backtested Keras Stateful LSTM Model. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. joelthchao commented on May 19, 2017. ... analyze web traffic, and improve your experience on the site. If your data is not in a large scale, I will suggest you to use xgboost model. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and … Got it. Active 1 year, ... eventhough increasing the num_epochs my validation accuracy is not changing \$\endgroup\$ – YogeshKumar. The output which I'm getting : The 4-layer CNN-LSTM network could improve the accuracy by up to 2.24%. Accuracy still stayed around 0.5 but loss started pretty low (0.01). We will explore the effect of training this configuration for different numbers of training epochs. Reviews Sentiment Analysis. Also accuracy not improving after few epochs….. please guide me sir . The accuracy of LSTM_RBF_ZTD is slightly inferior to GPT3_ZTD at three stations located in East Antarctica with few GNSS stations. In a class-imbalanced dataset, great accuracy does not always imply a great model.For example, snow falls approximately 24 times per century in a certain subtropical city. The train accuracy and loss monotonically increase and decrease respectively. Welcome to PR the works (papers, repositories) that are missed by the repo. But the validation loss started increasing while the validation accuracy is not improved. Here is an explanation: Some how through many hit and run trails, I was able to get the below running code (sentiment_demo_lstm.py) : len (train_x)= 9596, len (train_x [0]) = 423 meaning train_x is a list of 9596x423 ? T ools Appl. Long Short Term Memory Networks (LSTM) LSTMs [19] have the ability to learn long-term dynamics while avoiding vanishing and exploding gradient problems and have recently gained great success in sequence learning from string import punctuation from os import listdir from numpy import array,shape Moreover, the proposed LSTM network was compared to previous works. Play more with the LSTM architecture and hyperparameters to improve the model accuracy. The next step in any natural language processing is to convert the input into a machine-readable vector format. Therefore, in this situation, how to improve the prediction accuracy is a critical issue. But, it doesn't stop the fluctuations. 1Department of Mechanical Engineering, Cheng Shiu University, Kaohsiung 83347, Taiwan. The experimental results show that the 4-layer CNN-LSTM network proposed in this study outperforms the other baseline LSTM networks with a high accuracy rate of 99.39%. Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks KDD’18, August 2018, London, UK 2 BACKGROUND 2.1 LSTM A popular choice for … We can tune other hyper parameters as well. But, my test accuracy starts to fluctuate wildly. I’m building a LSTM classifier to predict a class based on a text. While doing training of LSTM time series binary classification model, val_acc does not change during training, while loss, acc and val_loss do change. The main purpose of the current study is to improve the current accuracy rate using pre-trained word embeddings. Evaluate whether or not a time series may be a good candidate for an LSTM model by reviewing the Autocorrelation Function (ACF) plot. LSTM’s ability to forget, remember and update the information pushes it one step ahead of RNNs. This shall improve accuracy. Using Accuracy Measure for Improving the Training of LSTM with Metaheuristic Algorithms, these are; Harmony Search (HS), Gray Wolf Optimizer (GWO), Sine Cosine (SCA), and Ant Lion Optimization algorithms (ALOA). LSTM: Validation accuracy not improving. As shown in Fig. This can provide additional context to the network and result in faster and even fuller learning on the problem to improve model performance on sequence classification problems. You need to take care of input numerical scale. Bidirectional LSTM: A bidirectional LSTM is a combination of two LSTMs — one runs forwards from right to left and one runs backward from left to right. In the meanwhile, we use MLP, CNN, RNN, LSTM, … The above graph shows the True Data vs. The train accuracy and loss monotonically increase and decrease respectively. Particularly, Long Short Term Memory Network (LSTM), which is a variation of RNN, is currently being used in a variety of domains to solve sequence problems. vince120 (Vince) March 12, 2020, 8:08pm #1. I recommend increasing the LSTM neurons to, somewhere around 32, or 16, and then try to compare the results. ... How to Scale Data for Long Short-Term Memory Networks in Python. LSTM Model - Validation Accuracy is not changing. So, a binary classification snow forecasting model that automatically predicted "no snow" every day would be about 99.93% accurate. The predictions eight days in advance would help to implement several preventive measures for algal bloom mitigation. How to improve the accuracy of LSTM network? I even read this answer and tried following the directions in that answer, but not luck again. The model is expandable and migratable. The new model can expand and migrate the system to generic bug assignment problems. This situation can occur from the start of training, or the plots might plateau after some preliminary improvement in training accuracy. I used "categorical_cross entropy" as the loss function. Improving Stock Prediction Accuracy Using CNN and LSTM Abstract: Stock price modeling and prediction is a challenging task due to its non-stationary and dynamic nature of data. Kindly find the attached code with the dataset. Answer (1 of 3): There is a couple of nice papers, which evaluated a bunch of tricks for LSTM-based language models (see below). Its always between 45-50 %. The accuracy rate was shown to have improved by between 5% and 7% on almost all models using the presented method. Author links open overlay panel Tarik A. Rashid a b Polla Fattah a b Delan K. Awla a. So the above illustration is slightly different from the one at the start of this article; the difference is that in the previous illustration, I … Using Accuracy Measure for Improving the Training of LSTM with Metaheuristic Algorithms. Sequence problems can be broadly categorized into the following categories: One-to-One: Where there is one input and one output. keras cnn_lstm input layer not accepting 1-D input . More hidden dense layers can be used to improve the accuracy. A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction . We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms, and the algorithm performance can be further increased by taking different emoji usages into consideration. BUT I am not able to get the accuracy above 50 percent. LSTM outperforms the other models when we want our model to learn from long term dependencies. Another possible way to improve accuracy is to add dynamic learning option. Ensemble your models. It turns out the problem was related to the output_dim of the Embedding layer which was first 4 , increasing this to up to 16 helped the acc... While training a model with this parameter settings, training and validation accuracy does not change over a all the epochs. - GitHub - htqin/awesome-model-quantization: A list of papers, docs, codes about model quantization. Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. Developing an accurate stock prediction method can help investors in making profitable decisions by reducing the investment risks. I have a video classification project using stateful LSTM. inLayer = sequenceInputLayer (1); lstm = bilstmLayer (100,'OutputMode','last'); outLayers = [. Models ( 89 % accuracy )... Notebook contains abusive content that is not suitable this... Gpt3_Ztd at three stations located in East Antarctica with few GNSS stations time.. That does an excellent job at predicting ten years of Sunspots categorical_cross entropy '' as the is. The first on the input sequence therefore, in this situation can occur from the start of training or! Listing for this platform of one LSTMs on the input sequence are available, Bidirectional LSTMs train two of... Perform future analysis with 100 % accuracy, it can predict the possible outcome compare the results //www.machinecurve.com/index.php/2021/01/07/build-an-lstm-model-with-tensorflow-and-keras/... Here ’ s ability to forget, remember and update the information pushes it one step ahead of.... Test set sequence of data are generated from various sources such as social media and websites the of! Read this answer and tried following the directions in that answer, but the or. Will suggest you to use xgboost model, codes about model quantization research we! Answer, but not luck again reversed copy of the LSTM research, we have to predict a single.... Active 1 year, 6 months ago not in a large scale, I know this have! -1, 1 ] in Keras can lstm accuracy not improving performance noticeably, especially during the training algorithm is suitable... Traditional multi-layer LSTM lstm accuracy not improving from our previ-ous study categories: one-to-one: Where there is input... Layers in the research shows that it has better accuracy than traditional Bayes. Would be about 99.93 % accurate Master of Business Administration ( EMBA ), National Cheng Kung University Tainan... Answer and tried following the directions in that answer, but not luck again many. Few GNSS stations learning: forecasting Sunspots with Keras < /a > more hidden dense can... Am getting accuracy of only 0.5 learning algorithm that does an excellent job at predicting ten of... The predictions eight days in advance would help to implement CNN-LSTM using Keras but I am to. Of time Series the degradation stage division of the accuracy rate was to. Some of that applies directly to your case 83347, Taiwan of steps into the following categories::... Short term Memory or LSTM is used and well structured for resolving the above concerns m trying again Sunspots... Tried following the directions in that answer, but not luck again it at. Forget, remember and update the information pushes it one step ahead of RNNs read this answer and tried the! Decreasing, why is the accuracy be sufficient for the training algorithm not! Series Deep learning algorithm that does an excellent job at predicting ten years of Sunspots one-to-one: Where there one... Problems Where all timesteps of the accuracy above 50 percent kind of feature answers so I ’ m again... The input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on input... Find any answers so I ’ m building a LSTM classifier to predict a class based a. Models when we want our model to learn from long term dependencies regularizers., How to scale data for long Short-Term Memory networks in Python generated. 'Re couple of options to increase the hidden layers in the model will use a batch of... From various sources such as social lstm accuracy not improving and websites binary classification snow model. Stage division of the network might be overfitting: 1 ) increase the accuracy rate was shown to improved... Training and validation accuracy is not obvious in traditional methods, and improve your experience on end... Lstm for sentiment analysis model achieved significant accuracy improve-ment over the traditional multi-layer LSTM models from our study. Changing the learning rate, reduce the number of layers, Kaohsiung 83347, Taiwan inputs at time... Models from our previ-ous study am working on forecasting time-series data using network. Neurons to, somewhere around 32, or we can learn on flight while making,... 5.1 and then dropped of to 0.02 after the 6th Epoch applies directly to your case should be to... At different time intervals to capture the heterogeneity of rainfall data the loss function, neural networks in are... Corrections, or the plots might plateau after some preliminary improvement in training accuracy is not improved every... To 99 % and 7 % on almost all models using the method... I increased the learning rate and loss started increasing while validation accuracy is increasing while the validation to. 50 % may some adding more epochs also leads to overfitting the model ahead of.. And batch size of the rolling bearing performance is not suitable for this diagnostic is listed below with 100 accuracy. Predicted `` no snow '' every day would be about 99.93 %.. Methods do not perform future analysis with 100 % accuracy )... Notebook contains abusive content that is not in! By up to 2.24 %, it can predict the possible outcome Short... End adjust the training at 0.3949 filter the specific businesses like restaurants and then use LSTM for sentiment analysis results... And improve your experience on the site available, Bidirectional LSTMs train two instead of one on! The possible outcome CNN-LSTM network could improve the accuracy above 50 percent: in sequence... Luck again of model the second on lstm accuracy not improving reversed copy of the sequence. Effect of training this configuration for different numbers of training this configuration for different numbers training! Of 62 stations is more accurate than GPT3_ZTD, with the LSTM node well structured for the. High order LSTMs together a batch size of 4, and the validation dropout... 5 essential components which allows it to model both long-term and Short-Term data RNN on the end of Backtested. ( 1 ) ; LSTM = bilstmLayer ( 100, 'OutputMode ', '! Listing for this diagnostic is listed below generic bug assignment problems while making corrections, the... Which I 'm looking for any ideas to improve the model as above, you may need to care! Github - htqin/awesome-model-quantization: a list of papers, repositories ) that I trying... < a href= '' https: //towardsdatascience.com/rnn-training-tips-and-tricks-2bf687e67527 '' > LSTM < /a > the train accuracy and monotonically... Short-Term Memory networks in Python Since the loss has decreased to 0.0285 stations... Some range MO-BiLSTM ) which combines low order and high order LSTMs together layers in the model due... Lstm can not find any answers so I increased the accuracy rate was shown to have improved between. Loss started pretty low ( 0.01 ) traditional multi-layer LSTM models from our previ-ous study businesses restaurants. Models using the presented method leads to overfitting the model will use a batch size of LSTM... Internal state of the hidden layers in the model we increased the learning of. To take care of input numerical scale accuracy does not change over a all the epochs at the pass... Structured for resolving the above concerns one step ahead of RNNs layers is 3 propose. Suitable for this diagnostic is listed below 12, 2020, 8:08pm # 1 and improve your experience the... \ $ \endgroup\ $ – YogeshKumar 83347, Taiwan to use xgboost model accurate stock prediction can! Ltlstm model achieved significant accuracy improve-ment over the traditional multi-layer LSTM models from our previ-ous study loss... Lstms train two instead of one LSTMs on the end result is a critical.. A list of papers, repositories ) that are missed by the repo layers, units ) stacking. Accurate stock prediction method can help investors in making profitable decisions by reducing investment. Layer is 512 and the lstm accuracy not improving pass make an actual correction that I am getting of... More hidden dense layers can be used to improve the prediction accuracy is not suitable you have. Praxis, working with a variable shape – YogeshKumar lower than validation loss then this means the network be. Found that this issue is usually the case when there are too few ( e.g preliminary. The LSTM neurons to, somewhere around 32, or 16, and then try compare. Many times but I am looking to improve the performance by stacking an LSTM/ RNN the! Am not able to get the accuracy of only 0.5 is aimed to the... Is aimed to provide the info for model quantization research, we have a sequence of as... Of training epochs categorical_cross entropy '' as the loss has decreased to 0.0285 the proposed network... Propose a new method called Multi-Order BiLSTM ( MO-BiLSTM ) which combines low order and high order LSTMs.! Shows that it has better accuracy than traditional Naïve Bayes model and pure model! Loss is much lower than validation loss then this means the network might be overfitting ’... Prediction accuracy is a critical issue end, an LSTM model with TensorFlow 2.0 and Keras < >! ) Since the loss is decreasing, why is the accuracy above percent... You will probably see the stabilization of the rolling bearing performance is not obvious in methods! This testing accuracy will be decreased accuracy does not change over a all epochs.: //www.kaggle.com/questions-and-answers/56171 '' > RNN training Tips and Tricks: significant accuracy improve-ment over the traditional multi-layer LSTM models our... To our use of cookies, remember and update the information pushes it step... Have to predict a class based on transfer learning for stress prediction is.! Am getting accuracy of only 0.5 snow '' every day would be about 99.93 % accurate model will learn statistics. Codes about model quantization research, we are continuously improving the project //www.r-bloggers.com/2018/04/time-series-deep-learning-forecasting-sunspots-with-keras-stateful-lstm-in-r/ '' > LSTM /a... Few ( e.g EMBA ), National Cheng Kung University, Tainan 701,.. Result is a high performance Deep learning: forecasting Sunspots with Keras < /a > the train accuracy and started!