Once you fit a deep learning neural network model, you must evaluate its performance on a test dataset.

This is critical, as the reported performance allows you to both choose between candidate models and to communicate to stakeholders about how good the model is at solving the problem.

The Keras deep learning API model is very limited in terms of the metrics that you can use to report the model performance.

I am frequently asked questions, such as:

How can I calculate the precision and recall for my model?

And:

How can I calculate the F1-score or confusion matrix for my model?

In this tutorial, you will discover how to calculate metrics to evaluate your deep learning neural network model with a step-by-step example.

After completing this tutorial, you will know:

- How to use the scikit-learn metrics API to evaluate a deep learning model.
- How to make both class and probability predictions with a final model required by the scikit-learn API.
- How to calculate precision, recall, F1-score, ROC AUC, and more with the scikit-learn API for a model.

Let’s get started.

## Tutorial Overview

This tutorial is divided into three parts; they are:

- Binary Classification Problem
- Multilayer Perceptron Model
- How to Calculate Model Metrics

## Binary Classification Problem

We will use a standard binary classification problem as the basis for this tutorial, called the “*two circles*” problem.

It is called the two circles problem because the problem is comprised of points that when plotted, show two concentric circles, one for each class. As such, this is an example of a binary classification problem. The problem has two inputs that can be interpreted as x and y coordinates on a graph. Each point belongs to either the inner or outer circle.

The make_circles() function in the scikit-learn library allows you to generate samples from the two circles problem. The “*n_samples*” argument allows you to specify the number of samples to generate, divided evenly between the two classes. The “*noise*” argument allows you to specify how much random statistical noise is added to the inputs or coordinates of each point, making the classification task more challenging. The “*random_state*” argument specifies the seed for the pseudorandom number generator, ensuring that the same samples are generated each time the code is run.

The example below generates 1,000 samples, with 0.1 statistical noise and a seed of 1.

1 2 |
# generate 2d classification dataset X, y = make_circles(n_samples=1000, noise=0.1, random_state=1) |

Once generated, we can create a plot of the dataset to get an idea of how challenging the classification task is.

The example below generates samples and plots them, coloring each point according to the class, where points belonging to class 0 (outer circle) are colored blue and points that belong to class 1 (inner circle) are colored orange.

1 2 3 4 5 6 7 8 9 10 11 |
# Example of generating samples from the two circle problem from sklearn.datasets import make_circles from matplotlib import pyplot from numpy import where # generate 2d classification dataset X, y = make_circles(n_samples=1000, noise=0.1, random_state=1) # scatter plot, dots colored by class value for i in range(2): samples_ix = where(y == i) pyplot.scatter(X[samples_ix, 0], X[samples_ix, 1]) pyplot.show() |

Running the example generates the dataset and plots the points on a graph, clearly showing two concentric circles for points belonging to class 0 and class 1.

## Multilayer Perceptron Model

We will develop a Multilayer Perceptron, or MLP, model to address the binary classification problem.

This model is not optimized for the problem, but it is skillful (better than random).

After the samples for the dataset are generated, we will split them into two equal parts: one for training the model and one for evaluating the trained model.

1 2 3 4 |
# split into train and test n_test = 500 trainX, testX = X[:n_test, :], X[n_test:, :] trainy, testy = y[:n_test], y[n_test:] |

Next, we can define our MLP model. The model is simple, expecting 2 input variables from the dataset, a single hidden layer with 100 nodes, and a ReLU activation function, then an output layer with a single node and a sigmoid activation function.

The model will predict a value between 0 and 1 that will be interpreted as to whether the input example belongs to class 0 or class 1.

1 2 3 4 |
# define model model = Sequential() model.add(Dense(100, input_dim=2, activation='relu')) model.add(Dense(1, activation='sigmoid')) |

The model will be fit using the binary cross entropy loss function and we will use the efficient Adam version of stochastic gradient descent. The model will also monitor the classification accuracy metric.

1 2 |
# compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) |

We will fit the model for 300 training epochs with the default batch size of 32 samples and evaluate the performance of the model at the end of each training epoch on the test dataset.

1 2 |
# fit model history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=300, verbose=0) |

At the end of training, we will evaluate the final model once more on the train and test datasets and report the classification accuracy.

1 2 3 |
# evaluate the model _, train_acc = model.evaluate(trainX, trainy, verbose=0) _, test_acc = model.evaluate(testX, testy, verbose=0) |

Finally, the performance of the model on the train and test sets recorded during training will be graphed using a line plot, one for each of the loss and the classification accuracy.

1 2 3 4 5 6 7 8 9 10 11 12 13 |
# plot loss during training pyplot.subplot(211) pyplot.title('Loss') pyplot.plot(history.history['loss'], label='train') pyplot.plot(history.history['val_loss'], label='test') pyplot.legend() # plot accuracy during training pyplot.subplot(212) pyplot.title('Accuracy') pyplot.plot(history.history['acc'], label='train') pyplot.plot(history.history['val_acc'], label='test') pyplot.legend() pyplot.show() |

Tying all of these elements together, the complete code listing of training and evaluating an MLP on the two circles problem is listed below.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
# multilayer perceptron model for the two circles problem from sklearn.datasets import make_circles from keras.models import Sequential from keras.layers import Dense from matplotlib import pyplot # generate dataset X, y = make_circles(n_samples=1000, noise=0.1, random_state=1) # split into train and test n_test = 500 trainX, testX = X[:n_test, :], X[n_test:, :] trainy, testy = y[:n_test], y[n_test:] # define model model = Sequential() model.add(Dense(100, input_dim=2, activation='relu')) model.add(Dense(1, activation='sigmoid')) # compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # fit model history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=300, verbose=0) # evaluate the model _, train_acc = model.evaluate(trainX, trainy, verbose=0) _, test_acc = model.evaluate(testX, testy, verbose=0) print('Train: %.3f, Test: %.3f' % (train_acc, test_acc)) # plot loss during training pyplot.subplot(211) pyplot.title('Loss') pyplot.plot(history.history['loss'], label='train') pyplot.plot(history.history['val_loss'], label='test') pyplot.legend() # plot accuracy during training pyplot.subplot(212) pyplot.title('Accuracy') pyplot.plot(history.history['acc'], label='train') pyplot.plot(history.history['val_acc'], label='test') pyplot.legend() pyplot.show() |

Running the example fits the model very quickly on the CPU (no GPU is required).

The model is evaluated, reporting the classification accuracy on the train and test sets of about 83% and 85% respectively.

Note, your specific results may vary given the stochastic nature of the training algorithm.

1 |
Train: 0.838, Test: 0.850 |

A figure is created showing two line plots: one for the learning curves of the loss on the train and test sets and one for the classification on the train and test sets.

The plots suggest that the model has a good fit on the problem.

## How to Calculate Model Metrics

Perhaps you need to evaluate your deep learning neural network model using additional metrics that are not supported by the Keras metrics API.

The Keras metrics API is limited and you may want to calculate metrics such as precision, recall, F1, and more.

One approach to calculating new metrics is to implement them yourself in the Keras API and have Keras calculate them for you during model training and during model evaluation.

For help with this approach, see the tutorial:

This can be technically challenging.

A much simpler alternative is to use your final model to make a prediction for the test dataset, then calculate any metric you wish using the scikit-learn metrics API.

Three metrics, in addition to classification accuracy, that are commonly required for a neural network model on a binary classification problem are:

- Precision
- Recall
- F1 Score

In this section, we will calculate these three metrics, as well as classification accuracy using the scikit-learn metrics API, and we will also calculate three additional metrics that are less common but may be useful. They are:

- Cohen’s Kappa
- ROC AUC
- Confusion Matrix.

This is not a complete list of metrics for classification models supported by scikit-learn; nevertheless, calculating these metrics will show you how to calculate any metrics you may require using the scikit-learn API.

For a full list of supported metrics, see:

The example in this section will calculate metrics for an MLP model, but the same code for calculating metrics can be used for other models, such as RNNs and CNNs.

We can use the same code from the previous sections for preparing the dataset, as well as defining and fitting the model. To make the example simpler, we will put the code for these steps into simple function.

First, we can define a function called *get_data()* that will generate the dataset and split it into train and test sets.

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# generate and prepare the dataset def get_data(): # generate dataset X, y = make_circles(n_samples=1000, noise=0.1, random_state=1) # split into train and test n_test = 500 trainX, testX = X[:n_test, :], X[n_test:, :] trainy, testy = y[:n_test], y[n_test:] return trainX, trainy, testX, testy |

Next, we will define a function called *get_model()* that will define the MLP model and fit it on the training dataset.

1 2 3 4 5 6 7 8 9 10 11 |
# define and fit the model def get_model(trainX, trainy): # define model model = Sequential() model.add(Dense(100, input_dim=2, activation='relu')) model.add(Dense(1, activation='sigmoid')) # compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # fit model model.fit(trainX, trainy, epochs=300, verbose=0) return model |

We can then call the *get_data()* function to prepare the dataset and the *get_model()* function to fit and return the model.

1 2 3 4 |
# generate data trainX, trainy, testX, testy = get_data() # fit model model = get_model(trainX, trainy) |

Now that we have a model fit on the training dataset, we can evaluate it using metrics from the scikit-learn metrics API.

First, we must use the model to make predictions. Most of the metric functions require a comparison between the true class values (e.g. *testy*) and the predicted class values (*yhat_classes*). We can predict the class values directly with our model using the *predict_classes()* function on the model.

Some metrics, like the ROC AUC, require a prediction of class probabilities (*yhat_probs*). These can be retrieved by calling the *predict()* function on the model.

For more help with making predictions using a Keras model, see the post:

We can make the class and probability predictions with the model.

1 2 3 4 |
# predict probabilities for test set yhat_probs = model.predict(testX, verbose=0) # predict crisp classes for test set yhat_classes = model.predict_classes(testX, verbose=0) |

The predictions are returned in a two-dimensional array, with one row for each example in the test dataset and one column for the prediction.

The scikit-learn metrics API expects a 1D array of actual and predicted values for comparison, therefore, we must reduce the 2D prediction arrays to 1D arrays.

1 2 3 |
# reduce to 1d array yhat_probs = yhat_probs[:, 0] yhat_classes = yhat_classes[:, 0] |

We are now ready to calculate metrics for our deep learning neural network model. We can start by calculating the classification accuracy, precision, recall, and F1 scores.

1 2 3 4 5 6 7 8 9 10 11 12 |
# accuracy: (tp + tn) / (p + n) accuracy = accuracy_score(testy, yhat_classes) print('Accuracy: %f' % accuracy) # precision tp / (tp + fp) precision = precision_score(testy, yhat_classes) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(testy, yhat_classes) print('Recall: %f' % recall) # f1: 2 tp / (2 tp + fp + fn) f1 = f1_score(testy, yhat_classes) print('F1 score: %f' % f1) |

Notice that calculating a metric is as simple as choosing the metric that interests us and calling the function passing in the true class values (*testy*) and the predicted class values (*yhat_classes*).

We can also calculate some additional metrics, such as the Cohen’s kappa, ROC AUC, and confusion matrix.

Notice that the ROC AUC requires the predicted class probabilities (*yhat_probs*) as an argument instead of the predicted classes (*yhat_classes*).

1 2 3 4 5 6 7 8 9 |
# kappa kappa = cohen_kappa_score(testy, yhat_classes) print('Cohens kappa: %f' % kappa) # ROC AUC auc = roc_auc_score(testy, yhat_probs) print('ROC AUC: %f' % auc) # confusion matrix matrix = confusion_matrix(testy, yhat_classes) print(matrix) |

Now that we know how to calculate metrics for a deep learning neural network using the scikit-learn API, we can tie all of these elements together into a complete example, listed below.

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# demonstration of calculating metrics for a neural network model using sklearn from sklearn.datasets import make_circles from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix from keras.models import Sequential from keras.layers import Dense # generate and prepare the dataset def get_data(): # generate dataset X, y = make_circles(n_samples=1000, noise=0.1, random_state=1) # split into train and test n_test = 500 trainX, testX = X[:n_test, :], X[n_test:, :] trainy, testy = y[:n_test], y[n_test:] return trainX, trainy, testX, testy # define and fit the model def get_model(trainX, trainy): # define model model = Sequential() model.add(Dense(100, input_dim=2, activation='relu')) model.add(Dense(1, activation='sigmoid')) # compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # fit model model.fit(trainX, trainy, epochs=300, verbose=0) return model # generate data trainX, trainy, testX, testy = get_data() # fit model model = get_model(trainX, trainy) # predict probabilities for test set yhat_probs = model.predict(testX, verbose=0) # predict crisp classes for test set yhat_classes = model.predict_classes(testX, verbose=0) # reduce to 1d array yhat_probs = yhat_probs[:, 0] yhat_classes = yhat_classes[:, 0] # accuracy: (tp + tn) / (p + n) accuracy = accuracy_score(testy, yhat_classes) print('Accuracy: %f' % accuracy) # precision tp / (tp + fp) precision = precision_score(testy, yhat_classes) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(testy, yhat_classes) print('Recall: %f' % recall) # f1: 2 tp / (2 tp + fp + fn) f1 = f1_score(testy, yhat_classes) print('F1 score: %f' % f1) # kappa kappa = cohen_kappa_score(testy, yhat_classes) print('Cohens kappa: %f' % kappa) # ROC AUC auc = roc_auc_score(testy, yhat_probs) print('ROC AUC: %f' % auc) # confusion matrix matrix = confusion_matrix(testy, yhat_classes) print(matrix) |

Running the example prepares the dataset, fits the model, then calculates and reports the metrics for the model evaluated on the test dataset.

Your specific results may vary given the stochastic nature of the training algorithm.

1 2 3 4 5 6 7 8 |
Accuracy: 0.842000 Precision: 0.836576 Recall: 0.853175 F1 score: 0.844794 Cohens kappa: 0.683929 ROC AUC: 0.923739 [[206 42] [ 37 215]] |

If you need help interpreting a given metric, perhaps start with the “Classification Metrics Guide” in the scikit-learn API documentation: Classification Metrics Guide

Also, checkout the Wikipedia page for your metric; for example: Precision and recall, Wikipedia.

## Further Reading

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

### Posts

- How to Use Metrics for Deep Learning With Keras in Python
- How to Generate Test Datasets in Python With scikit-learn
- How to Make Predictions With Keras

### API

- sklearn.metrics: Metrics API
- Classification Metrics Guide
- Keras Metrics API
- sklearn.datasets.make_circles API

### Articles

- Evaluation of binary classifiers, Wikipedia.
- Confusion Matrix, Wikipedia.
- Precision and recall, Wikipedia.

## Summary

In this tutorial, you discovered how to calculate metrics to evaluate your deep learning neural network model with a step-by-step example.

Specifically, you learned:

- How to use the scikit-learn metrics API to evaluate a deep learning model.
- How to make both class and probability predictions with a final model required by the scikit-learn API.
- How to calculate precision, recall, F1-score, ROC, AUC, and more with the scikit-learn API for a model.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

Very useful scikit-learn library modules (API), to avoid construct and develop your owns functions. Thanks !!.

I would appreciate if you can add to this snippet (example) the appropriate code to plot (to visualize) the ROC Curves, confusion matrix, (to determine the best threshold probability to decide where to put the “marker” to decide when it is positive or negative or 0/1).

Also I understand, those metrics only apply for binary classification (F1, precision, recall, AOC curve)? But I know Cohen`s kappa and confusion matrix also apply for multiclass !. Thank you.

Great suggestion, thanks.

i used the code blow to get the model result for F1-score

nn = MLPClassifier(activation=’relu’,alpha=0.01,hidden_layer_sizes=(20,10))

print (“F1-Score by Neural Network, threshold =”,threshold ,”:” ,predict(nn,train, y_train, test, y_test))

now i want to get all the other matrices result accuracy and prediction with Plot but i dont know how i can used in the code above

What problem are you having exactly?

thank you so much about your support ..

from sklearn.neural_network import MLPClassifier

threshold = 200

train, y_train, test, y_test = prep(data,threshold)

nn = MLPClassifier(activation=’relu’,alpha=0.01,hidden_layer_sizes=(20,10))

print (“F1-Score by Neural Network, threshold =”,threshold ,”:” ,predict(nn,train, y_train, test, y_test))

i used the code above i got it from your website to get the F1-score of the model now am looking to get the accuracy ,Precision and Recall for the same model

Perhaps check this:

https://scikit-learn.org/stable/modules/model_evaluation.html

Hello, thank you very much for your website, it helps a lot !

I have a problem related to this post, may be you can halp me 🙂

I try to understand why I obtain different metrics using “model.evaluate” vs “model.predict” and then compute the metrics…

I work on sementic segmentation.

I have an evaluation set of 24 images.

I have a custom DICE INDEX metrics defined as :

”

def dice_coef(y_true, y_pred):

y_true_f = K.flatten(y_true)

y_pred_f = K.flatten(y_pred)

intersection = K.sum (y_true_f * y_pred_f)

result =(2 * intersection)+1 / (K.sum(y_true_f) + K.sum(y_pred_f))+1

return result

”

When I use model.evaluate, I obtain a dice score of 0.9093835949897766.

When I use model.predict and then compute the metrics, I obtain a dice score of 0.9092264051238695.

To give more precisions : I set a batchsize of 24 in model.predict as well as in model.evaluate to be sure the problem is not caused by batch size. I do not know what happen when the batch size is larger (ex: 32) than the number of sample in evaluation set…

Finaly, to compute the metrics after model.prediction, I run :

”

dice_result = 0

for y_i in range(len(y)):

dice_result += tf.Session().run(tf.cast(dice_coef(y[y_i], preds[y_i]),

tf.float32))

tf.Session().close

dice_result /= (len(y))

”

I thought about the tf.float32 casting to be the cause of the difference ?

(Maybe “model.evaluate” computes all with tensorflow tensor and return a float at the end whereas I cast tensor in float32 at every loop ? …)

Do you think about an explanation ?

Thank you for your help.

Cheers !

Thibault

I suspect the evaluate score is averaging across batches.

Perhaps take use predict then calculate the score on all predictions.

Thank you for your reply.

I just have 24 images in my evaluation set, so if “model.evaluate” compute across batches, with a batch size of 24, it will compute the metric in one time on the whole evaluation set. So it will normally gives the same results than “model.predict” followed by the metric computation on the evaluation set ?

That’s why I do not understand my differences here.

Have a good day.

Thibault

I recommend calling predict, then calling the sklearn metric of choice with the results.

Ok 🙂

If I finally decide not to use my dice personal score, but rather to trust Sklearn, is it possible to use this biblioteque with Keras during the training?

Indeed, at the end of the training I get a graph showing the loss and the dice during the epochs.

I would like these graphs to be consistent with the final results?

Thanks again for help!

Have a good day

Thibault

I would expect the graphs to be a fair summary of the training performance.

For presenting an algorithm, I recommend using a final model to make predictions, and plot the results anew.

Ok, I worked on this today.

I fixed this problem. Just in case someone alse has a similar problem.

The fact was that when I resized my ground truth masks before feeding the network with, I did not threshold after the resizing, so I got other values than 0 and 1 at the edges, and my custom dice score gives bad results.

Now I put the threshold just after the resizing and have same results for all the functions I use !

Also, be careful with types casting (float32 vs float64 vs int) !

Anyway, I thank you very much for your disponibility.

Have a good daye

Well done!

How to calculate Precision, Recall, F1, and AUC for multi-class classification Problem

You can use the same approach, the scores are averaged across the classes.

Hello! Another great post of you! Thank you!

I want to calculate Precision, Recall, F1 for every class not only the average. Is it possible?

Thank you in advance

Yes, I believe the sklearn classification report will provide this information.

I also suspect you can configure the sklearn functions for each metric to report per-class scores.

Thank you so much for the quick answer! I will try to calculate them.

No problem. Let me know how you go.

I used

from sklearn.metrics import precision_recall_fscore_support

precision_recall_fscore_support(y_test, y_pred, average=None)

print(classification_report(y_test, y_pred, labels=[0, 1]))

It works fine for me.

Thanks again!

Nice work!

How is that accuracy calculated using “history.history[‘val_acc’]” provides different values as compared to accuracy calculated using “accuracy = accuracy_score(testy, yhat_classes)” ?

It should be the same, e.g. calculate score at the end of each epoch.

thank you

hi jason, i need a code of RNN through which i can find out the classification and confusion matrix of a specific dataset.

There are many examples you can use to get started, perhaps start here:

https://machinelearningmastery.com/start-here/#deep_learning_time_series

I calculated accuracy, precision,recall and f1 using following formulas.

accuracy = metrics.accuracy_score(true_classes, predicted_classes)

precision=metrics.precision_score(true_classes, predicted_classes)

recall=metrics.recall_score(true_classes, predicted_classes)

f1=metrics.f1_score(true_classes, predicted_classes)

The metrics stays at very low value of around 49% to 52 % even after increasing the number of nodes and performing all kinds of tweaking.

Eg:

precision recall f1-score support

nu 0.49 0.34 0.40 2814

u 0.50 0.65 0.56 2814

avg / total 0.49 0.49 0.48 5628

The confusion matrix shows very high values of FP and FN

confusion= [[ 953 1861]

[ 984 1830]]

What can I do to improve the performance?

For the low values of accuracy, precision, recall and F1, the accuracy and loss plot is also weird.

The accuracy of validation dataset remains higher than training dataset; similarly, the validation loss remains lower than that of training dataset; whereas the reverse is expected.

How to overcome this problem?

Better results on the test set than the training set may suggest that the test set is not representative of the problem, e.g. is too small.

thanks

No problem.

I offer some suggestions here:

https://machinelearningmastery.com/start-here/#better