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How to Make Predictions with Keras

Once you choose and fit a final deep learning model in Keras, you can use it to make predictions on new data instances.

There is some confusion amongst beginners about how exactly to do this. I often see questions such as:

How do I make predictions with my model in Keras?

In this tutorial, you will discover exactly how you can make classification and regression predictions with a finalized deep learning model with the Keras Python library.

After completing this tutorial, you will know:

  • How to finalize a model in order to make it ready for making predictions.
  • How to make class and probability predictions for classification problems in Keras.
  • How to make regression predictions in in Keras.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Apr/2018: First publish
  • Updated Jan/2020: Updated for changes in scikit-learn v0.22 API.
  • Updated Aug/2022: Updated for TensorFlow 2.x syntax
How to Make Classification and Regression Predictions for Deep Learning Models in Keras

How to Make Classification and Regression Predictions for Deep Learning Models in Keras
Photo by mstk east, some rights reserved.

Tutorial Overview

This tutorial is divided into 3 parts; they are:

  1. Finalize Model
  2. Classification Predictions
  3. Regression Predictions

1. Finalize Model

Before you can make predictions, you must train a final model.

You may have trained models using k-fold cross validation or train/test splits of your data. This was done in order to give you an estimate of the skill of the model on out of sample data, e.g. new data.

These models have served their purpose and can now be discarded.

You now must train a final model on all of your available data. You can learn more about how to train a final model here:

2. Classification Predictions

Classification problems are those where the model learns a mapping between input features and an output feature that is a label, such as “spam” and “not spam“.

Below is an example of a finalized neural network model in Keras developed for a simple two-class (binary) classification problem.

If developing a neural network model in Keras is new to you, see this Keras tutorial.

After finalizing, you may want to save the model to file, e.g. via the Keras API. Once saved, you can load the model any time and use it to make predictions. For an example of this, see the post:

For simplicity, we will skip this step for the examples in this tutorial.

There are two types of classification predictions we may wish to make with our finalized model; they are class predictions and probability predictions.

Class Predictions

A class prediction is given the finalized model and one or more data instances, predict the class for the data instances.

We do not know the outcome classes for the new data. That is why we need the model in the first place.

We can predict the class for new data instances using our finalized classification model in Keras using the predict_classes() function. Note that this function is only available on Sequential models, not those models developed using the functional API.

For example, we have one or more data instances in an array called Xnew. This can be passed to the predict_classes() function on our model in order to predict the class values for each instance in the array.

Let’s make this concrete with an example:

Running the example predicts the class for the three new data instances, then prints the data and the predictions together.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

If you had just one new data instance, you could provide this as an instance wrapped in an array to the predict_classes() function; for example:

Running the example prints the single instance and the predicted class.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

A Note on Class Labels

Note that when you prepared your data, you will have mapped the class values from your domain (such as strings) to integer values. You may have used a LabelEncoder.

This LabelEncoder can be used to convert the integers back into string values via the inverse_transform() function.

For this reason, you may want to save (pickle) the LabelEncoder used to encode your y values when fitting your final model.

Probability Predictions

Another type of prediction you may wish to make is the probability of the data instance belonging to each class.

This is called a probability prediction where, given a new instance, the model returns the probability for each outcome class as a value between 0 and 1.

You can make these types of predictions in Keras by calling the predict_proba() function; for example:

In the case of a two-class (binary) classification problem, the sigmoid activation function is often used in the output layer. The predicted probability is taken as the likelihood of the observation belonging to class 1, or inverted (1 – probability) to give the probability for class 0.

In the case of a multi-class classification problem, the softmax activation function is often used on the output layer and the likelihood of the observation for each class is returned as a vector.

The example below makes a probability prediction for each example in the Xnew array of data instance.

Running the instance makes the probability predictions and then prints the input data instance and the probability of each instance belonging to class 1.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

This can be helpful in your application if you want to present the probabilities to the user for expert interpretation.

3. Regression Predictions

Regression is a supervised learning problem where given input examples, the model learns a mapping to suitable output quantities, such as “0.1” and “0.2”, etc.

Below is an example of a finalized Keras model for regression.

We can predict quantities with the finalized regression model by calling the predict() function on the finalized model.

The predict() function takes an array of one or more data instances.

The example below demonstrates how to make regression predictions on multiple data instances with an unknown expected outcome.

Running the example makes multiple predictions, then prints the inputs and predictions side by side for review.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

The same function can be used to make a prediction for a single data instance, as long as it is suitably wrapped in a surrounding list or array.

For example:

Running the example makes a single prediction and prints the data instance and prediction for review.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.


In this tutorial, you discovered how you can make classification and regression predictions with a finalized deep learning model with the Keras Python library.

Specifically, you learned:

  • How to finalize a model in order to make it ready for making predictions.
  • How to make class and probability predictions for classification problems in Keras.
  • How to make regression predictions in in Keras.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

224 Responses to How to Make Predictions with Keras

  1. Avatar
    Nitin April 9, 2018 at 10:14 am #

    Great article Jason. Do you recommend any articles for hyperparameter tuning to further improve accuracies? Also any articles for common problems and solutions during model tuning?

  2. Avatar
    bekky April 10, 2018 at 7:32 pm #

    Thanks for the tutorial! If I want to build a CNN which has both classification and regression heads, I suppose I cannot use a sequential model. Do you know an example for such a multi-head CNN? Thank you

  3. Avatar
    Moustafa April 16, 2018 at 6:23 am #

    Thanks for your the explication,
    Could you please put photos of network architectures aside the code ?
    I think it will help us to understand best the architecture

    • Avatar
      Jason Brownlee April 16, 2018 at 6:27 am #

      Thanks for the suggestion.

      • Avatar
        sanjie May 10, 2018 at 1:42 am #

        hello Jason,
        thanks for your articles. you really help us a lot.
        may i ask one question ?

        Can Keras be used to build clustering models?

        keras.wrappers.scikit_learn can be used to build KerasClassifier model, Keras be used to build clustering models? If it can be, are there any examples for that?

        you know i want to use some features like age, city, education, company, job title and so on to cluster people into some groups and to get the key features of each group.

  4. Avatar
    Janne April 17, 2018 at 11:38 pm #

    Hi Jason and thanks for this post. I have a quick question about regression with bounded target values.
    If my target values are always restricted between [0,1] with most of the values close to 0.5 (i.e., values are rarely close to 0 or 1), is it useful to use sigmoid output activation instead of linear? Would it help in convergence or stability when training a complex model? It seems like a waste not to take any advantage of the fact that target values belong into bounded interval.

    So in your code, one would simply make a replacement

    model.add(Dense(1, activation=’linear’)) –> model.add(Dense(1, activation=’sigmoid’))

    • Avatar
      Jason Brownlee April 18, 2018 at 8:08 am #

      Good question.

      Yes, interesting idea. It might change the loss function used to fit the model, which may result in optimizing the wrong problem (e.g. logloss instead of mse). Nevertheless, try it and compare error results between the two approaches.

      Yes, that is the code change.

  5. Avatar
    olufemi April 23, 2018 at 11:22 am #

    What are the considerations in using scikit-learn over Keras for classification problems (or vis versa) in R or Python?

  6. Avatar
    Ali May 2, 2018 at 11:36 am #

    I am trying to predict a new image on a model that I trained with emnist letters. Here is the code snippet that tries to do so.

    import matplotlib
    # Force matplotlib to not use any Xwindows backend.
    import keras
    import matplotlib.pyplot as plt
    from keras.models import load_model
    from keras.preprocessing import image
    from keras import backend as K
    from scipy.misc import imread
    from PIL import Image
    import as io
    import skimage.transform as tr
    import numpy as np
    from keras.utils import plot_model
    from keras.datasets import mnist
    # Returns a compiled model identical to the previous one
    model = load_model(‘matLabbed.h5’)

    print(“Testing the model on our own input data”)

    imgA = imread(‘A.png’)
    imgA = tr.resize(imgA, (28, 28, 1)).astype(‘float32’)
    imgA = imgA[np.newaxis, …]
    y_classes = model.predict_classes(imgA)

    When I try to print y_classes, it gives me numerical outputs e.g 4, 10. I am trying to figure out the labels that my data uses and compare that with the y_classes output. Any suggestion? Thanks.

    • Avatar
      Jason Brownlee May 3, 2018 at 6:30 am #

      The integer will relate to a class in your training data.

  7. Avatar
    Rico May 15, 2018 at 8:03 pm #

    how to predict classes with model developed using functional API ?

    • Avatar
      Jason Brownlee May 16, 2018 at 6:02 am #

      You can call model.predict() then use argmax() on the resulting vector to get the class index.

  8. Avatar
    Valentin May 31, 2018 at 7:56 am #

    Hi Jason, this code editor is really irritating, please consider substituting it with another or just with some simple text.

  9. Avatar
    Vladislav Gladkikh May 31, 2018 at 3:42 pm #

    The networks for classification and regression differ only a little (activation function of the output neuron and the the loss function) yet in the case of classification it is so easy to estimate the probability of the prediction (via predict_proba) while in the case of regression the analog is the prediction interval which is difficult to calculate for non-linear models like neural networks.

    Why is there such a difference? Shouldn’t the probability estimation for regression be done as easily as for classification? A straightforward (but maybe naive) solution that I see is to bin the target variable, make a classification, then use predict_proba to get the probability of the predicted value to be in a certain interval, and then to construct prediction interval from that.

    Can it be done this way? Or changing the loss function will make two problems (regression and classification for the binned target) so much different that the result of one problem cannot be transferred to another?

  10. Avatar
    Cristian Cadar June 1, 2018 at 7:23 pm #

    Hi Jason,

    I built a model to classify a financial time series for stock trading having 10 inputs and one output (‘0’ for stock going down, ‘1’ going up). Now I have 1400 such inputs for training the model and managed to get an accuracy of 97%! …

    but when presenting new inputs to the model I don’t get a classification accuracy more than 30%! my opinion the problem would be:

    1. Overfitting
    2, time series not predictable?

    do you have any idea what the problem might be or how to improve the accuracy for new data presented to the model?


    • Avatar
      Jason Brownlee June 2, 2018 at 6:28 am #

      Low skill might mean underfitting, the wrong model, an intractable problem, etc.

  11. Avatar
    Zoey June 5, 2018 at 7:21 am #

    how to get the RMSE for the regression model?

  12. Avatar
    Bayo June 8, 2018 at 6:33 am #

    very helpful. thanks

  13. Avatar
    Alex June 21, 2018 at 7:31 am #

    Hi, I am working on a multilabel problem. My X_train has this shape (for instance) [[0 1 0 0 1]…]. I want to predict the next, like [1 0 0 1 0]. How can I do that? Each label is independent from the others. How train that? Where can I find some information to learn about it?


    • Avatar
      Jason Brownlee June 21, 2018 at 4:49 pm #

      Sorry, I don’t have examples of multilabel forecasting, only multiclass forecasting.

  14. Avatar
    Narayan June 22, 2018 at 12:35 am #

    Very nice! could you plz also make a similar tutorial with PyToorch.

  15. Avatar
    Abrar July 6, 2018 at 2:41 am #

    Thanks for your post, Jason.

    When we are predicting, we are required to revert back to the original scaling. as both the input and output seem to be scaled for the model to predict correctly.

    • Avatar
      Jason Brownlee July 6, 2018 at 6:45 am #

      Correct, we should return yhat to the original scale before evaluating error in the case of regression.

  16. Avatar
    Nijat July 7, 2018 at 8:33 pm #

    Hi, thanks for a great post.
    I have question related to regression problem.
    I am trying to develop a model that predcits 3 outputs from 36 input variables. To be clear, I have 3 output parameters and 36 input parameters. Which means that in the output layer I have 3 neurons. The model can sucessively predict whe there is only one output, but when it comes to 3 outputs it gives somethig useless. What do you suggest? By the way I use keras mlp regressor.

  17. Avatar
    chatra July 12, 2018 at 4:33 pm #

    Hi Jason, Thanks for the explanation of classification and regression architecture.

    I am trying to use the regression model on 300 dimensions vectors. So the problem statement is to Know which two vectors are similar or non-similar. So for this, I have taken the difference between two similar vectors and non-similar vectors. We labeled similar diff vectors as 0 and non-similar diff vectors as 1. And feeding it to the network of which looks like this:
    model = Sequential()
    model.add(Dense(300, input_dim=301, activation=’relu’))
    model.add(Dense(1, activation=’sigmoid’))
    model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘acc’])

    Training_Loss: 0.0171
    Training_Acc: 0.9988

    Testing_Loss = 0.6456110666
    Testing_Acc = 0.821831868464

    I am not sure, why the model is overfitting, Can you please give some insight into what could be wrong?

    • Avatar
      Jason Brownlee July 13, 2018 at 7:31 am #

      Perhaps try some different model configurations and see if they over fit? E.g. less nodes.

  18. Avatar
    shivam sinha July 30, 2018 at 3:03 pm #

    Thanks for this explanation.
    i am trying to use a end to end nvidia model for self driving car in keras. its a regression problem to predict the angle of steering by providing image of camera installed front side of car.
    here the problem i am facing is when i predicting the angle using model.predict() , i get a constant value for all input. even my model gives very less error at the time of training like–

    Epoch 00001: val_loss improved from inf to 21.63187, saving model to drive/abc/weights.best6.hdf5
    Epoch 2/30
    283/283 [==============================] – 199s 702ms/step – loss: 18.0307 – val_loss: 16.9002

    Epoch 00002: val_loss improved from 21.63187 to 16.90022, saving model to drive/abc/weights.best6.hdf5
    Epoch 3/30
    5/283 […………………………] – ETA: 25s – loss: 16.0846283/283 [==============================] – 199s 704ms/step – loss: 14.2439 – val_loss: 13.9543

    Epoch 00003: val_loss improved from 16.90022 to 13.95434, saving model to drive/abc/weights.best6.hdf5
    Epoch 4/30
    283/283 [==============================] – 200s 708ms/step – loss: 11.9354 – val_loss: 12.1763

    Epoch 00004: val_loss improved from 13.95434 to 12.17632, saving model to drive/abc/weights.best6.hdf5
    Epoch 5/30
    4/283 […………………………] – ETA: 25s – loss: 11.4636283/283 [==============================] – 201s 711ms/step – loss: 10.5595 – val_loss: 11.1414

    Epoch 00005: val_loss improved from 12.17632 to 11.14141, saving model to drive/abc/weights.best6.hdf5
    Epoch 6/30
    283/283 [==============================] – 201s 711ms/step – loss: 8.3687 – val_loss: 0.7611

    Epoch 00006: val_loss improved from 11.14141 to 0.76112, saving model to drive/abc/weights.best6.hdf5
    Epoch 7/30
    3/283 […………………………] – ETA: 26s – loss: 1.0417283/283 [==============================] – 199s 704ms/step – loss: 0.7403 – val_loss: 0.4893

    Epoch 00007: val_loss improved from 0.76112 to 0.48934, saving model to drive/abc/weights.best6.hdf5
    Epoch 8/30
    283/283 [==============================] – 203s 716ms/step – loss: 0.5372 – val_loss: 0.3477

    Epoch 00008: val_loss improved from 0.48934 to 0.34773, saving model to drive/abc/weights.best6.hdf5
    Epoch 9/30
    6/283 […………………………] – ETA: 26s – loss: 0.4749283/283 [==============================] – 216s 763ms/step – loss: 0.4332 – val_loss: 0.2760

    Epoch 00009: val_loss improved from 0.34773 to 0.27596, saving model to drive/abc/weights.best6.hdf5
    Epoch 10/30
    283/283 [==============================] – 211s 744ms/step – loss: 0.3821 – val_loss: 0.2406

    Epoch 00010: val_loss improved from 0.27596 to 0.24057, saving model to drive/abc/weights.best6.hdf5
    Epoch 11/30
    6/283 […………………………] – ETA: 25s – loss: 0.3903283/283 [==============================] – 207s 733ms/step – loss: 0.3565 – val_loss: 0.2229

    Epoch 00011: val_loss improved from 0.24057 to 0.22293, saving model to drive/abc/weights.best6.hdf5
    Epoch 12/30
    283/283 [==============================] – 222s 784ms/step – loss: 0.3438 – val_loss: 0.2134

    Epoch 00012: val_loss improved from 0.22293 to 0.21340, saving model to drive/abc/weights.best6.hdf5
    Epoch 13/30
    7/283 […………………………] – ETA: 25s – loss: 0.3331283/283 [==============================] – 205s 724ms/step – loss: 0.3331 – val_loss: 0.2076

    Epoch 00013: val_loss improved from 0.21340 to 0.20755, saving model to drive/abc/weights.best6.hdf5
    Epoch 14/30
    283/283 [==============================] – 218s 771ms/step – loss: 0.3305 – val_loss: 0.2036

    Epoch 00014: val_loss improved from 0.20755 to 0.20359, saving model to drive/abc/weights.best6.hdf5
    Epoch 15/30
    7/283 […………………………] – ETA: 26s – loss: 0.5313283/283 [==============================] – 211s 745ms/step – loss: 0.3295 – val_loss: 0.2006

    Epoch 00015: val_loss improved from 0.20359 to 0.20061, saving model to drive/abc/weights.best6.hdf5
    Epoch 16/30
    283/283 [==============================] – 210s 743ms/step – loss: 0.3272 – val_loss: 0.1982

    Epoch 00016: val_loss improved from 0.20061 to 0.19824, saving model to drive/abc/weights.best6.hdf5
    Epoch 17/30
    6/283 […………………………] – ETA: 27s – loss: 0.3350283/283 [==============================] – 220s 778ms/step – loss: 0.3236 – val_loss: 0.1963

    Epoch 00017: val_loss improved from 0.19824 to 0.19628, saving model to drive/abc/weights.best6.hdf5
    Epoch 18/30
    283/283 [==============================] – 205s 726ms/step – loss: 0.3219 – val_loss: 0.1946

    Epoch 00018: val_loss improved from 0.19628 to 0.19460, saving model to drive/abc/weights.best6.hdf5
    Epoch 19/30
    6/283 […………………………] – ETA: 28s – loss: 0.4911283/283 [==============================] – 215s 761ms/step – loss: 0.3180 – val_loss: 0.1932

    Epoch 00019: val_loss improved from 0.19460 to 0.19320, saving model to drive/abc/weights.best6.hdf5
    Epoch 20/30
    283/283 [==============================] – 215s 761ms/step – loss: 0.3197 – val_loss: 0.1920

    Epoch 00020: val_loss improved from 0.19320 to 0.19198, saving model to drive/abc/weights.best6.hdf5
    Epoch 21/30
    6/283 […………………………] – ETA: 31s – loss: 0.7507283/283 [==============================] – 207s 731ms/step – loss: 0.3188 – val_loss: 0.1910

    Epoch 00021: val_loss improved from 0.19198 to 0.19101, saving model to drive/abc/weights.best6.hdf5
    Epoch 22/30
    283/283 [==============================] – 221s 781ms/step – loss: 0.3201 – val_loss: 0.1901

    Epoch 00022: val_loss improved from 0.19101 to 0.19008, saving model to drive/abc/weights.best6.hdf5
    Epoch 23/30
    7/283 […………………………] – ETA: 27s – loss: 0.6471283/283 [==============================] – 207s 730ms/step – loss: 0.3198 – val_loss: 0.1893

    Epoch 00023: val_loss improved from 0.19008 to 0.18925, saving model to drive/abc/weights.best6.hdf5
    Epoch 24/30
    283/283 [==============================] – 215s 758ms/step – loss: 0.3199 – val_loss: 0.1886

    Epoch 00024: val_loss improved from 0.18925 to 0.18856, saving model to drive/abc/weights.best6.hdf5
    Epoch 25/30
    6/283 […………………………] – ETA: 26s – loss: 0.6787283/283 [==============================] – 218s 769ms/step – loss: 0.3197 – val_loss: 0.1879

    Epoch 00025: val_loss improved from 0.18856 to 0.18787, saving model to drive/abc/weights.best6.hdf5
    Epoch 26/30
    283/283 [==============================] – 206s 727ms/step – loss: 0.3191 – val_loss: 0.1873

    Epoch 00026: val_loss improved from 0.18787 to 0.18725, saving model to drive/abc/weights.best6.hdf5
    Epoch 27/30
    5/283 […………………………] – ETA: 26s – loss: 0.2300283/283 [==============================] – 221s 779ms/step – loss: 0.3175 – val_loss: 0.1868

    Epoch 00027: val_loss improved from 0.18725 to 0.18680, saving model to drive/abc/weights.best6.hdf5
    Epoch 28/30
    283/283 [==============================] – 209s 740ms/step – loss: 0.3186 – val_loss: 0.1865

    Epoch 00028: val_loss improved from 0.18680 to 0.18653, saving model to drive/abc/weights.best6.hdf5
    Epoch 29/30
    5/283 […………………………] – ETA: 28s – loss: 0.1778283/283 [==============================] – 213s 752ms/step – loss: 0.3165 – val_loss: 0.1864

    Epoch 00029: val_loss improved from 0.18653 to 0.18639, saving model to drive/abc/weights.best6.hdf5
    Epoch 30/30
    283/283 [==============================] – 218s 771ms/step – loss: 0.3167 – val_loss: 0.1864

    Epoch 00030: val_loss did not improve from 0.18639

    so my question is why model.predict() gives constant value always?


    • Avatar
      Jason Brownlee July 31, 2018 at 5:58 am #

      Perhaps the model needs to be tuned to your problem?

      • Avatar
        zzhulm October 21, 2020 at 4:06 pm #

        Thank you so much for this tutorial, right now I am facing some issues which may not be covered here, the problem listed hete are single data or a set of data , but since my network have both batch normalization and lstm later,which have impact on the output result if the batch size is different, particularly for the time serial prediction problem, normally trained the model with large batch size to increase the training speed. After traing , the usage is in real time input pattern, only one data per minute , the problem is how to set the input pattern with batch size in real time such that the impact of the batch minimized.
        When a new data come, should I just input one data? Or append it to a large batch set and input a batch or even many batches to the model?
        Btw I did read your article about the statefull lstm model. Thank you

        • Avatar
          Jason Brownlee October 22, 2020 at 6:36 am #

          I recommend evaluating the performance of the model with a single input vs batches of different sizes to see if it impacts model skill, then use the configuration that works best.

  19. Avatar
    Rihana July 31, 2018 at 8:36 am #

    I have a question on text prediction.
    Since we are using different approaches to enumerate the text before any model fitting. I face a logical problem here.
    Imaging in my train set I have 20 uniq token with I enumerate them like this: (“i”,1)(“love”,2)(“this”,3)… .
    After training the model and fitting the data I destroy everything but the model. So I cann’t keep track of this dictionary anymore.
    When I want to predict a new sequence that its tokens are completely new things [“we”, “went”, “to”, “Spain”].
    How could this new tokens being related to the old dictionary?
    how should I enumerate it?

    If I use a new dictionary to enumerate this, like: (“we”, 1)(“went”, 2)… . then how model can differentiate that this “We” is different from the “i” in train model?

    Thanks in advance!

    • Avatar
      Jason Brownlee July 31, 2018 at 2:57 pm #

      You must also keep the method used to encode words to integers.

  20. Avatar
    Feyza August 1, 2018 at 1:19 am #

    Hi Jason, I don’t understand how program predicts 0 or 1?

  21. Avatar
    Atique August 3, 2018 at 4:54 pm #

    Great Article. one thing is confusing though. For regression predictions, you used 3 instances in first example and 1 instance in next example, instance in second example is actually first instance in the first example. so why are their results different?

    • Avatar
      Jason Brownlee August 4, 2018 at 6:01 am #

      Sorry, I don’t follow. Perhaps you can provide more context?

    • Avatar
      Shooter August 4, 2018 at 10:44 pm #

      Because model doesn’t run in exact the same manner everytime unless u save it that’s why.

  22. Avatar
    Shooter August 4, 2018 at 10:37 pm #

    How to evaluate the model? This code does not work for evaluation as in previous example.

    scores = model.evaluate(X, y)
    print(“\n%s: %.2f%%” % (model.metrics_names[1], scores[1]*100))

    • Avatar
      Jason Brownlee August 5, 2018 at 5:30 am #

      This tutorial is about making predictions.

      I recommend evaluating model performing using using a train/test split or using a resampling method such as k-fold cross-validation. I have many tutorials on each.

      Perhaps start here:

      • Avatar
        Shooter August 5, 2018 at 7:59 pm #

        I mean not about about train/test split, what if i want to just evaluate my model on the same data i used for training?

        I used the command

        scores = model.evaluate(X, y, verbose=2)

        In diabetes example when i print scores,
        output is : [0.5047814001639684, 0.7513020833333334]
        score[1] here means accuracy of the model

        In case of this regression example, when i print scores. I get 0.021803879588842393. What does this data mean? How can i calculate accuracy from this?

        • Avatar
          Jason Brownlee August 6, 2018 at 6:27 am #

          If you evaluate a model for regression, the score will be an error score, e.g. the loss chosen to minimize during training. Such as mse.

          • Avatar
            Shooter August 6, 2018 at 2:40 pm #

            Oh ok thanks . I think i got it. Accuracy is used only in classification to check whether the model classifies it in correct class or not. But in prediction is in real values so accuracy does not make any sense. Now if someone how accurate is my model, should i give them answer in error score?

          • Avatar
            Jason Brownlee August 6, 2018 at 2:56 pm #

            We use cross-validation or train/test splits to answer the question: “how accurate is my model?”

            We use the model to make predictions on new data, to answer the question: “what is the likely class for this observation?”

            Does that help?

          • Avatar
            Shooter August 6, 2018 at 5:37 pm #

            Thanks, it does help. I think I need to study deeper on this topic. By the way, your contents on this site are great, covering almost all the problems about Deep Learning using python.

          • Avatar
            Jason Brownlee August 7, 2018 at 6:24 am #


  23. Avatar
    Moritz September 18, 2018 at 5:17 am #

    Thanks for the interesting post! Is it really required to also do scaling of the ground truth and if yes why?

    • Avatar
      Jason Brownlee September 18, 2018 at 6:24 am #

      It can help during training to scale the output for regression. It depends on the model and the problem.

  24. Avatar
    Franco Arda October 1, 2018 at 2:08 am #

    hi Jason,

    may I ask you why you did not do a prediction from a saved h5 file?
    without it, we need to run the model every time.
    to make it simpler to explain?

    thanks! Franco

  25. Avatar
    Amit Chaudhary October 28, 2018 at 4:57 am #

    Are model.predict(…) and model.predict_proba(…) equivalent if activation in last layer is softmax or sigmoid?

    • Avatar
      Jason Brownlee October 28, 2018 at 6:15 am #

      No. I believe predict() will perform an argmax on the predict_proba result to give an integer.

      • Avatar
        ashok harnal February 6, 2021 at 12:45 pm #

        In tensorflow 2.X, predict() also returns probability

        • Avatar
          Jason Brownlee February 6, 2021 at 2:23 pm #

          Agreed, I believe the previous comment was regarding sklearn. Keras/TF does not have a predict_proba() function.

  26. Avatar
    Akshat Jain November 3, 2018 at 4:56 am #

    Is there any way to get classes with probabilities like:
    class 1 : 0.99, class 2 : 0.8 etc something like this. I have a project in which I have to show confidence of every class for an input, how to do that?

    • Avatar
      Jason Brownlee November 3, 2018 at 7:12 am #

      Yes, mentioned in the above post:

    • Avatar
      habib June 18, 2019 at 9:13 am #

      Hi Jason How can I get the predicted probability of each test sample? it is possible for one image given ?

  27. Avatar
    Akshat Jain November 3, 2018 at 6:19 am #

    Is there any way so that I can print classes with confidence in this. Like I am working on a project in which for an input I have to print classes with confidence in the output.

  28. Avatar
    Faraz Malik Awan November 13, 2018 at 11:53 pm #

    Thank you so much for explaining it so well. I have a question. Can you please tell us what changes will be required if we want to do the regression for multiple outputs. For example, if we have features X=[x1,x2,x3] and we want to produce output Y=[y1,y2]. we want to produce multiple outputs against each observation.

    • Avatar
      Jason Brownlee November 14, 2018 at 7:30 am #

      Sure, for a problem that requires predicting 2 values, you will require 2 nodes in the output layer of your network. That’s it.

  29. Avatar
    irfan November 18, 2018 at 6:17 pm #

    hi jason

    is there any model for hyperspectral images. or any link which give training model for hyperspectral images..

  30. Avatar
    Steffen November 26, 2018 at 6:49 pm #

    Hi, what is the meaning of X and y?
    I assume X being an input, y some output. But what is the meaning of upper/lower case writing?

  31. Avatar
    Praveen December 17, 2018 at 11:33 pm #

    Hi Jason

    Is there a way to get variable / input importance with keras ?

    or How do you find which variables are important while training a neural network.

  32. Avatar
    prisilla January 8, 2019 at 5:17 pm #

    Hi Jason,

    why did you use scalar = MinMaxScaler()?

  33. Avatar
    David January 12, 2019 at 6:58 am #


    I am running a CNN to classify images labeled as high or low. When I run predict_proba(img) after just one epoch and predict the results of a set of images all classified the same, I see a series of values for the images that are all very similar to:

    [[ 0.49511209]]
    [[ 0.49458334]]
    [[ 0.49470016]]

    After 50 epochs, the validation accuracy is about 95%, and the output of predict_proba(img) is similar to:

    [[ 0.80663812]]
    [[ 0.97021145]]
    [[ 0.96050763]]

    where none of the values are below 0.5

    Could you please tell me essentially what I’m seeing? That is, how do I know what class the probability is referring to (“high” or “low”); why is the probability all below 0.5 for minimum training and closer to 1 at 50 epochs; and why does the probability rise almost uniformly with more training?

    Thank you for your work and service in freely sharing your knowledge.

    • Avatar
      Jason Brownlee January 13, 2019 at 5:35 am #

      It suggests for that image that the model is unsure of what it is and strongly believes it belongs to all 3 classes.

      • Avatar
        David January 13, 2019 at 5:57 am #

        Hi, thank you for this response. Those values of accuracy are for three separate images as examples for training at 1 epoch and 50 epochs, respectively. Knowing that, could you please respond to my original questions? Thank you!

        • Avatar
          Jason Brownlee January 14, 2019 at 5:22 am #

          I see, the “low” and “high” are mapped to integers 0 and 1, then the predictions will be between 0 and 1 for class 1.

          The mapping of strings to ints will be controlled by you, e.g. labelencoder in sklearn, and you can inspect it to discover whether low is 0 or 1.

          Does that help?

          • Avatar
            David January 15, 2019 at 2:18 am #

            That does help, thank you! To be sure, when the probability is 0.97, for instance, the predicted class is 1, but when the probability is 0.494, the predicted class is 0?



          • Avatar
            Jason Brownlee January 15, 2019 at 5:54 am #


  34. Avatar
    David January 15, 2019 at 8:24 am #

    OK, thank you!

  35. Avatar
    Roi Ong January 29, 2019 at 6:56 pm #

    For text classification of binary sentiments, positive and negative.
    Why are words not found on the model’s vocabulary always give the same prediction value?

    For example, when i input a single word ‘extravagant’, which is not found on the vocabulary of the model, it will give me a value of 0.165 that is classified as negative. The same probability is resulted when i enter new single terms that are not found on the vocabulary. What causes these words to be predicted with the probability of 0.165 or the same prediction value?

    • Avatar
      Jason Brownlee January 30, 2019 at 8:07 am #

      Unknown words are mapped a zero or “unknown” input.

      • Avatar
        Roi Ong January 30, 2019 at 3:48 pm #

        Hi! Thank you so much for the response Jason.
        I’d just like to ask another question. If unknown outputs are mapped zero/unknown, why is it that the output is not zero as well?
        Is it due to the model itself? that it generates the prediction 0.165 based on the values its layers always generate when receiving 0 as input?

        • Avatar
          Jason Brownlee January 31, 2019 at 5:26 am #

          I suspect because it is unusual to have a single unknown word as input to the model – e.g. it would not happen in practice.

          • Avatar
            Roi Ong January 31, 2019 at 1:58 pm #

            Thank you so much again Jason! I really appreciate the help.

  36. Avatar
    Rajat March 4, 2019 at 5:55 pm #

    Hello Jason,
    Thank you for the great article !
    I had a query, you have used Sequential() to create the architecture. How can we predict the values when we have used Model() for the architecture ?

    • Avatar
      Jason Brownlee March 5, 2019 at 6:32 am #

      You can use the predict() function on the Model instance.

      If you need the class value, you can use something like:

      yhat = model.predict()
      class_id = argmax(yhat[0])

  37. Avatar
    Manos March 20, 2019 at 10:23 pm #

    I build an encoder-decoder model for text summarization based on ‘’.
    I train the model and now i want to predict a summary based on
    a test data set(find the summary for a single document), so i used the same structure for the test data as in the train data. I have a problem with the command model.predict(). Which is the input ?
    I tried to use an array, list of numphy arrays, to encode the test data, but nothing. Is there any example for prediction in text summarization ?

    • Avatar
      Jason Brownlee March 21, 2019 at 8:14 am #

      The input to the predict function is an array of samples with the same structure/preparation as trainX.

  38. Avatar
    rachana patel March 23, 2019 at 9:38 am #

    i am working on my project for predicting house price from images,model is created 200 epochs are being scanned and an avg price is being displayed ,now what i want to do is predict the house price with the using four images that is kitchen,bathroom,frontal image and zipcode ,
    how shall i apply the input? and how shall i call method.predict(),please can u write me down the code?

    • Avatar
      Jason Brownlee March 24, 2019 at 7:01 am #

      Very cool project idea.

      Perhaps compare an ensemble of separate models vs a model that takes each photo as input in parallel – e.g. a multi-input model.

  39. Avatar
    yao April 9, 2019 at 3:49 pm #

    Thank you for your effort and excellent post! For the single data, Xnew = array([[0.89337759, 0.65864154]]), do we need to use MinMax scaler to preprocess before we feed it to the model for prediction? This is related to Class Prediction part2 for predicting the class for a single instance. Thanks a lot!

    • Avatar
      Jason Brownlee April 10, 2019 at 6:09 am #

      Any new input to the model should be prepared in an identical way to the training data.

  40. Avatar
    ROOPAL April 9, 2019 at 8:23 pm #

    I want to ask if I want to use CNN for binary simple non-image classification data without the fully connected layer then how can use.

  41. Avatar
    Krati Dave April 16, 2019 at 3:30 pm #

    Thanks for the tutorial !!
    How can I predict images in real time through webcam??

  42. Avatar
    Roya April 19, 2019 at 10:48 am #

    I have my own dataset and images and label file, How could I load them to Keras and feed them to network?
    thanks for this tutorial

  43. Avatar
    Danial April 23, 2019 at 7:56 pm #

    Hi jason..
    I saw you are using keras. Above codes are using BP algorithm for optimizing weights or not at the back end that we can’t see in code? Is it possible

    • Avatar
      Jason Brownlee April 24, 2019 at 7:57 am #

      Keras uses backpropagation to update model weights.

  44. Avatar
    simran kalra June 9, 2019 at 4:14 pm #

    I have multiple classes how can i make probabilities for each class?

  45. Avatar
    zeinab July 8, 2019 at 3:49 am #

    I run the multichannel cnn model on a text similarity problem.

    Every time I run the model, I have different results(loss, accuracy).

    Does it is normal to have different loss and accuracy results every time I run the code?

  46. Avatar
    tawsin August 4, 2019 at 1:17 pm #

    model.predict() it gives me an array of 0 and 1 and not the probabilities.

    • Avatar
      Jason Brownlee August 5, 2019 at 6:45 am #

      Perhaps confirm you are using a sigmoid activation function and a cross entropy loss.

  47. Avatar
    Suraj Pawar August 13, 2019 at 8:09 am #

    What is the data type of output of model.predict()? My training dataset has float64 data type. But the model.predict gave me float32 output. Do Keras stores learned weights and biases in float32 format?
    Thank you.

    • Avatar
      Jason Brownlee August 13, 2019 at 2:36 pm #

      Typically it is float32 as far as I know.

      • Avatar
        Nikita Saxena . June 3, 2020 at 12:50 am #

        So is there no way to make the model predict float64 type?

  48. Avatar
    zeinab August 13, 2019 at 12:29 pm #

    Hi Jason,
    Why loss and accuracy values changes very time i run the model. however, I upload the training, validation, and the testing dataset

  49. Avatar
    Wang Xing Yang August 18, 2019 at 9:58 pm #

    Hi this might be stupid question but I want to know what is the difference between the Sequential model from keras and creating an autoencoder for the same prediction problem. I am just looking for a simple or clear definition of both of them. keras documentation does not say much about this sequential model. thanks in advance.

    • Avatar
      Jason Brownlee August 19, 2019 at 6:07 am #

      A prediction model maps an input to a given output, e.g. numeric or class label.

      An antoencoder provides a compressed representation of an input, e.g. a projection.

      • Avatar
        Mikhail August 19, 2019 at 8:38 pm #

        Hi, Mr Jason.
        How are you?
        I have one problem.
        I have built model for OCR but i can’t get probability result
        In building model, i have softmax as a activation function.
        I tried to get the probability result to use prediction function but i had not.
        Could you explain this?

        • Avatar
          Jason Brownlee August 20, 2019 at 6:26 am #

          Call model.predict() to get probability predictions.

          What problem are you having exactly?

  50. Avatar
    Mikhail August 21, 2019 at 5:49 pm #

    thank you for answering
    by the way my training result is [0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0]
    but i want probability result like [0.01,0.03, 0.91,0.07,0.10, …]
    i use model.predict() function now.
    but result is not probability value.
    i want how to get probability value
    Kind regards.
    thank you sir.

  51. Avatar
    Prem August 22, 2019 at 12:22 pm #

    Hi Jason,

    The sklearn classifiers uses target names as strings (Good, Bad) but the keras sklearn modelling requiring to map as (0,1), is there a way to use as string itself, this is to match with the LIME explanation from the keras model.

    • Avatar
      Jason Brownlee August 22, 2019 at 2:00 pm #

      No, typically you must map the integers to strings yourself with some custom code.

      • Avatar
        Prem August 22, 2019 at 2:21 pm #

        Thanks Jason,
        Keras Sequential Model returns probability of one class as below

        pred = Keras_model.predict_proba(testData_for_model)
        Out[73]: array([[0.6559619]], dtype=float32)

        pre.argmax(axis=-1)[0] gives class 0

        so I have to code 1-pred as class 1 probability

        Is this way it works

        • Avatar
          Jason Brownlee August 23, 2019 at 6:17 am #

          One sample is used to make one prediction.

          You can convert probabilities to clasess via argmax or call predict_classes() directly.

          • Avatar
            Prem August 23, 2019 at 11:06 am #

            Thanks Jason, I am bit confused finding which class belongs to from the following,

            Out[30]: array([[0.77143025]], dtype=float32)

            Out[32]: array([[1]])

            Out[33]: array([0], dtype=int64)

            When I call predict_classes it says 1 and with argmax it says 0, may I know which class it belongs to please

          • Avatar
            Jason Brownlee August 23, 2019 at 2:09 pm #

            An argmax on a single element vector is invalid.

            Instead, you must round the result, which be class 1.

  52. Avatar
    ziko August 27, 2019 at 8:57 pm #

    hi Jason, as always i find your tutorials best for me to learn and get better understanding! thx! for a regression system, can you please explain how to actually extract the weights and get real (not scaled ) predictions values?

    • Avatar
      Jason Brownlee August 28, 2019 at 6:34 am #


      You can fit the model on scaled data, then invert the scaling on the predicted values – this is common.

  53. Avatar
    Atena September 19, 2019 at 4:24 am #

    Hi dear Jason. Thanks for your good site and contents which helps us to start coding machine learning model.
    I have a dataset of some activities. The dataset contains the status of different sensors and the label of activity. T trained a model in Keras with the following architecture which models the activities.

    model = Sequential()
    model.add(Embedding(max_features+1, embedding_vector_dim))
    model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2, return_sequences = True))
    model.add(Dense(1, activation=’sigmoid’))

    Now, I need to make a different LSTM-based model for each activity separately. Then, calculate the probability of creating the sequence by each model. Can I do that with Keras? How should I design the architecture of the model? Is it right to remove the dense layer and then create a separate model for each activity label separately?

  54. Avatar
    Suraj Pawar September 20, 2019 at 2:04 pm #

    I would like to know when I give a number of test samples for the prediction to model.predict(), does the model takes each sample sequentially? Or does the model takes all samples at a time and give the predicted output? Is there a way to make the prediction in a vectorized manner? Thank you.

    • Avatar
      Jason Brownlee September 21, 2019 at 6:42 am #

      Yes, input examples are processed sequentially.

      That means the first prediction will be for the first row of input.

      Does that help?

  55. Avatar
    Omri October 21, 2019 at 8:57 am #

    hie Jason. thanks for the tutorial. I managed to make the model (model.yaml /model.h5) from the previous tutorial. Now how do i use this file to predict the output when i enter new data?

    • Avatar
      Jason Brownlee October 21, 2019 at 1:41 pm #

      Load the model and call predict()

      • Avatar
        Omri October 21, 2019 at 3:30 pm #

        thank an example is this correct?

        from keras.models import load_model
        model = load_model(‘model.h5’)

  56. Avatar
    Adela October 29, 2019 at 5:37 am #

    I have a question. I want to make a prediction, but it is not based on numbers, but on text data. Is there an effective way to do it?
    Input is date, time, location. Output is hashtag 1 to 35. There are thousands of possible hashtags.
    The training file has tens of thousands of dates, times, locations and fields of 1-35 hashtags.
    Do I need to convert everything to numbers in order to make the regression? Or can the nltk help me somehow?
    I can do regression with numbers, but how do I use text?
    Thank you so much for your help.

  57. Avatar
    Markus Buchholz December 17, 2019 at 9:50 pm #

    Hi Jason,

    Your work, books and posts have made great impact on Deep Learning understanding across CS enthusiasts, scientists and researchers. Awesome! Thanks

    Currently I work on robot inverse kinematics which solution will be approximate by neural network (NN). Data set comes from forward kinematics. My data set consists of 50000 records (each record consist of 7 values ) – 7 Inputs (position of end effector X,Y,Z and orientation *4 = 7). Outputs from NN are motor/robot angles => 6. I defined many different architectures (flat/deep/broad and narrow) but still there is a issue with quality (the quality of prediction of angles are unfortunately quiet poor MSE = 0.3).
    I suppose that the problem is directly connected with architecture of NN. It will be great if you have a chance and can give me some tips how to improve prediction (change the NN architecture or something more).


    INPUT = 7 (x,y,x + orientation*4 )
    OUTPUT = 6 robot motor angles

    I use sequential NN from Keras. Neurons are configured as follow (deployed NN architecture) : 7 – 32 -128 – 256 – 256 -128 – 32 – 6. I tried also to increase the hidden layers or made the NN totally flat but without positive result.
    I used also (for example) 7-256-512-1024-1024-1024-512-256-6 and other variants but without succeed (batch_size = from 32 to 512; epoch from 1000 to 500. The dataset is pre-proceeded – I used the MinMaxScaler (0,1). Neurons have a RELU activation function

    I wonder Jason if you can help me, please?


    Best regards,
    Markus Buchholz

  58. Avatar
    Mohamed Abdullah March 16, 2020 at 3:39 am #

    Hi, Jason.

    if there is a way to output prediction values as integers only (not float).

    • Avatar
      Jason Brownlee March 16, 2020 at 5:57 am #

      You can call predict_classes() to get the class integers.

  59. Avatar
    liu sheng March 22, 2020 at 6:56 pm #

    Hello, sir. I’d like to make a crop recommendation. There are many kinds of crops, about soil characteristics (pH, N, P, K…). Recommend crops by forecasting the yield of various crops.But I don’t know how to implement it with keras. Can you give me some ideas?

  60. Avatar
    Hector March 31, 2020 at 1:04 pm #

    Hi, thanks for this, so i have a question, how can i get 2 or more outputs with this example? and then, how can i print this outputs?

    • Avatar
      Jason Brownlee March 31, 2020 at 1:37 pm #

      You can provide an array with two input samples to make two predictions.

      You can print predictions by using the print() function.

  61. Avatar
    Patrick Lai May 2, 2020 at 2:32 am #

    Thank you I really appreciate your step-by-step approach. I’ve been taking quite many courses on deeplearning already I know a lot of theories but how to apply them there are not so many articles are as clear as yours.

    Keep the good work!

  62. Avatar
    Mousheng Xu May 22, 2020 at 1:00 pm #

    Hi Jason,

    Great post! I have been following several of your posts and they are all great!

    One question about “predict_proba(..)”: is it available for a VGG model? It seems that it does not exist. If I just use “predict(..)”, it only returns an array like “[[7.090991e-05]]”. Is this the probability for class 1 (and thus the probability for class 0 is 7.090991e-05)?

    • Avatar
      Jason Brownlee May 22, 2020 at 1:25 pm #

      Calling predict() on a classification model will return probabilities.

  63. Avatar
    ahmad May 31, 2020 at 9:29 am #

    Thanks for the tutorial! I built the DNN model to predict the survivability of breast cancer patients (Died(1) or survived (0) ), how to predict the class label (0 or 1) for new cases? “without the training process directly” just I want to insert features value and predict it using DNN model?

    • Avatar
      Jason Brownlee May 31, 2020 at 1:23 pm #

      You’re welcome!

      You can create an array and pass it to the predict() function. The above tutorial shows you how.

      • Avatar
        ahmad June 1, 2020 at 9:14 am #

        Xnew = array([[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,

        ynew = model.predict_classes(Xnew)

        I have 33 features, and I want to predict new cases (0 or 1) after fitting the model!

        But show this error!

        Error when checking input: expected input_2 to have shape (33,) but got array with shape (2,)

        • Avatar
          Jason Brownlee June 1, 2020 at 1:41 pm #

          That is surprising, perhaps double check the expectation of your model.

  64. Avatar
    nkm August 16, 2020 at 3:56 am #

    Hello Mr. Jason,

    thanks for your great support.

    I have one query. When I calculate confusion matrix for binary classification using sigmoid, I get wrong confusion matrix with 50 percent accuracy. My code is:

    y_true = test_it.classes
    Y_pred = model.predict(test_it, STEP_SIZE_TEST)
    Y_pred = model.predict(test_it, STEP_SIZE_TEST)
    y_pred1 = np.argmax(Y_pred, axis=1, out=None)
    cm = confusion_matrix(y_true, y_pred1)

    One possible solution I found was

    y_pred1 = np.where(Y_pred>=0.5, 1, 0)

    However, this is also not matching with the test accuracy though its very close to it.

    kindly guide on this issue.

    Thanks and Regards

    • Avatar
      Jason Brownlee August 16, 2020 at 5:58 am #

      The cause of your issue is not obvious to me, sorry.

  65. Avatar
    nkm August 31, 2020 at 2:58 pm #

    Hi Jason,

    Thanks for your great support.

    I would like to ask for binary classification of Gray-scale images, what should be the activation function in the last layer, sigmoid or softmax?. Theoretically both should give the same results but practically both are not matching. Even test accuracy differs. Kindly guide.

  66. Avatar
    Valentin August 31, 2020 at 9:49 pm #

    Hi Jason,

    I have developed a beautiful time series prediction model based on
    Now my inexperience in machine learning shows: I saved the model and then wanted to make a prediction based on 2 rows of new data.
    “yhat = model.predict(X)”. The result is scaled, right? So I wanted to inverse that by “scaler.inverse_transform(yhat)”.
    I get a “ValueError: non-broadcastable output operand with shape (2,1) doesn’t match the broadcast shape (2,8)”.
    That operand is ‘yhat’, and the expected shape seems to expect all features (count 8) for the inverse_transform function.
    I believe that I am missing something basic, but I don’t know how to fill the gap. Maybe I missed that point in your books and other posts …

    Thanks again!


    • Avatar
      Jason Brownlee September 1, 2020 at 6:30 am #

      Good question.

      To use the transform object to invert scaling, the input to the transform object must have the same shape, e.g. same columns in the same order as when the transform was performed.

  67. Avatar
    Gabriele Simonetta September 24, 2020 at 7:57 pm #

    Good morning,
    I wanted to know how to reprocess and have predictions for data outside the range 0-1,
    A solution regarding the activation function is to use the “relu” function, but anyway, and I don’t understand why, the prediction values are still equal to 1.

    • Avatar
      Jason Brownlee September 25, 2020 at 6:36 am #

      You can use a linear output for your model, then write code to scale the output to any range you require.

  68. Avatar
    Logan October 17, 2020 at 9:11 am #

    Hi Jason,

    Great article! I am using your code with my own dataset, but I have about 70 inputs. The predicted probabilities are much lower than they should be, and I am wondering if you have any suggestions on how to fix this. I am new to neural networks, and I think I may need to change the architecture a fair amount.

  69. Avatar
    HSA October 26, 2020 at 3:23 am #

    why I am getting one output with predict_proba?

    • Avatar
      HSA October 26, 2020 at 5:46 am #

      given that I need both values because I need them as input for a function

    • Avatar
      Jason Brownlee October 26, 2020 at 6:52 am #

      If you provide one sample as input, you will get one output.

  70. Avatar
    HSA November 5, 2020 at 4:26 am #

    I have an estimator that returns two values
    (predicted_labels, log_probs) = create_model( is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)
    the log_probs should return the probability of each class why I got this result
    array([-6.2299333e+00, -1.9715650e-03], dtype=float32)

  71. Avatar
    HSA November 5, 2020 at 7:10 pm #

    yes but I was expected to retrieve the probability of each class [0.70, 0.30] as examples, in which their sum gives 1…

    • Avatar
      Jason Brownlee November 6, 2020 at 5:53 am #

      You will only get the probability for each class if you use a softmax activation in the output layer – e.g. for multiclass classificaton, not binary classificaiton.

  72. Avatar
    Ankit Singh November 28, 2020 at 7:26 am #

    Sir, How we can use ELM and F score for lie detection? Can you please explain or help me in this

  73. Avatar
    Peyman February 24, 2021 at 3:30 pm #

    Hello Jason,
    I have a problem. I have trained 1d-CNN for regression purposes. I have used sigmoid activation function for my output dense layer to bound the results between 0 and 1. But when I want to do the prediction, my model gives accurate results only on the first try. When I use it for the second time the accuracy decreases. for each time I run it next the accuracy falls in a pattern (maybe 2 or 3 percent).
    I have used seeding when I wanted to train my model.
    Any idea what is the source of problem?? Thank you

    • Avatar
      Jason Brownlee February 25, 2021 at 5:24 am #

      Sigmoid activation is not appropriate in the output layer for a regression problem, you must use linear.

      Also, accuracy is not appropriate metric for regression, you must use error, like RMSE or MAE.

      • Avatar
        Peyman February 25, 2021 at 2:16 pm #

        Thanks for your reply. I changed the activation function. but still every time I run the model on the same inputs the results get worst and worst (this time I tested that using R2 and RMSE). only my first run on the model I get good results.

        • Avatar
          Jason Brownlee February 26, 2021 at 4:54 am #

          Perhaps try an alternate data preparation, alternate model configuration or alternate model.

  74. Avatar
    Carlos Meza March 13, 2021 at 1:27 am #

    This is so useful. I was wondering if its possible to calculate the F1 score, precision, and recall from the examples you present here. I’m reproducing your example with my data, but I’ve been struggling to figure out how to obtain these values. I wonder If you could provide a suggestion to this.

    Kind regards,

  75. Avatar
    joshep May 15, 2021 at 2:30 pm #

    what kind of neural network that you use? deep neural network or artificial neural network? i dont know the difference from dnn and ann

  76. Avatar
    Viren May 20, 2021 at 3:44 am #

    I used your code for predictions and every time i run the code i get a different prediction for the same input data, how could i solve this?

  77. Avatar
    Ray August 19, 2021 at 3:55 am #

    Hi Jason,
    I am using keras and tensorflow versions 2.6.0 and I am getting the following error when doing ‘predict_classes’: AttributeError: ‘Sequential’ object has no attribute ‘predict_classes’. Do you know a way to fix this?

    • Avatar
      Adrian Tam August 19, 2021 at 5:42 am #

      In that case, you need to use argmax with the predict result, such as np.argmax(y_pred, axis=1)

  78. Avatar
    Muhammad Putra November 17, 2021 at 3:25 pm #

    hi, i wanna ask you, can we predict the nan / missing value data? if can, how to predict nan/missing value data, thanks

    • Avatar
      Adrian Tam November 18, 2021 at 5:33 am #

      Technically cannot. But if you mean to collect data from somewhere and you want to predict if you will see nan or missing values, then it is a binary classification problem. In that case, you can build a model to predict it

      • Avatar
        Putra November 27, 2021 at 10:46 pm #

        ok adrian, I mean it’s possible to train neural networks with missing data? because I’ve already tried and the result shows loss and the result is also nan

        • Avatar
          Adrian Tam November 29, 2021 at 8:49 am #

          Neural networks are based on differentiable functions and matrix multiplications. Hence no NaN value should be input to the neural network. You either substitute them with some value, or not to use neural network for that.

  79. Avatar
    Putra November 29, 2021 at 1:22 pm #

    Ok, Adrian thanks for your answer, i’ve already run my neural network with ratio of training and testing data is 0.1 (training data) and 0.9 (testing data) and the result showed coefficient correlation is 0.9. Why it can be happen? because all of said if you have a little of amount training data the result must be bad

    • Avatar
      Adrian Tam December 2, 2021 at 12:26 am #

      Not must be bad, but more likely to be bad. In your case, maybe you just being lucky (or the nature of the dataset is beautifully fit).
      To understand what happened, it is useful to think in linear regression (the simplest machine learning model) – if all points are on the same line perfectly, you just need two point from the dataset and you get a perfect model.

      • Avatar
        Muhammad Putra December 11, 2021 at 2:05 pm #

        if we want to predict some NaN values, should we use training data with no NaN values?. Or we can use training data with NaN values?

        • Avatar
          Adrian Tam December 15, 2021 at 5:48 am #

          Make it a binary classification model of whether it is NaN or not.

  80. Avatar
    William Booth December 16, 2021 at 9:31 pm #

    Thank you for this article Jason.

    I am wondering whether it is it possible to print both the prediction that the model makes, as well as the probability that this prediction is achieved.

    Say for example the prediction from the model will be an integer between 75 – 251 – how could I then see the probability of this outcome?

    To generate this initial prediction (75 – 251) the output layer has 1 neuron and no sigmoid/softmax function.

    With there being no sigmoid/softmax function on the last layer it is not normalised to make probability predictions. However, if I do add these, them I’m unable to make the initial prediction.

    I would have guessed it would have been a combination of predict_ and predict_proba, but I have confused myself and I’m not even sure if this is possible?

    Any advice/guidance is much appreciated.

    Thank you

    Dummy example below:

    model1 = Sequential()
    model1.add(Dense(250, input_dim=5))
    model1.compile(loss=’mse’, optimizer=’Adam’), y, validation_split=0.25, epochs=10, batch_size=32, verbose=1)

    Xnew = array([[118,1,1,0,0]])
    ynew = model1(Xnew)

    output: 158.35785

    • Avatar
      Adrian Tam December 17, 2021 at 7:25 am #

      How do you define the probability in this case?

  81. Avatar
    William Booth December 17, 2021 at 8:22 pm #

    Hi Adrian. The probability I would like to define is the likelihood that the output 158.35785 occurs. Is it possible for a model to make the prediction (of 158.35785 in this case) as well as then predict the probability that this occurs?

    • Avatar
      Adrian Tam December 19, 2021 at 1:40 pm #

      I would say the probably is zero. That’s the nature of any continuous variable. But even so, unlike linear regression, neural network does not give you the error margin to its result naturally.

  82. Avatar
    Putra December 21, 2021 at 1:47 am #

    i’ve already tried and the result shows still NaN value, i cannot predict the NaN values

    • Avatar
      Muhammad Putra December 21, 2021 at 1:48 am #

      this message is reply for your message “Make it a binary classification model of whether it is NaN or not.” Before your message, i asked “if we want to predict some NaN values, should we use training data with no NaN values?. Or we can use training data with NaN values?”

  83. Avatar
    Fatimah Aloraini November 24, 2022 at 5:13 am #

    Hello Adrian Tam,
    thank you for such great content,

    I am working on a binary classification problem (IDS with normal and malicious packets), I am using sequential
    model from keras. My question is how can I get the probability prediction for both classes in each single sample for example, a function like:

    predict (x),
    that returns–> [0: (probability for class 0), 1: (probability for class 1)]

    • Avatar
      James Carmichael November 24, 2022 at 7:33 am #

      You are very welcome Fatimah! Please let us know if you have any questions we can help you with!

  84. Avatar
    Fatimah Aloraini November 26, 2022 at 5:33 am #

    Thank you,
    I am working on a binary classification problem (IDS with normal and malicious packets), I am using sequential
    model from keras. My question is how can I get the probability prediction for both classes in each single sample for example, a function like:

    predict (x),
    that returns–> [0: (probability for class 0), 1: (probability for class 1)]

  85. Avatar
    AmBG September 22, 2023 at 6:49 pm #

    Hi @Jason!
    First of all, thank you very much for all your amazing posts!!

    My question is: when your are going to predict a model that has been previosly trained (the final selected model), the input data to predict the output variable has to be preprocessed in the same way that the data used to train (clean outliers, selected important features…)?

    Thank you very much!

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