# Use Keras Deep Learning Models with Scikit-Learn in Python

Last Updated on August 7, 2022

Keras is one of the most popular deep learning libraries in Python for research and development because of its simplicity and ease of use.

The scikit-learn library is the most popular library for general machine learning in Python.

In this post, you will discover how you can use deep learning models from Keras with the scikit-learn library in Python.

This will allow you to leverage the power of the scikit-learn library for tasks like model evaluation and model hyper-parameter optimization.

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.

• May/2016: Original post
• Update Oct/2016: Updated examples for Keras 1.1.0 and scikit-learn v0.18.
• Update Jan/2017: Fixed a bug in printing the results of the grid search.
• Update Mar/2017: Updated example for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
• Update Jun/2022: Updated code for TensorFlow 2.x and SciKeras.

Use Keras deep learning models with scikit-learn in Python
Photo by Alan Levine, some rights reserved.

## Overview

Keras is a popular library for deep learning in Python, but the focus of the library is deep learning models. In fact, it strives for minimalism, focusing on only what you need to quickly and simply define and build deep learning models.

The scikit-learn library in Python is built upon the SciPy stack for efficient numerical computation. It is a fully featured library for general machine learning and provides many useful utilities in developing deep learning models. Not least of which are:

• Evaluation of models using resampling methods like k-fold cross validation
• Efficient search and evaluation of model hyper-parameters

There was a wrapper in the TensorFlow/Keras library to make deep learning models used as classification or regression estimators in scikit-learn. But recently, this wrapper was taken out to become a standalone Python module.

In the following sections, you will work through examples of using the KerasClassifier wrapper for a classification neural network created in Keras and used in the scikit-learn library.

The test problem is the Pima Indians onset of diabetes classification dataset. This is a small dataset with all numerical attributes that is easy to work with. Download the dataset and place it in your currently working directly with the name pima-indians-diabetes.csv (update: download from here).

The following examples assume you have successfully installed TensorFlow 2.x, SciKeras, and scikit-learn. If you use the pip system for your Python modules, you may install them with:

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## Evaluate Deep Learning Models with Cross Validation

The KerasClassifier and KerasRegressor classes in SciKeras take an argument model which is the name of the function to call to get your model.

You must define a function called whatever you like that defines your model, compiles it, and returns it.

In the example below, you will define a function create_model() that creates a simple multi-layer neural network for the problem.

You pass this function name to the KerasClassifier class by the model argument. You also pass in additional arguments of nb_epoch=150 and batch_size=10. These are automatically bundled up and passed on to the fit() function, which is called internally by the KerasClassifier class.

In this example, you will use the scikit-learn StratifiedKFold to perform 10-fold stratified cross-validation. This is a resampling technique that can provide a robust estimate of the performance of a machine learning model on unseen data.

Next, use the scikit-learn function cross_val_score() to evaluate your model using the cross-validation scheme and print the results.

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.

Running the example displays the skill of the model for each epoch. A total of 10 models are created and evaluated, and the final average accuracy is displayed.

In comparison, the following is an equivalent implementation with a neural network model in scikit-learn:

The role of the KerasClassifier is to work as an adapter to make the Keras model work like a MLPClassifier object from scikit-learn.

## Grid Search Deep Learning Model Parameters

The previous example showed how easy it is to wrap your deep learning model from Keras and use it in functions from the scikit-learn library.

In this example, you will go a step further. The function that you specify to the model argument when creating the KerasClassifier wrapper can take arguments. You can use these arguments to further customize the construction of the model. In addition, you know you can provide arguments to the fit() function.

In this example, you will use a grid search to evaluate different configurations for your neural network model and report on the combination that provides the best-estimated performance.

The create_model() function is defined to take two arguments, optimizer and init, both of which must have default values. This will allow you to evaluate the effect of using different optimization algorithms and weight initialization schemes for your network.

After creating your model, define the arrays of values for the parameter you wish to search, specifically:

• Optimizers for searching different weight values
• Initializers for preparing the network weights using different schemes
• Epochs for training the model for a different number of exposures to the training dataset
• Batches for varying the number of samples before a weight update

The options are specified into a dictionary and passed to the configuration of the GridSearchCV scikit-learn class. This class will evaluate a version of your neural network model for each combination of parameters (2 x 3 x 3 x 3 for the combinations of optimizers, initializations, epochs, and batches). Each combination is then evaluated using the default of 3-fold stratified cross validation.

That is a lot of models and a lot of computation. This is not a scheme you want to use lightly because of the time it will take. It may be useful for you to design small experiments with a smaller subset of your data that will complete in a reasonable time. This is reasonable in this case because of the small network and the small dataset (less than 1000 instances and nine attributes).

Finally, the performance and combination of configurations for the best model are displayed, followed by the performance of all combinations of parameters.

Note that in the dictionary param_gridmodel__init was used as the key for the init argument to our create_model() function. The prefix model__ is required for KerasClassifier models in SciKeras to provide custom arguments.

This might take about 5 minutes to complete on your workstation executed on the CPU (rather than GPU). Running the example shows the results below.

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.

You can see that the grid search discovered that using a uniform initialization scheme, rmsprop optimizer, 150 epochs, and a batch size of 10 achieved the best cross-validation score of approximately 77% on this problem.

For a fuller example of tuning hyperparameters with Keras, see the tutorial:

## Summary

In this post, you discovered how to wrap your Keras deep learning models and use them in the scikit-learn general machine learning library.

You can see that using scikit-learn for standard machine learning operations such as model evaluation and model hyperparameter optimization can save a lot of time over implementing these schemes yourself.

Wrapping your model allowed you to leverage powerful tools from scikit-learn to fit your deep learning models into your general machine learning process.

### 250 Responses to Use Keras Deep Learning Models with Scikit-Learn in Python

1. Shruthi June 2, 2016 at 6:38 am #

First, this is extremely helpful. Thanks a lot.

I’m new to keras and i was trying to optimize other parameters like dropout and number of hidden neurons. The grid search works for the parameters listed above in your example. However, when i try to optimize for dropout the code errors out saying it’s not a legal parameter name. I thought specifying the name as it is in the create_model() function should be enough; obviously I’m wrong.

in short: if i had to optimize for dropout using GridSearchCV, how would the changes to your code look?

apologies if my question is naive, trying to learn keras, python and deep learning all at once. Thanks,

Shruthi

• Jason Brownlee June 23, 2016 at 10:20 am #

Great question.

As you say, you simply add a new parameter to the create_model() function called dropout_rate then make use of that parameter when creating your dropout layers.

Below is an example of grid searching dropout values in Keras:

Running the example produces the following output:

I hope that helps

2. Rish June 22, 2016 at 10:01 pm #

Hi Jason,

Thanks for the post, this is awesome. I’ve found the grid search very helpful.

One quick question: is there a way to incorporate early stopping into the grid search? With a particular model I am playing with, I find it can often over-train and consequently my validation loss suffers. Whilst I could incorporate an array of epoch parameters (like in your example), it seems more efficient to just have it stop if the validation accuracy increases over a small number of epochs. Unless you have a better idea?

Thanks again!

• Jason Brownlee June 23, 2016 at 5:30 am #

Great comment Rish and really nice idea.

I don’t think scikit-learn supports early stopping in it’s parameter searching. You would have to rig up your own parameter search and add an early stop clause to it, or consider modifying sklearn itself.

I also wonder whether you could hook-up Kears check-pointing and capture the best parameter combinations along the way to file (checkpoint callback) and allow you to kill the search at any time.

• Rish July 19, 2016 at 12:06 pm #

Thanks for the reply! 🙂 I wonder too!

• Vadim March 13, 2017 at 8:16 am #

Hey Jason,

Awesome article. Did you by any chance find a way to hookup callback functions to grid search? In this case it would be possible to have Tensorboard aggregating and visualizing the grid search outcomes.

• Thanin Wangveerathananon October 25, 2019 at 7:37 pm #

This would be the ideal use case. Is there any example for this use case somewhere?

3. Rishabh August 17, 2016 at 3:13 am #

Hi Jason,

Great Post. Thanks for this.

One problem that i have always faced with training a deep learning model (in H2O as well) is that predicted probability distribution is always flat (as in very less variation in probability across the sample). Any other ML model e.g. RF/GBM is easier to tune and gives good results in most cases. So the my doubt is two fold:

1. Except for lets say image data where CNN might be a good thing to try, in what scenarios should we try to fit a deep learning model.
2. I think the issues that i face with deep learning models is usually due to underfitting. Can you please give some tips on how to tune a deep learning model (other ML models are easier to tune)

Thanks

• Jason Brownlee August 17, 2016 at 9:53 am #

Deep learning is good on raw data like images, text, audio and similar. It can work on complex tabular data, but often feature engineering + xgboost can do better in practice.

Keep adding layers/neurons (capacity) and training longer until performance flattens out.

• Rishabh August 17, 2016 at 7:51 pm #

Thanks. I will try to add more layers and add some regularization parameters as well

• Jason Brownlee August 18, 2016 at 7:16 am #

Good luck Rishabh, let me know how you go.

• Rishabh September 10, 2016 at 11:26 pm #

Hi Jason, I was able to tune the model, thanks to your post although GBM had a better fit. I had included prelu layers which improved the fit.

One question, is there an optimal way to find number of hidden neurons and hidden layer count or grid search the only option?

• Jason Brownlee September 12, 2016 at 8:27 am #

Tuning (grid/random) is the best option I know of Rishabh.

• Rishabh September 12, 2016 at 5:03 pm #

Thanks. Grid search takes a toll on my 16 GB laptop, hence searching for an optimal way.

• Jason Brownlee September 13, 2016 at 8:10 am #

It is punishing. Small grids are kinder on memory.

4. xiao September 17, 2016 at 10:59 pm #

Hi Jason,
Thanks for the post. I’m a beginner about keras, and I met some problems using keras with sk-learn recently. If it’s convenient, could you do me a falvor?

Thank you!!!

• Jason Brownlee September 18, 2016 at 8:01 am #

Ouch. Nothing comes to mind Xiao, sorry.

I would debug it by cutting it back to the simplest possible network/code that generates the problem then find the line that causes the problem, and go from there.

If you need a result fast, I would use the same decompositional approach to rapidly find a workaround.

Let me know how you go.

• xiao September 18, 2016 at 10:43 am #

Thanks a lot, Jason.
I need the result fast if possible.

• xiao September 21, 2016 at 12:22 pm #

Hi, Jason. Got any idea?

• Jason Brownlee September 22, 2016 at 8:06 am #

Yes, I gave you an approach to debug the problem in the previous comment Xiao.

I don’t know any more than that, sorry.

• xiao September 22, 2016 at 4:52 pm #

Thanks, I’ll try it out.

5. Josh September 22, 2016 at 12:12 am #

Jason, Thanks for the tutorial it saved me a lot of time. I am running a huge amount of data on a remote server from shell files. The output of the model is written to an additional shell file in case there is errors. However when I run my code, following how you approach above, it outputs the status of training, i.e. epoch number and accuracy, for every model in gridsearch. Is there a way to suppress this output? I tried to use “verbose=0” as an additional argument both in calling “fit” which created an error and GridsearchCV which did not do anything.

Thanks

• Jason Brownlee September 22, 2016 at 8:17 am #

Great question Josh.

Pass verbose=0 into the constructor of your classifier:

6. Tom October 5, 2016 at 9:09 pm #

Hello Jason,
First of all thank you so much for your guides and examples regarding Keras and deep learning!! Please keep on going 🙂
Question 1
Is it possible to save the best trained model with grid and set some callbacks (for early stopping as well)? I wanted to implement saving the best model by doing
checkpoint=ModelCheckpoint(filepath, monitor=’val_acc’, verbose=0, save_best_only=True, mode=’max’)
grid_result = grid.fit(X, Y, callbacks=[checkpoint])
but TypeError: fit() got an unexpected keyword argument ‘callbacks’
Question 2
Is there a way to visualize the trained weights and literaly seeing the created network? I want to make neural networks a bit more practical instead of only classification. I know you can plot the model: plot(model, to_file=’model.png’) but I want to implement my data in this model.
Tom

• Jason Brownlee October 6, 2016 at 9:37 am #

Hi Tom, I’m glad you’re finding my material valuable.

Sorry, I don’t know about call-backs in the grid search. Sounds like it might just make a big mess (i.e. not designed to do this).

I don’t know about built in ways to visualize the weights. Personally, I like to look at the weights for production models to see if something crazy is going on – but just the raw numbers.

• David August 22, 2017 at 4:46 pm #

Hi Jason,
Thanks for the very well written article! Very clear and easy to understand.
I have a callback that does different types of learning rate annealing. It has four parameters I’d like to optimise.
Based on your above comment I’m guessing the SciKit wrapper won’t work for optimising this?
Do you know how I would do this?
David

• Jason Brownlee August 23, 2017 at 6:41 am #

You might have to write your own for loop David. In fact, I’d recommend it for the experience and control it offers.

7. Soren Pallesen October 18, 2016 at 5:08 pm #

Hi there, thanks for all your inspiration. When running the above example i get a slightly different result:

Best: 0.751302 using {‘optimizer’: ‘rmsprop’, ‘batch_size’: 5, ‘init’: ‘normal’, ‘nb_epoch’: 150}

e.i. init: normal and not uniform as in your example.

Is it normal with these variations?

• Jason Brownlee October 19, 2016 at 9:16 am #

Hi Soren,

Results can vary based on the initialization method. It is hard to predict how they will vary for a given problem.

8. Saddam December 15, 2016 at 9:20 pm #

Hey am getting an error “ValueError: init is not a legal parameter”
code is as follows.
init = [‘glorot_uniform’, ‘normal’, ‘uniform’]
batches = numpy.array([50, 100, 150])
param_grid = dict(init=init)
print(str(self.model_comp.get_params()))
grid = GridSearchCV(estimator=self.model_comp, param_grid=param_grid)
grid_result = grid.fit(X_train, Y_train)

i can’t figure out what am doing wrong. Help me out here.

• Jason Brownlee December 16, 2016 at 5:41 am #

Sorry Saddam, the cause does not seem obvious.

Perhaps post more of the error message or consider posting the question on stack overflow?

• Palash Goyal January 15, 2017 at 11:36 pm #

The error is due to the init parameter ‘glorot_uniform’.
Seems like it has been deprecated or something, once you remove this from the possible values (i.e., init=[‘uniform’,’normal’]) your code will work.

Thanks

• Jason Brownlee January 16, 2017 at 10:41 am #

It does not appear to be deprecated:
https://keras.io/initializations/

• Nils Holgersson January 23, 2017 at 4:11 am #

Don’t forget to add the parameter (related to parameters inside the creation of your model) that you want to iterate over as input parameter to the function that creates your model.

def create_model(init=’normal’):

• Fareed February 8, 2018 at 11:27 pm #

check you have defined the ‘init’ as parameter in the function. if not define it as parameter in the function. once done, when you create the instance of KerasClassifier and call that in the GRidSearchCV(), it will not throw the error.

9. Tameru December 21, 2016 at 9:44 am #

Hi Jason,

recently, I tried to use K-fold cross validation for Image classification problem and found the following error

training Image X shape= (2041,64,64)
label y shape= (2041,2)

code:

model = KerasClassifier(build_fn=creat_model, nb_epoch=15, batch_size=10, verbose=0)

# evaluate using 6-fold cross validation

kfold = StratifiedKFold(n_splits=6, shuffle=False, random_state=seed)

results = cross_val_score(model, x, y, cv=kfold)

print results

print ‘Mean=’,

print(results.mean())

an error:

IndexError Traceback (most recent call last)
in ()
2 # evaluate using 6-fold cross validation
3 kfold = StratifiedKFold(n_splits=6, shuffle=False, random_state=seed)
—-> 4 results = cross_val_score(model, x, y, cv=kfold)
5 print results
6 print ‘Mean=’,

IndexError: too many indices for array

I don’t understand what is wrong here?
Thanks,

• Jason Brownlee December 22, 2016 at 6:29 am #

Sorry Tameru, I have not seen this error before. Perhaps try stack overflow?

• coyan January 5, 2017 at 6:50 pm #

Hi, I have the same problem. Do you have any easy way to solve it?

Thanks!

• Jason Brownlee January 6, 2017 at 9:07 am #

Sounds like it could be a data issue.

Perhaps your data does not have enough observations in each class to split into 6 groups?

Ideas:
– Try different values of k.
– Try using KFold rather than StratifiedKFild
– Try a train/test split

• Tameru January 8, 2017 at 7:04 am #

@ Coyan, I am still trying to solve it. let us try Jason’s advice. Thanks, Jason.

• JiaMingLin January 20, 2017 at 8:47 pm #

Hi

I also encounter this problem, and I guess the scikit-learn k-fold functions do not accept the “one hot” vectors. You may try on StratifiedShuffleSplit with the list of “one hot” vectors.

And that means you can only evaluate Keras model with scikit-learn in the binary classification problem.

10. Ali January 15, 2017 at 7:52 pm #

Dear Dr. Brownlee,

Your results here are around 75%. My experiment with my data result in around 85%. Is this considered good result?

Because DNN and RNN are known for their great performances. I wonder if it’s normal and how we can improve the results.

Regards,

11. Miriam February 3, 2017 at 1:01 am #

Hi Jason,

I cannot tell you how awesome your tutorials are in terms of saving me time trying to understand Keras,

However, I have run into a conceptual wall re: training, validation and testing. I originally understood that wrapping Keras in Gridsearch helped me tune my hyperparameters. So with GridsearchCV, there is no separate training and validation sets. This I can live with as it is the case with any CV.

But then I want to use Keras to predict my outcomes on the model with optimized hyperparameters. Every example I see for model.fit/model.evaluate, uses the argument validation_data (or validation_split) and I’m understand that we’re using our test set as a validation set — a real no no.

Please see https://github.com/fchollet/keras/issues/1753 for a discussion of this and proof that I am not theonly one confused.

SO MY QUESTION IS: In completing your wonderful cookbook how to’s for novices, after I have found all my hyperparameters, how do I run my test data?

If I use model.fit, won’t the test data be unlawfully used to retrain? What exactly is happening with the validation_data or _split argument in model.fit in keras????

• Jason Brownlee February 3, 2017 at 10:05 am #

Hi Miriam,

Generally, you can hold out a separate validation set for evaluating a final model and set of parameters. This is recommended.

Model selection and tuning can be performed on the same test set using a suitable resampling method (k-fold cross validation with repeats). Ideally, separate datasets would be used for algorithm selection and parameter selection, but is often too expensive (in terms available data).

12. wenger March 6, 2017 at 5:12 pm #

i follow the step ，but error

ValueError: optimizer is not a legal parameter

i don’t konw how to deal with it

• Jason Brownlee March 7, 2017 at 9:35 am #

I’m sorry to hear that wenger. Perhaps confirm that you have the latest versions of Keras and sklearn installed?

• Pedro Cadahia February 5, 2018 at 9:19 pm #

Hi wenger, that error is produced because there is an error of coding in the example shown below. you should change the function and pass it an argument called optimizer, like this create_model(optimizer), also in the same function you have to edit the model compile and change the optimized (which is fixed) that is wrong if you want to do a grid search… so a good solution would be change ‘adam’ and write again ‘optimizer=optimizer’, by doing this you would be able to run the code again and find the best solver.

Happy deep learning!

• Xian Jing February 2, 2018 at 12:11 am #

I have met your problem, and I find that maybe you haven’t transmit the optimizer to the function model.

13. myk March 31, 2017 at 9:25 pm #

hello how can i implement SVM machine learning algorithm by using scikit library with keras

• Jason Brownlee April 1, 2017 at 5:54 am #

Keras is for deep learning, not SVM. You only need sklearn.

14. Ronen April 3, 2017 at 10:02 pm #

Hi Jason,

As always very informative and skillfully written post.

In the face of an extremely unbalanced data set, how would pipeline under-sampling pre-processing step, in the example above.

Thanks !

15. Jens April 16, 2017 at 6:54 am #

Hey Jason,

I played around with your code for my own project and encountered an issue that I get different results when using the pipeline (both without standardization)
I posted this also on crossvalidated:
https://stats.stackexchange.com/questions/273911/different-results-for-keras-sklearn-wrapper-with-and-without-use-of-pipline

Can you help me with that?
Thank you

• Jason Brownlee April 16, 2017 at 9:32 am #

Neural networks are a stochastic algorithm that gives different results each time they are run (unless you fix the seed and make everything else the same).

16. Carlton banks April 30, 2017 at 11:15 am #

Could you provide an example in which data generator and fit_generator is being used..

17. Anni May 3, 2017 at 7:59 pm #

Hi Jason,

Thanks for the post, this is awesome. But i’m facing

ValueError: Can’t handle mix of multilabel-indicator and binary

when i set scoring function to precision, recall, or f1 in cross validation. But it works fine if i didn’t set scoring function just like you did. Do you have any easy way to solve it?

Big Thanks!

Here’s the code :
scores2=cross_val_score(model, X_train.as_matrix(), y_train, cv=10, scoring=’precision’)

• Jason Brownlee May 4, 2017 at 8:06 am #

Is this happening with the dataset used in this tutorial?

• Anni May 5, 2017 at 4:57 am #

No, actually its happening with breast cancer data set from UCI Machine Learning. I used train_test_split to split the data into training and testing. But when I tried to fit the model, i got

IndexError: indices are out-of-bounds.

So I tried to modify y_train by following code :

y_train = np_utils.to_categorical(y_train)

Do you have any idea ? i tried to solve this error for a week and still cannot fixed the problem.

• Jason Brownlee May 5, 2017 at 7:34 am #

I believe all the variables in that dataset are categorical.

I expect you will need to use an integer encoding and a one hot encoding for each variable.

• Anni May 5, 2017 at 10:24 pm #

• cass July 30, 2018 at 8:01 am #

Hey Anni

do you figure it out. I am kind of confused, since the output of neural network is probability, it cannot get like precision and recall directly….

18. Edward May 12, 2017 at 7:24 am #

Jason is there something in deep learning like feature_importance in xgboost?
For images it makes no sense though in this case it can be important

• Jason Brownlee May 12, 2017 at 7:52 am #

There may be, I’m not aware of it.

You could use a neural net within an RFE process.

• Edward May 12, 2017 at 7:59 am #

19. Adrian May 26, 2017 at 12:04 am #

Do you know how to save the hyperparameters with the TensorBoard callback?

• Jason Brownlee June 2, 2017 at 11:46 am #

Sorry, I don’t have an example or tutorial.

20. Anisa May 28, 2017 at 6:12 pm #

Hi Jason!

I’m doing grid search with my own scoring function, but I need to get result like accuracy and recall from training model. So, I use cross_val_score with best params that I get from grid search. But then cross_val_score produce different result with best score that I got from grid search. Do you have any idea to solve my problem?

Thanks,

21. Shabran May 30, 2017 at 3:41 pm #

Hi Jason!

I’m doin grid search with my own function as scoring function, but i need to reports other metrics from best param that I got from grid search. So, I’m doin cross validation with best params. But the problem is cross validation produce different result with best score from grid search. The different really significan. Do you have any idea to solve this problem?

Thanks.

• Jason Brownlee June 2, 2017 at 12:33 pm #

Yes, you must re-run the algorithm evaluation multiple times and report the average performance.

22. Szymon June 6, 2017 at 6:52 am #

How could we use Keras with ensembles, let’s take Voting. When base models are from sklearn library everything works fine, but with Keras I’m getting ‘TypeError: cannot create ‘sys.flags’ instances’. Do you know any work around?

• Jason Brownlee June 6, 2017 at 10:09 am #

You can perform voting manually on the lists of predictions from each sub model.

23. Kirana June 14, 2017 at 3:12 pm #

I’m evaluating 3 algorithms (SVM-RBF, XGBoost, and MLP) against small datasets. Is it true if SVM and XGBoost are suitable for small datasets whereas deep learning require “relatively” large datasets to work well? Could you please explain to me?

Thanks a lot.

• Jason Brownlee June 15, 2017 at 8:42 am #

Perhaps, let the results and hard data drive your model selection.

24. James June 17, 2017 at 2:55 am #

Great article. It seems similar to what I am currently learning about. I’m using the sklearn wrapper for Keras; particularly the KerasClassifier and sklearn’s cross_val_score(). I am running into an issue with the n_jobs parameter for cross_val_score. I’d like to take advantage of the GPU on my Windows 10 machine, but am getting Blas GEMM launch failed when n_jobs = -1 or any value above 1. Also getting different results if I run it from the shell prompt vs. the python interpreter. Any ideas what I can do to get this to work?

Windows 10
Geforce 1080 Ti
Tensorflow GPU
Python 3.6 via Anaconda
Keras 2.0.5

• Jason Brownlee June 17, 2017 at 7:33 am #

Sorry James, I don’t have good advice on setting up GPUs on windows for Keras.

Perhaps you can post on stackoverflow?

25. Anastasios Selalmazidis June 18, 2017 at 2:17 am #

Hello Jason,

when I run your first example, the one which uses StratifiedKFold, with a multiclass dataset of mine, I get an errorIsn’t it possible to run StratifiedKFold with multiclass ? I also have the same problem “”IndexError: too many indices for array”. when try to run a GridSearch with StritifiedKFold

• Jason Brownlee June 18, 2017 at 6:32 am #

Try changing it from StratifiedKFold to KFold.

26. Sean June 27, 2017 at 5:24 pm #

Hi Jason,
I follow your steps and the program takes more than 40 minutes to run. Therefore, I gave up waiting in the middle. Do you know if there is any way to speed up the GridSearchCV? Or is it normal to wait more than 40 minutes to run this code on a 2013 Mac? Thank you

• Jason Brownlee June 28, 2017 at 6:18 am #

You could cut down the number of parameters that are being searched.

I often run grid searches that run for weeks on AWS.

27. Clarence Wong June 27, 2017 at 8:31 pm #

Hi Jason,

How do I save the model when wrapping a Keras Classifier in Scikit’s GridSearchCV?

Do I treat it as a scikit object and use pickle/joblib or do I use the model.save method native to Keras?

• Jason Brownlee June 28, 2017 at 6:23 am #

Sorry, I have not tried this. It may not be supported out of the box.

28. Karthik July 15, 2017 at 11:39 am #

Hi Jason,

I want to perform stratified K-fold cross-validation with a model that predicts a distance and not a binary label. Is it possible to provide a distance threshold to the cross-validation method in scikit-learn (or is there some other approach), i.e., distance < 0.5 to be treated as a positive label (y=1) and a negative label (y=0) otherwise?

• Jason Brownlee July 16, 2017 at 7:56 am #

Stratification requires a class value.

Perhaps you can frame your problem so the outcome distances are thresholded class labels.

Note, this is a question of how you frame your prediction problem and prepare your data, not the sklearn library.

29. Merco July 15, 2017 at 11:12 pm #

Very cool!! And you can use Recursive Feature Elimination sklearn function by this way too?

30. ambika July 19, 2017 at 4:33 pm #

Please could you tell me formula for relu function , i need it for regression.

31. ds July 21, 2017 at 7:34 pm #

Is it possible to use your code with more complex model architectures? I have 3 different inputs and branches (with ConvLayers), which are concatenated at the end to form a dense layer.
I tried to call the grid.fit() function as follows:
grid_result = grid.fit({‘input_1’: train_input_1, ‘input_2’: train_input_2, ‘input_3’: train_input_3}, {‘main_output’: lebels_train})

I’m getting an error:
“ValueError: found input variables with inconsistent numbers of samples: [3, 1]”

Do you have any experience on that?

• Jason Brownlee July 22, 2017 at 8:32 am #

I don’t sorry.

I would recommend using native Keras.

32. Nas October 10, 2017 at 4:23 pm #

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy

def create_model():
# create model
model = Sequential()
# Compile model
return model

seed = 7
numpy.random.seed(seed)
X = dataset[:,0:41]
Y = dataset[:,41]

model = KerasClassifier(build_fn=create_model, epochs=150, batch_size=10, verbose=0)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())

Using TensorFlow backend.
Traceback (most recent call last):
File “nsl2.py”, line 20, in
File “/home/nasrin/.local/lib/python3.5/site-packages/numpy/lib/npyio.py”, line 1024, in loadtxt
items = [conv(val) for (conv, val) in zip(converters, vals)]
File “/home/nasrin/.local/lib/python3.5/site-packages/numpy/lib/npyio.py”, line 1024, in
items = [conv(val) for (conv, val) in zip(converters, vals)]
File “/home/nasrin/.local/lib/python3.5/site-packages/numpy/lib/npyio.py”, line 725, in floatconv
return float(x)
ValueError: could not convert string to float: b’tcp’
sorry to bother you, what step i missed here.

33. Piotr October 12, 2017 at 10:33 pm #

Do you know, how to do K.clear_session() inside cross_val_score(), between folds?

I have huge CNN networks, but they fits in my memory when I do just one training. The problem is, when I do cross-validation using sci-kit learn and cross_val_score function. I see that memory is increasing with each fold. Do you know hot to change that? After all, after each fold we have to remember just results, not huge model with all weights.

I’ve tried to use on_train_end callback from keras, but this doesn’t work as model is wiped out before evaluating. So do you know if exists other solution? Unfortunately I don’t see any callbacks in cross_val_score function…

34. Sanaz October 25, 2017 at 10:17 pm #

So many thanks for such a helpful code! I have a problem, despite defining the init both in the model and the dictionary in the same way that you have defined it here, I get an error:

‘{} is not a legal parameter’.format(params_name))

ValueError: init is not a legal parameter

35. Ab November 9, 2017 at 9:31 pm #

Hi,
I have tried to use AdaBoostClassifier( model, n_estimators=2, learning_rate=1.5, algorithm=”SAMME”)
and used CNN as ‘model’. However I get the following error:

File “”, line 1, in

File “/usr/local/lib/python2.7/dist-packages/spyder/utils/site/sitecustomize.py”, line 688, in runfile
execfile(filename, namespace)

File “/usr/local/lib/python2.7/dist-packages/spyder/utils/site/sitecustomize.py”, line 93, in execfile
builtins.execfile(filename, *where)

bdt_discrete.fit(X_train, y_train)

File “/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.py”, line 413, in fit

File “/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.py”, line 130, in fit
self._validate_estimator()

File “/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.py”, line 431, in _validate_estimator
% self.base_estimator_.__class__.__name__)

ValueError: KerasClassifier doesn’t support sample_weight.

36. Arooj December 15, 2017 at 5:14 pm #

Hi,
I am new to keras. Just copied the above code with grid and executed it but getting this error:

“ValueError: init is not a legal parameter”

• Jason Brownlee December 16, 2017 at 5:24 am #

Double check that you copied all of the code with the same spacing.

Also double check you have the latest version of Keras and sklearn installed.

37. michele December 18, 2017 at 10:20 pm #

Hi, great tutorial, so thanks!

I have a question: if I want to use KerasClassifier with my own score function, let’s say maximizing F1, instead of accuracy, in a grid search scenario or similar.
What should I do?

Thanks!

• Jason Brownlee December 19, 2017 at 5:19 am #

You can specify the scoring function used by sklearn when evaluating the model with CV or what have you, the “scoring” attribute.

• michele December 20, 2017 at 12:50 am #

Thx for the answer. But, if I understood correctly, the score function of kerasclassifier must be differentiable, since it is used also as loss function, and F1 is not.

• Jason Brownlee December 20, 2017 at 5:46 am #

No, that is the loss function of the keras model itself, a different thing from the sklearn’s evaluation of model predictions.

38. Ankur Singh December 19, 2017 at 3:24 pm #

Hi Jason, your blog is amazing. I have your Deep Learning book as well.

I have a question: I don’t want the stratified KFold in my code. I have my own validation data. Can I train my model on a given data and check the best scores on a different validation data using Grid Search?

39. Claudio January 3, 2018 at 2:11 am #

HI Jason…
Thanks so much for for this set of article on Keras.. I thinks it’s simply awesome

I have a question on what is the best way to run the procedure about Grid Search Deep Learning Model Parameters on a Spark cluster. Is it just a matter to port the code there without any change and the optimization is magically enabled ( run in parallel on different node any combination of parameter to test ) …. or should we import other libraries or apply some changes for enabling that?

• Jason Brownlee January 3, 2018 at 5:39 am #

Sorry, I cannot give you good advice for tuning Keras model with Spark.

40. vikram singh January 4, 2018 at 11:29 pm #

How to cross validation when we have multi label classification problem ?
Whenever I pass the Y_train, I get ‘IndexError: too many indices for array’, how to resolve this ?

41. Sam Miller January 5, 2018 at 6:56 am #

Hi Jason,
How do you obtain the precision and recall scores from the model when using k-folds with KerasClassifier? Is there a method of generating the sklearn.metrics classification report after applying cross_val_score?
I need these values as my dataset is imbalanced and I want to compare the results from before and after undersampling the data by generating a confusion matrix, ROC curve and precision-recall curve.

• Jason Brownlee January 5, 2018 at 11:35 am #

Yes, change the “scoring” argument to one or a list of metrics to report.

42. yerra January 8, 2018 at 2:47 am #

Hi Brownlee ,

Thanks for wonderful material for Machine Learning and Deep Learning. As suggested I have dataset which already split into training and test. Using Keras , How to train model and then predict the model on test data . ?

43. Reed Guo January 21, 2018 at 1:13 am #

Hi, Jason

How to find the best number of hidden layers and number of neurons?

Can you post the python code? I didn’t find any useful posts by Google.

Thank you very much.

• Jason Brownlee January 21, 2018 at 9:12 am #

Great question.

You must use trial and error with your specific model on your specific data.

44. Reed Guo January 21, 2018 at 7:23 pm #

Hi, Jason

I still feel confused. I don’t know how to do? (use grid search or just try and try again?)

Can you provide a clip of python code for the example in this course?

45. Reed Guo January 23, 2018 at 12:19 pm #

Hi, Jason

Thank you very very much.

46. Magesh Rathnam February 2, 2018 at 3:48 am #

Hi Jason – thanks for the post. I was not aware of this wrapper. Do we have a similar wrapper for regressor too? I am new to ML and I was trying to do house price prediction problem. I see that it can be done with scikit random forest regressors. But I want to see if we can do the same with keras as well, since I started with keras and find it little easier. I tried with a simple sequential model, with multiple layers, but it did not work. Can you pls let me know how can I implement keras for my problem?

Thanks.

• Jason Brownlee February 2, 2018 at 8:22 am #

Yes, there is a wrapper for regression.

47. Atefeh February 6, 2018 at 12:28 am #

hello

I face to the error:

what is the problem?

• Jason Brownlee February 6, 2018 at 9:18 am #

You may not have it imported?

48. Atefeh February 6, 2018 at 5:35 pm #

hello

again after importing Kerasclassifier, for Kfold what should i import?

from keras.wrappers.scikit_learn import KerasClassifier
def create_model():
model=Sequential()

model.compile(…)
return model
model=KerasClassifier(build_fn=create_model, epochs=150, batch_size=10)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)

NameError Traceback (most recent call last)
in ()
6 return model
7 model=KerasClassifier(build_fn=create_model, epochs=150, batch_size=10)
—-> 8 kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)

NameError: name ‘StratifiedKFold’ is not defined

thank you

• Jason Brownlee February 7, 2018 at 9:22 am #

Looks like you are still missing some imports.

Consider using existing code from the blog post as a starting point?

49. K V Subrahmanyam February 13, 2018 at 8:41 pm #

Hi

Thanks for the brilliant post.I have one question. Why are we not passing epochs and batches as parameter to create_model function. Can you please helping me in understanding this?

Regards
Subbu

• Jason Brownlee February 14, 2018 at 8:18 am #

Because we are passing them when we fit the model. The function is only used to define the model, not fit it.

50. Janina February 18, 2018 at 9:27 am #

Hi Jason, even though your post seems very straight-forward, still I struggle to implement this grid search approach. It does not give me any error message, BUT it just runs on and on forever without printing out anything. I deliberately tried it with very few epochs and very few hyperparameters to search. Without grid search, one epoch runs through extremely fast, so I don’t think that I just need to give it more time. It simply doesn’t do anything.

My code:

Thanks a lot for any help!!

• Jason Brownlee February 19, 2018 at 8:59 am #

Perhaps try using a smaller sample of your data?

Perhaps perform the grid search manually with your own for loop? or distributed across machines?

• starsini January 12, 2020 at 1:57 pm #

How about changing verbose to verbose=9

model = KerasClassifier(build_fn=create_model, verbose=9)

• Jason Brownlee January 13, 2020 at 8:17 am #

Not familiar with that setting. What does it do?

51. Rishab Verma March 2, 2018 at 5:33 pm #

Nothing can be more appreciated than looking at the replies that you have made to other people’s issues.

I also have an issue. I want to train a SVM classifier on the weights that I have already saved of a model that used fully connected layer of a ResNet. How do I feel in the values to the SVM classifier?
Any guidance is appreciated.

• Jason Brownlee March 3, 2018 at 8:07 am #

Sorry, I don’t know how to load neural net weights into an SVM.

52. Choi March 6, 2018 at 2:09 pm #

Hi Jason,
Is there any other ways not to use ‘KerasClassifier’ to fit the model?
Because I want to train the model with augmented data, which is achieved by using ‘ImageDataGenerator’.

More specifically,

You can change argument X_train, and Y_train with ‘fit()’ function as written below.

history = model.fit(X_train, Y_train, nb_epoch=nb_epoch, batch_size=batch_size, shuffle=True, verbose=1)

However, you can’t change argument X_train, and Y_train using ‘KerasClassifier’ function as written below, because there are no arguments for input data in this function.

model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=1, verbose=0)

53. Elena Markova April 19, 2018 at 1:42 am #

Hi Jason,

Thank you for the article
Can you explain how the kfold cross validation example with scikit is different from just using validation split = 1/k in keras, when fitting a model? Sorry, Im new to machine learning. Is the only difference is that the validation split opton in keras never changes or shuffles its validation data?

• Jason Brownlee April 19, 2018 at 6:36 am #

A validation split is a single split of the data. One model evaluated on one dataset.

k-fold cross-validation creates k models evaluated on k disjoint test sets.

It is a better estimate of the skill of the model trained on a random sample of data of a given size. But comes at an increased computational cost, especially for deep learning models that are really slow to train.

54. Saber May 27, 2018 at 12:45 am #

I have read in a few places that k-fold CV is not very common with DL models as they are computationally heavy. But k-fold CV is also used to pick an “optimal” decision/classification threshold for a desired FPR, FNR, etc. before going to the test data set. Do you have any suggestions on how to do “threshold optimization” with DL models? Is k-fold CV the only option?

Thanks!

• Jason Brownlee May 27, 2018 at 6:46 am #

Makes sense.

Yes, each split you can estimate the threshold to use from the train data and test it on the hold out fold.

55. Sazid May 27, 2018 at 8:59 pm #

Which one give me good result keras scikit learn wraper class or general machine learning algorithms like as KNN , SVM,decession tree , random forest tree?

• Jason Brownlee May 28, 2018 at 5:58 am #

scikit-learn provides those algorithms.

56. Mik June 2, 2018 at 1:12 am #

Hey Jason,

I’ve followed your blog for some time now and I find your posts on ML very useful, as it sort of fills out some of the holes that many documentation sites leave out. For example, your posts on Keras are lightyears ahead of Keras’ own documentation in terms of clarity. So, please keep up the fantastic work mate.

I have run into a periodic problem when using Keras. Far from every time, but occasionally (I’d say 1 in every 10 times or so), the code fails with this error:

Exception ignored in: <bound method BaseSession.__del__ of >
Traceback (most recent call last):
File “/home/mede/virtualenv/lib/python3.5/site-packages/tensorflow/python/client/session.py”, line 707, in __del__

I believe it’s the same error that these stackoverflow posts describe: https://stackoverflow.com/questions/40560795/tensorflow-attributeerror-nonetype-object-has-no-attribute-tf-deletestatus and https://github.com/tensorflow/tensorflow/issues/3388 Naturally, I have tried to apply the fixes that those posts suggest. However, since (as far as I can see) the error occurs within the loop that executes the grid search I cannot delete the Tensorflow session between each run. Do you know if there is, or can you think of, a workaround for this?

• Jason Brownlee June 2, 2018 at 6:35 am #

I have not seen this before sorry.

Perhaps ensure that all libs and dependent libs are up to date?
Perhaps move to Py 3.6?

57. Mik June 4, 2018 at 7:27 am #

OK, I’ll give it a shot, thanks.

58. Chase July 11, 2018 at 6:33 am #

Thanks for the great content!

I’m looking for some guidance on building learning curves that specifically show the training and test error of a sequential keras model as the training examples increases. I’ve put together some code already and have gotten hung up on an error that is making me rethink my approach. Any suggestions would be appreciated.

For reference this is the current error:

TypeError: Cannot clone object ” (type ): it does not seem to be a scikit-learn estimator as it does not implement a ‘get_params’ methods.

• Jason Brownlee July 11, 2018 at 2:52 pm #
• Chase July 12, 2018 at 4:34 am #

Thanks Jason. I had actually seen that already and it was helpful. Since I’m looking at how the losses change as the number of training examples (not number of epochs) increase I need to train on several subsets of data. In your example mentioned, would you simply loop over what you have while passing the subsetted data? I’ve been trying to use scikit learns learning_curve function while creating a scorer to be used with the sequential model I’m passing.

Got a post at stack overflow if interested.

https://stackoverflow.com/questions/51291980/using-sequential-model-as-estimator-in-learning-curve-function

59. cass July 30, 2018 at 7:58 am #

Hi Jason

the first example of using KerasClassifier to do cross validation is only for accuracy. Is there any way to get f1 score or recall.

The neural network always return the probabilities. so for the binary classification, how the cross_val_score works(I tried, it does not work)

• Jason Brownlee July 30, 2018 at 2:14 pm #

Yes, you can specify any scoring metric you prefer.

Set the “scoring” argument to cross_val_score().

60. Huda August 31, 2018 at 7:48 pm #

Hi Jason,

How about if I have multiple models trained on same data. Can you give an example of using VotingClassifier with keras models??

Thaks!

61. tequila rifa September 5, 2018 at 9:47 pm #

Hi Jason,

I’ve learnt a lot from your website and books. I really thank you for your help.

An observation I’ve made, looking inside the Ebook: Deep Learning With Python and other books from you.

most / nearly all chapters + code is available on your website as separate blogs,

62. reaky December 20, 2018 at 12:27 am #

Hi Jason,
Traceback (most recent call last):
File “H:/pycharm_workspace/try_process/model1.py”, line 61, in
model_change.fit(X_train,Y_train)
File “J:\python_3.6\lib\site-packages\keras\wrappers\scikit_learn.py”, line 210, in fit
return super(KerasClassifier, self).fit(x, y, **kwargs)
File “J:\python_3.6\lib\site-packages\keras\wrappers\scikit_learn.py”, line 139, in fit
**self.filter_sk_params(self.build_fn.__call__))
TypeError: __call__() missing 1 required positional argument: ‘inputs’
what is the error meaning?

63. bileesh January 10, 2019 at 6:08 pm #

Hi Jason,

Is it possible to wrap SVM classifier in scikit with a Keras CNN feature extraction module?

• Jason Brownlee January 11, 2019 at 7:42 am #

The outputs of a CNN (features) can be fed to an SVM.

• bileesh January 11, 2019 at 4:36 pm #

Thank You, Jason.

Instead of extracting the features using CNN and then classifying using SVM, is it possible to replace the fully connected layers of CNN with SVM module available in scikit learn and to perform an end-to-end training?

• Jason Brownlee January 12, 2019 at 5:37 am #

Directly? Perhaps, I have not tried.

64. jessy February 1, 2019 at 9:03 am #

sir,
Which anaconda prompt is best for executing the grid search hyper parameter….for best and execute fast

65. wybiu February 23, 2019 at 1:59 pm #

sir: I use keras api to adaboost LSTM in scikit=learn.but I got some bugs
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from keras.layers.core import Dense, Activation, Dropout
from keras.models import Sequential
import time
import math
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras import optimizers
import os
from matplotlib import pyplot
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn import ensemble
from sklearn.tree import DecisionTreeRegressor

def create_dataset(dataset, look_back):
dataX, dataY = [], []
for i in range(len(dataset)-look_back):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)

def create_train_test_data(dataset,look_back ):
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
datasetX, datasetY = create_dataset(dataset, look_back)
train_size = int(len(datasetX) * 0.90)
trainX, trainY, testX, testY = datasetX[0:train_size,:],datasetY[0:train_size],datasetX[train_size:len(datasetX),:],datasetY[train_size:len(datasetX)]
trainXrp = trainX.reshape (trainX.shape[0],look_back,1)
testXrp = testX.reshape(testX.shape[0], look_back,1)
return scaler,trainXrp,trainY,testXrp,testY,trainX,testX

def build_model(): #layers [1,50,100,1]
model = Sequential()

start = time.time()
rmsprop = optimizers.rmsprop(lr=0.001)
#model.compile(loss=”mse”, optimizer=rmsprop)
model.compile(loss=”mse”, optimizer=”rmsprop”)
print(“Compilation Time : “, time.time() – start)
return model

if __name__==’__main__’:
global_start_time = time.time()
dataset = dataframe.values
dataset = dataset.astype(‘float32’)
scaler,trainXrp,trainY,testXrp,testY,trainX,testX = create_train_test_data(dataset,seq_len)
LSTM_estimator = KerasRegressor(build_fn=build_model, nb_epoch=2)
trainXrp = [np.concatenate(i) for i in trainXrp]
boosted_LSTM.fit(trainXrp, trainY)
trainPredict=boosted_LSTM.predict(testXrp)

————————————————————————————————————————
—————————————————————————
ValueError Traceback (most recent call last)
in
102 trainZ = trainX.reshape (trainX.shape[0],1)
103 print(‘Xrp_train shape:’,trainZ.shape)
–> 104 boosted_LSTM.fit(trainZ, trainY.ravel())
105 trainPredict=boosted_LSTM.predict(trainX)

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in fit(self, X, y, sample_weight)
958
959 # Fit
–> 960 return super(AdaBoostRegressor, self).fit(X, y, sample_weight)
961
962 def _validate_estimator(self):

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in fit(self, X, y, sample_weight)
141 X, y,
142 sample_weight,
–> 143 random_state)
144
145 # Early termination

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in _boost(self, iboost, X, y, sample_weight, random_state)
1017 # Fit on the bootstrapped sample and obtain a prediction
1018 # for all samples in the training set
-> 1019 estimator.fit(X[bootstrap_idx], y[bootstrap_idx])
1020 y_predict = estimator.predict(X)
1021

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/wrappers/scikit_learn.py in fit(self, x, y, **kwargs)
150 fit_args.update(kwargs)
151
–> 152 history = self.model.fit(x, y, **fit_args)
153
154 return history

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
950 sample_weight=sample_weight,
951 class_weight=class_weight,
–> 952 batch_size=batch_size)
953 # Prepare validation data.
954 do_validation = False

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
675 # to match the value shapes.
676 if not self.inputs:
–> 677 self._set_inputs(x)
678
679 if y is not None:

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/training.py in _set_inputs(self, inputs, outputs, training)
587 assert len(inputs) == 1
588 inputs = inputs[0]
–> 589 self.build(input_shape=(None,) + inputs.shape[1:])
590 return
591

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/sequential.py in build(self, input_shape)
219 self.inputs = [x]
220 for layer in self._layers:
–> 221 x = layer(x)
222 self.outputs = [x]
223 self._build_input_shape = input_shape

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
530
531 if initial_state is None and constants is None:
–> 532 return super(RNN, self).__call__(inputs, **kwargs)
533
534 # If any of initial_state or constants are specified and are Keras

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
412 # Raise exceptions in case the input is not compatible
413 # with the input_spec specified in the layer constructor.
–> 414 self.assert_input_compatibility(inputs)
415
416 # Collect input shapes to build layer.

/usr/local/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
309 self.name + ‘: expected ndim=’ +
310 str(spec.ndim) + ‘, found ndim=’ +
–> 311 str(K.ndim(x)))
312 if spec.max_ndim is not None:
313 ndim = K.ndim(x)

ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=2

• Jason Brownlee February 24, 2019 at 9:04 am #

Sorry, I don’t have the capacity to debug your code.

Perhaps post on stackoverflow?

• Carolina June 9, 2021 at 9:14 pm #

Hi!

Did you manage to find the solution? I am in the same situation.

Thank you.

66. danielprasaja March 8, 2019 at 7:51 pm #

hello jason,i just read few of your article, but all of that is use simple keras model. I want to know if it is possible to run crossvalidation on complex model like CNN ?

67. abhijit April 20, 2019 at 12:16 am #

Hello Jason,

One question here i am trying to find the best parameters with grid search using keras model but my scoring in auc , how to do that as keras classifiers only allows accuracy as scoring

• Jason Brownlee April 20, 2019 at 7:41 am #
• abhijit April 20, 2019 at 4:40 pm #

i want the best model with gridsearch and with scoring as AUC , i tried this code but it is not working , Can you just review and help where am going wrong.

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import roc_auc_score
import tensorflow as tf
from keras.utils import np_utils
from keras.callbacks import Callback, EarlyStopping

# define roc_callback, inspired by https://github.com/keras-team/keras/issues/6050#issuecomment-329996505
def auc_roc(y_true, y_pred):
# any tensorflow metric
value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)

# find all variables created for this metric
metric_vars = [i for i in tf.local_variables() if ‘auc_roc’ in i.name.split(‘/’)[1]]

# Add metric variables to GLOBAL_VARIABLES collection.
# They will be initialized for new session.
for v in metric_vars:

# force to update metric values
with tf.control_dependencies([update_op]):
value = tf.identity(value)
return value

def build_classifier(optimzer,kernel_initializer):
classifier = Sequential()
classifier.add(Dense(6, input_dim = 11, kernel_initializer = kernel_initializer, activation = ‘relu’ ))
classifier.add(Dense(6, kernel_initializer = kernel_initializer, activation = ‘relu’ ))
classifier.add(Dense(1, kernel_initializer = kernel_initializer, activation = ‘sigmoid’ ))
classifier.compile(optimizer = optimzer, loss = ‘binary_crossentropy’, metrics = [auc_roc])
return classifier

classifier = KerasClassifier(build_fn = build_classifier)
my_callbacks = [EarlyStopping(monitor= auc_roc, patience=300, verbose=1, mode=’max’)]
parameters = {‘batch_size’: [25, 32],
‘nb_epoch’: [100, 300],
‘kernel_initializer’:[‘random_uniform’,’random_normal’]}
grid_search = GridSearchCV(estimator = classifier,
param_grid = parameters,
scoring = auc_roc,
cv = 5)
grid_search = grid_search.fit(X_train, y_train,callbacks=my_callbacks)
best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_

68. Mustafa May 24, 2019 at 3:49 am #

Thank You,Dr Jason.

I want to know how to combine an LSTM with SVM with the sequential model of Keras, if possible to give us a simple example without execution. thank you in advance Dr Jason.

69. John June 21, 2019 at 4:28 pm #

I’m building an estimator where some of the parameters are callables. Some of the callables have there own parameters. Using the example above, the estimator has an EarlyStop callable as a parameter. The EarlyStop callable takes a patience parameter. The patience parameter is NOT a parameter of the estimator.

If one wanted to do a GridsearchCV over various values for the patience parameter, is scikit learn equipped to report callables and their parameters among the ‘best_parameters_’?

• Jason Brownlee June 22, 2019 at 6:33 am #

You would have to implement your own for-loops for the search I believe.

70. Emi August 30, 2019 at 10:34 am #

Hi Jason,

First of all thank you for the great tutorial.

I have about 1000 nodes dataset where each node has 4 time-series. Each time series is exactly 6 length long.The label is 0 or 1 (i.e. binary classification).

More precisely my dataset looks as follows.

node, time-series1, time_series2, time_series_3, time_series4, Label
n1, [1.2, 2.5, 3.7, 4.2, 5.6, 8.8], [6.2, 5.5, 4.7, 3.2, 2.6, 1.8], …, 1
n2, [5.2, 4.5, 3.7, 2.2, 1.6, 0.8], [8.2, 7.5, 6.7, 5.2, 4.6, 1.8], …, 0
and so on.

I am using the below mentioned LSTM model.

model = Sequential()

Since, I have a small dataset I would like to perform 10-fold cross-validation as you have suggested in this tutorial.

However, I am not clear what is meant by batch-size of KerasClassifier.

Could you please let me know what batch size is, and what batch size do you recommend me to have?

Cheers, Emi

• Jason Brownlee August 30, 2019 at 2:15 pm #

If they are time series, then cross validation would not be valid, instead, you must used walk forward validation.

https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/

You can get started with LSTMs for time series forecasting here:
https://machinelearningmastery.com/start-here/#deep_learning_time_series

• Emi August 30, 2019 at 3:02 pm #

Hi Jason, Thanks a lot for the response. I went through your “backtest-machine-learning-models-time-series-forecasting” tutorial. However, since my problem is not time-series forecasting, I am not clear how to apply these techniques to my problem.

More specifically, I have a binary classification problem of detecting likely to be trendly goods in future. For that, I am caluculating the following four features;
– timeseries 1: how degree centrality of each good changed from 2010-2016
– timeseries 2: how betweenness centrality of each good changed from 2010-2016
– timeseries 3: how closeness centrality of each good changed from 2010-2016
– timeseries 4: how eigenvector centrality of each good changed from 2010-2016

The label value (0 or 1) is based on the trendy goods on 2017-2018. If the item was trendy it was marked as 1, if not 0.

So, I am assuming that my problem is more like sequence classification. In that case, can I use 10-fold cross validation?

I look forward to hearing from you.

Cheers -Emi

• Jason Brownlee August 31, 2019 at 6:02 am #

Yes, it sounds like a time series classification, and the notion of walk-forward validation applies just as well. That is, you must only ever fit on the past and predict on the future, never shuffle the samples.

I give examples of walk forward validation for time series classification here:
https://machinelearningmastery.com/start-here/#deep_learning_time_series

• Emi August 31, 2019 at 10:14 pm #

Hi Jason, I thought a lot about what you said.

However, I feel like the data in my problem setting is divided by point-wise in the cross-validation, but not time-wise.

i.e. (point-wise)

1st fold training:
item_1, item2, …….., item_799, item_800

1st fold testing:
item 801, …….., item_1000
not (time-wise)

1st fold training:
2010, 2011, …….., 2015

1st fold testing:
2016, …….., 2018

Due to this reason I am not clear how to use walk forward validation.

May be, I provided you a little description about my problem setting. So, I have expanded my problem setting and posted it as a Stackoverflow question: https://stackoverflow.com/questions/57734955/how-to-use-lstm-for-sequence-classification-using-kerasclassifier/57737501#57737501

I hope, my problem is clearly described in the stackoverflow question.

Please let me know your thoughts. I am still new to this area. So, I would love to get your feedback to improve my solution. Looking forward to hearing from you.

Thank you very much

🙂

Cheers -Emi

• Jason Brownlee September 1, 2019 at 5:44 am #

Well, you are the expert on your problem, not me, so if you think it’s safe, go for it!

Let me know how you go.

• Emi August 31, 2019 at 10:16 pm #

Sorry, the link to the stackoverfow question should be: https://stackoverflow.com/questions/57734955/how-to-use-lstm-for-sequence-classification-using-kerasclassifier

• Minesh Jethva May 22, 2020 at 3:41 pm #

Thanks

71. Suyash August 31, 2019 at 9:12 pm #

Plz help how the extracted feature from cnn can be fed into svm is there any code then plz let me know

72. Ulises García September 28, 2019 at 8:28 am #

Hi Jason, greetings, good article
you can help me?

how to use .predict to calculate f1 score “weighted” or how calculate f1 score “weighted”

73. Dima January 26, 2020 at 1:32 am #

Hi Jason,
Thank you for your great work. I was wondering if you have something regarding the coupling of CNN’s used for image recognition with normal features. Could you help?

74. Mehreen Tariq March 27, 2020 at 7:46 pm #

I have implemented same code, but the achieved accuracy is 32%.
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
dataset = dataframe.values
X = dataset[:,0:4].astype(float)
Y = dataset[:,4]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y)
# define baseline model
def baseline_model():
# create model
model = Sequential()
model.add(Dense(4, input_dim=4, kernel_initializer=’normal’ , activation= ‘relu’ ))
model.add(Dense(3, kernel_initializer=’normal’ , activation= ‘sigmoid’ ))
# Compile model
model.compile(loss= ‘categorical_crossentropy’ , optimizer= ‘adam’ , metrics=[ ‘accuracy’ ])
return model
estimator = KerasClassifier(build_fn=baseline_model, nb_epoch=200, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print(“Accuracy: %.2f%% (%.2f%%)” % (results.mean()*100, results.std()*100))

75. jr April 3, 2020 at 8:08 pm #

Hi, thanks for your tutos ! They are very useful.
I’m struggling though …
I work with regression problem and the accuracy metrics parameter is not the best in my case.
Instead, I’d like to use the mean square parameter as the metrics in a grid search (using sci-kit).
Though I can’t find where to precise this
My guess would be either in the definition of the KerasClassifier or GridSearchCV ?
Thank you very much !

• Jason Brownlee April 4, 2020 at 6:18 am #

Good question.

The keras compile() function would specify ‘mse’ as the loss.

The scikit-learn library would specify ‘neg_mean_squared_error’.

76. lavanya June 1, 2020 at 10:49 pm #

sir, kindly tell pros and cons of incorporating Neural Network and Regression

the created model will be a neural network model, regression model or deep network model ?

• Jason Brownlee June 2, 2020 at 6:14 am #

Neural nets can be effective for regression as they can learn complex, discontiguous, and non-linear relationships between inputs and the target as a continuous function.

77. Basma July 15, 2020 at 8:52 am #

Hi Jason,
Thanks a lot for your posts. When i executed i met an error.

RuntimeError: Cannot clone object , as the constructor either does not set or modifies parameter nb_epoch

• Jason Brownlee July 15, 2020 at 1:58 pm #

Sorry to hear that, perhaps try using the Keras API directly instead.

78. amjass August 15, 2020 at 2:47 am #

A quick question: when running KFold cross validation using scikit learn, and using the model.fit method with keras, is it ok to use the validaiton dataset (test in KFold split) for early stopping AND model performance during the kFold metrics? I see some people who dont add any validation split during model.fit, and some that use the same test set for validation as for model performance. So would the following be ok acceptable? Since the validation is only used for early stopping and there will be different validation samples at each fold, there is no reason this would be incorrect?

inputs=np.array(alldata)
targets=trainingtargets

K-fold Cross Validator
kfold = KFold(n_splits=num_folds, shuffle=True, random_state=42)

#earlystopping
early stopping etc…

fold_no = 1
for train, test in kfold.split(inputs, targets):

# model

model=tf.keras.models.Sequential()

# Compile the model
model.compile(loss=”binary_crossentropy”, optimizer=optimizer, metrics=[‘accuracy’])

# Generate a print
print(‘————————————————————————‘)
print(f’Training for fold {fold_no} …’)

# Fit data to model
history=model.fit(inputs[train], targets[train],
epochs=200,
batch_size=32,
verbose=1,
validation_data=(inputs[test], targets[test]),
callbacks=[earlystop], shuffle=True)

# Generate generalization metrics
scores = model.evaluate(inputs[test], targets[test], verbose=0)

Using validation as inputs[test] early stopping AND for generating fold scores is ok and does not necessarily HAVE to be an independent holdout set? The number of samples is generally limited so dont have millions and millions of data to play with!

thank you!

• Jason Brownlee August 15, 2020 at 6:34 am #

Good question, generally it is possible but I don’t have an example. This will help:
https://machinelearningmastery.com/faq/single-faq/how-do-i-use-early-stopping-with-k-fold-cross-validation-or-grid-search

• amjass August 15, 2020 at 7:25 am #

thank you very much!! this does indeed help! May I follow up by asking specifically two questions:

1. (I have read the link you sent,) but just wondering what the school of thought is re: using early stopping during KFold validation generally, is it recommended? if my model tends to converge at ~30 epochs, allowing it to run for 150 wouldn’t be a good measure of real world performance which is what KFold attempts to predict?

2. Coming back to my original question, could using the test data as a predictor of model performance be biased by the fact that it was also used as validation data in the validation split argument in the model.fit call?

thanks again!

• Jason Brownlee August 15, 2020 at 1:25 pm #

Generally it’s hard/a mess.

It’s way simpler to tune the number of epochs as a hyperparameter or treat early stopping as “part of the model”. They are the only two viable approaches I believe.

Yes, test data should not be used, you’d split the training set again and use a portion as a validation set:
https://machinelearningmastery.com/difference-test-validation-datasets/

• amjass August 15, 2020 at 6:36 pm #

thank you so much!

• Jason Brownlee August 16, 2020 at 5:49 am #

You’re welcome.

79. Rex August 19, 2020 at 5:39 am #

Hi Jason,

Thanks a lot for your post! I have a question on tuning the learning rate within different optimizers. Say I want to choose between SGD and Adam, and I have a selection of learning_rate (say [0.001, 0.01, 0.1]. How would I set up the grid search in this case?

Thanks very much.

• Jason Brownlee August 19, 2020 at 6:06 am #

You’re welcome!

If you tune the learning rate, you must use SGD.

80. EOE September 2, 2020 at 6:55 pm #

Hello Sir,

Following your tutorial, I tried using the LSTM Model inside KerasClassifier. However, I came across an issue here.

LSTM requires input in 3D shape, but model.fit() of sklearn demands input in 2D shape.

Is there any way to overcome this problem?

• Jason Brownlee September 3, 2020 at 6:05 am #

I would not recommend using LSTMs with the kerasclassifier.

I recommend using LSTMs directly via the Keras API.

• EOE September 3, 2020 at 7:16 pm #

Alright, Thank you Sir.

81. Faria Zarin Subah September 7, 2020 at 2:28 pm #

Hi, Jason. Following your book, “Deep Learning with Python”, I tried using the KerasClassifier. However, I came across an issue here. When I am using the KerasClassifier and performing kfold cross-validation I am having an accuracy of about 53%. Again, when I’m implementing the same model directly via the Keras API, I am having a completely different accuracy of 23%. Why? Here’s the link to my code:
https://stackoverflow.com/questions/63763714/why-am-i-having-different-accuracy-results-for-the-same-network-configuration-wh

• Jason Brownlee September 8, 2020 at 6:45 am #

Sorry to hear that.

Perhaps the model you are using has a high variance. Maybe try training for longer with a smaller learning rate?

82. Ravit September 19, 2020 at 9:35 pm #

Hi Jason,

Thank you so much for this great tutorial. I am trying to implement this code in my project. I’m working with tensorflow-gpu keras.
I am getting this Error and I can’t figure out how to fix it:

Traceback (most recent call last):
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\IPython\core\interactiveshell.py”, line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File “”, line 1, in
runfile(‘C:\\Users\\ravit\\PycharmProjects\\Barak_BGU\\Decoder-ANN.py’, wdir=’C:/Users/ravit/PycharmProjects/Barak_BGU’)
File “C:\Program Files\JetBrains\PyCharm Community Edition 2019.1.3\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py”, line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File “C:\Program Files\JetBrains\PyCharm Community Edition 2019.1.3\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py”, line 18, in execfile
exec(compile(contents+”\n”, file, ‘exec’), glob, loc)
File “C:\Users\ravit\PycharmProjects\Barak_BGU\Decoder-ANN.py”, line 281, in
best_grid_results = dec.check_for_best_params(dec_model, new_train, y_train)
File “C:\Users\ravit\PycharmProjects\Barak_BGU\Decoder-ANN.py”, line 184, in check_for_best_params
grid_result = grid.fit(X[:-500,:,:], y[:-500], X_val=X[500:,:,:], y_val=y[500:])
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\utils\validation.py”, line 72, in inner_f
return f(**kwargs)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\model_selection\_search.py”, line 681, in fit
base_estimator = clone(self.estimator)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\utils\validation.py”, line 72, in inner_f
return f(**kwargs)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\base.py”, line 87, in clone
new_object_params[name] = clone(param, safe=False)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\utils\validation.py”, line 72, in inner_f
return f(**kwargs)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\sklearn\base.py”, line 71, in clone
return copy.deepcopy(estimator)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 281, in _reconstruct
state = deepcopy(state, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 241, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 216, in _deepcopy_list
append(deepcopy(a, memo))
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 281, in _reconstruct
state = deepcopy(state, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 241, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 216, in _deepcopy_list
append(deepcopy(a, memo))
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 281, in _reconstruct
state = deepcopy(state, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 241, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 216, in _deepcopy_list
append(deepcopy(a, memo))
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 281, in _reconstruct
state = deepcopy(state, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 241, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 281, in _reconstruct
state = deepcopy(state, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 241, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 281, in _reconstruct
state = deepcopy(state, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 150, in deepcopy
y = copier(x, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 241, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File “C:\ProgramData\Anaconda3\envs\tf-gpu\lib\copy.py”, line 169, in deepcopy
rv = reductor(4)

Do you have any idea how I could resolve this ?

83. Jenny Li September 28, 2020 at 7:27 pm #

Hi Jason,
I want to know how to show a confusion matrix with KerasClassifier.

84. Ömer October 12, 2020 at 5:38 pm #

Hello Jason,
thank you for this website, I have learned a lot from your posts and your examples.
I just have one thing to ask: I would like to save the models and the weights and at the end have a model pool to compare for my project. I know I can save models normally with
‘model.save()’
but while using scikit-learn wrapper I don’t know how to reach to the model inside the wrapper and save it.
Wrapper approach makes it easier to generate many models, so I would like to use it, but I need help.
Could you give me a hint to solve this issue or explain it? Others might have a similar situation.
Thank you!

• Jason Brownlee October 13, 2020 at 6:32 am #

You must use the Keras API directly in order to save models. You cannot use the wrapper.

• Ömer October 19, 2020 at 5:09 am #

I have solved my problem. I am writing this, so it might be helpful for other people.
I wanted save my models, not just the best one only, while using a scikit-learn wrapper, because i needed them on my project, for future comparisons, saving weights etc. It is mostly research and experimentation.

Inside ‘tensorflow.keras.wrappers.scikit_learn’ there are ‘KerasClassifier’ and ‘KerasRegressor’ classes, these have been inherited from a third class ‘BaseWrapper’

self.model.save(“name_of_your_model.h5”) inside the fit() method in ‘BaseWrapper’ class, you can save all the fitted models, weights, neural network architectures.
I do not know how useful is this in a normal usage, but in my project I wanted to have pool of neural networks for future comparisons and diagnostics, so I needed them.
Thank you.

• Jason Brownlee October 19, 2020 at 6:41 am #

Thanks for sharing.

• Soumili February 19, 2022 at 1:05 am #

hey can you please let me know in details. It is really very urgent.

85. bhat December 6, 2020 at 2:00 am #

Dear Jason this is an amazing tutorial.
thank you for this.

I have one small warning that keeps popping up every time when I run the Keras classifier piece of code.

WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function..test_function at 0x7fd0eb1fd378> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.

I tried looking out for solutions online but didn’t quite get to understand the solution.
Thank you

• Jason Brownlee December 6, 2020 at 7:04 am #

Perhaps ignore it for now.

• Alex January 15, 2021 at 10:40 pm #

I am having the same problem, any news on this?
thanks

• Jason Brownlee January 16, 2021 at 6:55 am #

Looks like a warning, perhaps ignore?

86. Lilo February 4, 2021 at 12:50 am #

Dear Jason,

thank you for the tutorial.

I want to perform cross validation on a Keras model with multiple inputs. When I use cross_val_score with multiple inputs does not seem to work, and I have this error “ValueError: Found input variables with inconsistent numbers of samples: [2, 500]”.

This is the code:

accuracies = cross_val_score(keras_classifier, [X1, X2], y, cv=cv, scoring=’f1_weighted’).

i know that the problem is the inputs, I would like to ask you how i can fix it?.

• Jason Brownlee February 4, 2021 at 6:21 am #

You may need to enumerate the folds in a for-loop manually and manually fit/evaluate the model within each loop.

87. Gus Diamantis March 24, 2021 at 8:34 am #

Hello Jason!

What if I want to plot the val_acc for every epoch,and that for every case that I have run it? Is this data stored?

• Jason Brownlee March 25, 2021 at 4:35 am #

I would recommend using the keras API directly and then plotting the history returned from calling fit(), e.g.:
https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/

• Vijay Anaparthi April 18, 2021 at 3:02 pm #

But in this case, it won’t print val_accuaracy right because here we are not dividing train/Val, we are using k-fold cv like below.

model = KerasClassifier(build_fn=create_model, epochs=150, batch_size=10, verbose=0)
results = cross_val_score(model, X, Y, cv=kfold)

Here how to print val_accuarcay for every epoch when training model?

• Jason Brownlee April 19, 2021 at 5:49 am #

You will have to train the model manually one epoch at a time in a for loop.

Or, use the keras API directly and use the callback functionality.

88. Andrei April 6, 2021 at 4:04 pm #

Hello Jason,
I just want to say that I truly appreciate the time that was put into making the posts on the website. I really learned a lot from the tutorials and I feel very fortunate to have found this resource.

This being said as I was following your tutorial to try to implement cross-validation into the project I stumbled upon some problems.
I posted my question on StackOverflow as well.
https://stackoverflow.com/questions/66963373/scikit-learn-cross-val-score-throws-valueerror-the-first-argument-to-layer-cal

After following everything carefully the result that I’m getting is an array with nan’s :
array([nan, nan, nan, nan, nan])

I passed error_score=”raise” to cross_val_score and now I get :
ValueError: The first argument to Layer.call must always be passed.

89. ENAS KHALIL April 30, 2021 at 8:29 am #

Dear Jason , thank you so much for this valuble tutorials i learn from every day ,
im trying to use grid search with my lstm model but i have a problem that fit() take two dimension array but LSTM requires 3 dimension array my code is :

# Function to create model, required for KerasClassifier
def create_model(learn_rate, dropout_rate):

# Create model
model = Sequential()

# Compile the model
return model

# expanding dimension of my data
X_all = np.expand_dims(X_all, 1)
Y_all = np.expand_dims(Y_all, 1)
# ###################

#Create the model
model = KerasClassifier(build_fn=create_model, verbose=10)
# Define the parameters that you wish to use in your Grid Search along
# with the list of values that you wish to try out

learn_rate = [0.001, 0.02]
dropout_rate = [0.0, 0.2, 0.5]
batch_size = [10,20,30]

epochs = [5,10,20]

# fix random seed for reproducibility
seed = 30

scoring = {‘F1_score’: ‘f1_micro’,’precision_micro’: ‘precision_micro’,’Accuracy’: ‘jaccard’}
# Make a dictionary of the grid search parameters
param_grid =dict(learn_rate=learn_rate, dropout_rate=dropout_rate, batch_size=batch_size, epochs=epochs )

# Build and fit the GridSearchCV
grid = GridSearchCV(estimator=model, param_grid=param_grid,scoring=scoring,refit=’Accuracy’,
cv=5, verbose=10)

grid_results = grid.fit(X_all, Y_all)

# Summarize the results in a readable format
print(“Best: {0}, using {1}”.format(grid_results.best_score_, grid_results.best_params_))

means = grid_results.cv_results_[‘mean_test_score’]
stds = grid_results.cv_results_[‘std_test_score’]
params = grid_results.cv_results_[‘params’]

for mean, stdev, param in zip(means, stds, params):
print(‘{0} ({1}) with: {2}’.format(mean, stdev, param))

———————————————————————————————-

————————————————————————————————————————————————————

when i run i got the following :

Traceback (most recent call last):

File “D:\code in thesis\new experiment 6-4-2021\grid search4.py”, line 215, in
grid_results = grid.fit(X_all, Y_all)

File “C:\Users\Admin\anaconda3\lib\site-packages\sklearn\utils\validation.py”, line 72, in inner_f
return f(**kwargs)

File “C:\Users\Admin\anaconda3\lib\site-packages\sklearn\model_selection\_search.py”, line 765, in fit
self.best_estimator_.fit(X, y, **fit_params)

File “C:\Users\Admin\anaconda3\lib\site-packages\tensorflow\python\keras\wrappers\scikit_learn.py”, line 222, in fit
raise ValueError(‘Invalid shape for y: ‘ + str(y.shape))

ValueError: Invalid shape for y: (4381, 1, 11)

=————————————————————————————————————————————-
how should i fix this
also if i didnt mention what score inthe GridSearchCV what should be the default score in the output

thanks a lot for your great support

90. George August 18, 2021 at 2:42 pm #

Hi Jason,
When running create_model Function with
 param_grid = dict(dropout_rate=dropout_rate) grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=5) grid_result = grid.fit(X, Y) 
Getting an UserWarning
 UserWarning: model.predict_classes() is deprecated and will be removed after 2021-01-01. Please use instead:* np.argmax(model.predict(x), axis=-1), if your model does multi-class classification (e.g. if it uses a softmax last-layer activation).* (model.predict(x) > 0.5).astype(“int32”), if your model does binary classification (e.g. if it uses a sigmoid last-layer activation). warnings.warn('model.predict_classes() is deprecated and ' 
May i know how to fix this please?
Thanks

• George August 18, 2021 at 3:07 pm #

Actually when fitting grid with validation set like
 grid_result = grid.fit(X_train, Y_train, validation_data=(X_valid, y_valid))

 

getting the above UserWarning

• George August 18, 2021 at 3:10 pm #

Is it correct to use validation set on fitting grid?

• Adrian Tam August 19, 2021 at 3:45 am #

Your code above is correct. Fit with training, and validate with validation set.

• Adrian Tam August 19, 2021 at 3:44 am #

The deprecated warning is fine. It just means the syntax will be in trouble in the future. Usually this is the case when your lower-level library is newer than the higher-level library. When in the future, the higher-level library get a new version, it might go away.

• George August 19, 2021 at 9:53 am #