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Evaluate the Performance Of Deep Learning Models in Keras

Last Updated on August 19, 2019

Keras is an easy to use and powerful Python library for deep learning.

There are a lot of decisions to make when designing and configuring your deep learning models. Most of these decisions must be resolved empirically through trial and error and evaluating them on real data.

As such, it is critically important to have a robust way to evaluate the performance of your neural networks and deep learning models.

In this post you will discover a few ways that you can use to evaluate model performance using Keras.

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  • Update Oct/2016: Updated examples for Keras 1.1.0 and scikit-learn v0.18.
  • Update Mar/2017: Updated example for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
  • Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.

Evaluate the Performance Of Deep Learning Models in Keras

Evaluate the Performance Of Deep Learning Models in Keras
Photo by Thomas Leuthard, some rights reserved.

Empirically Evaluate Network Configurations

There are a myriad of decisions you must make when designing and configuring your deep learning models.

Many of these decisions can be resolved by copying the structure of other people’s networks and using heuristics. Ultimately, the best technique is to actually design small experiments and empirically evaluate options using real data.

This includes high-level decisions like the number, size and type of layers in your network. It also includes the lower level decisions like the choice of loss function, activation functions,  optimization procedure and number of epochs.

Deep learning is often used on problems that have very large datasets. That is tens of thousands or hundreds of thousands of instances.

As such, you need to have a robust test harness that allows you to estimate the performance of a given configuration on unseen data, and reliably compare the performance to other configurations.

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Data Splitting

The large amount of data and the complexity of the models require very long training times.

As such, it is typically to use a simple separation of data into training and test datasets or training and validation datasets.

Keras provides a two convenient ways of evaluating your deep learning algorithms this way:

  1. Use an automatic verification dataset.
  2. Use a manual verification dataset.

Use a Automatic Verification Dataset

Keras can separate a portion of your training data into a validation dataset and evaluate the performance of your model on that validation dataset each epoch.

You can do this by setting the validation_split argument on the fit() function to a percentage of the size of your training dataset.

For example, a reasonable value might be 0.2 or 0.33 for 20% or 33% of your training data held back for validation.

The example below demonstrates the use of using an automatic validation dataset on a small binary classification problem. All examples in this post use the Pima Indians onset of diabetes dataset. You can download it from the UCI Machine Learning Repository and save the data file in your current working directory with the filename pima-indians-diabetes.csv (update: download from here).

# MLP with automatic validation set
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=”,”)
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation=’relu’))
model.add(Dense(8, activation=’relu’))
model.add(Dense(1, activation=’sigmoid’))
# Compile model
model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10)

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# MLP with automatic validation set

from keras.models import Sequential

from keras.layers import Dense

import numpy

# fix random seed for reproducibility

numpy.random.seed(7)

# load pima indians dataset

dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=”,”)

# split into input (X) and output (Y) variables

X = dataset[:,0:8]

Y = dataset[:,8]

# create model

model = Sequential()

model.add(Dense(12, input_dim=8, activation=’relu’))

model.add(Dense(8, activation=’relu’))

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

# Compile model

model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

# Fit the model

model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10)

Running the example, you can see that the verbose output on each epoch shows the loss and accuracy on both the training dataset and the validation dataset.


Epoch 145/150
514/514 [==============================] – 0s – loss: 0.5252 – acc: 0.7335 – val_loss: 0.5489 – val_acc: 0.7244
Epoch 146/150
514/514 [==============================] – 0s – loss: 0.5198 – acc: 0.7296 – val_loss: 0.5918 – val_acc: 0.7244
Epoch 147/150
514/514 [==============================] – 0s – loss: 0.5175 – acc: 0.7335 – val_loss: 0.5365 – val_acc: 0.7441
Epoch 148/150
514/514 [==============================] – 0s – loss: 0.5219 – acc: 0.7354 – val_loss: 0.5414 – val_acc: 0.7520
Epoch 149/150
514/514 [==============================] – 0s – loss: 0.5089 – acc: 0.7432 – val_loss: 0.5417 – val_acc: 0.7520
Epoch 150/150
514/514 [==============================] – 0s – loss: 0.5148 – acc: 0.7490 – val_loss: 0.5549 – val_acc: 0.7520

Epoch 145/150

514/514 [==============================] – 0s – loss: 0.5252 – acc: 0.7335 – val_loss: 0.5489 – val_acc: 0.7244

Epoch 146/150

514/514 [==============================] – 0s – loss: 0.5198 – acc: 0.7296 – val_loss: 0.5918 – val_acc: 0.7244

Epoch 147/150

514/514 [==============================] – 0s – loss: 0.5175 – acc: 0.7335 – val_loss: 0.5365 – val_acc: 0.7441

Epoch 148/150

514/514 [==============================] – 0s – loss: 0.5219 – acc: 0.7354 – val_loss: 0.5414 – val_acc: 0.7520

Epoch 149/150

514/514 [==============================] – 0s – loss: 0.5089 – acc: 0.7432 – val_loss: 0.5417 – val_acc: 0.7520

Epoch 150/150

514/514 [==============================] – 0s – loss: 0.5148 – acc: 0.7490 – val_loss: 0.5549 – val_acc: 0.7520

Use a Manual Verification Dataset

Keras also allows you to manually specify the dataset to use for validation during training.

In this example we use the handy train_test_split() function from the Python scikit-learn machine learning library to separate our data into a training and test dataset. We use 67% for training and the remaining 33% of the data for validation.

The validation dataset can be specified to the fit() function in Keras by the validation_data argument. It takes a tuple of the input and output datasets.

# MLP with manual validation set
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=”,”)
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# split into 67% for train and 33% for test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation=’relu’))
model.add(Dense(8, activation=’relu’))
model.add(Dense(1, activation=’sigmoid’))
# Compile model
model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=150, batch_size=10)

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# MLP with manual validation set

from keras.models import Sequential

from keras.layers import Dense

from sklearn.model_selection import train_test_split

import numpy

# fix random seed for reproducibility

seed = 7

numpy.random.seed(seed)

# load pima indians dataset

dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=”,”)

# split into input (X) and output (Y) variables

X = dataset[:,0:8]

Y = dataset[:,8]

# split into 67% for train and 33% for test

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)

# create model

model = Sequential()

model.add(Dense(12, input_dim=8, activation=’relu’))

model.add(Dense(8, activation=’relu’))

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

# Compile model

model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

# Fit the model

model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=150, batch_size=10)

Like before, running the example provides verbose output of training that includes the loss and accuracy of the model on both the training and validation datasets for each epoch.


Epoch 145/150
514/514 [==============================] – 0s – loss: 0.4847 – acc: 0.7704 – val_loss: 0.5668 – val_acc: 0.7323
Epoch 146/150
514/514 [==============================] – 0s – loss: 0.4853 – acc: 0.7549 – val_loss: 0.5768 – val_acc: 0.7087
Epoch 147/150
514/514 [==============================] – 0s – loss: 0.4864 – acc: 0.7743 – val_loss: 0.5604 – val_acc: 0.7244
Epoch 148/150
514/514 [==============================] – 0s – loss: 0.4831 – acc: 0.7665 – val_loss: 0.5589 – val_acc: 0.7126
Epoch 149/150
514/514 [==============================] – 0s – loss: 0.4961 – acc: 0.7782 – val_loss: 0.5663 – val_acc: 0.7126
Epoch 150/150
514/514 [==============================] – 0s – loss: 0.4967 – acc: 0.7588 – val_loss: 0.5810 – val_acc: 0.6929

Epoch 145/150

514/514 [==============================] – 0s – loss: 0.4847 – acc: 0.7704 – val_loss: 0.5668 – val_acc: 0.7323

Epoch 146/150

514/514 [==============================] – 0s – loss: 0.4853 – acc: 0.7549 – val_loss: 0.5768 – val_acc: 0.7087

Epoch 147/150

514/514 [==============================] – 0s – loss: 0.4864 – acc: 0.7743 – val_loss: 0.5604 – val_acc: 0.7244

Epoch 148/150

514/514 [==============================] – 0s – loss: 0.4831 – acc: 0.7665 – val_loss: 0.5589 – val_acc: 0.7126

Epoch 149/150

514/514 [==============================] – 0s – loss: 0.4961 – acc: 0.7782 – val_loss: 0.5663 – val_acc: 0.7126

Epoch 150/150

514/514 [==============================] – 0s – loss: 0.4967 – acc: 0.7588 – val_loss: 0.5810 – val_acc: 0.6929

Manual k-Fold Cross Validation

The gold standard for machine learning model evaluation is k-fold cross validation.

It provides a robust estimate of the performance of a model on unseen data. It does this by splitting the training dataset into k subsets and takes turns training models on all subsets except one which is held out, and evaluating model performance on the held out validation dataset. The process is repeated until all subsets are given an opportunity to be the held out validation set. The performance measure is then averaged across all models that are created.

Cross validation is often not used for evaluating deep learning models because of the greater computational expense. For example k-fold cross validation is often used with 5 or 10 folds. As such, 5 or 10 models must be constructed and evaluated, greatly adding to the evaluation time of a model.

Nevertheless, it when the problem is small enough or if you have sufficient compute resources, k-fold cross validation can give you a less biased estimate of the performance of your model.

In the example below we use the handy StratifiedKFold class from the scikit-learn Python machine learning library to split up the training dataset into 10 folds. The folds are stratified, meaning that the algorithm attempts to balance the number of instances of each class in each fold.

The example creates and evaluates 10 models using the 10 splits of the data and collects all of the scores. The verbose output for each epoch is turned off by passing verbose=0 to the fit() and evaluate() functions on the model.

The performance is printed for each model and it is stored. The average and standard deviation of the model performance is then printed at the end of the run to provide a robust estimate of model accuracy.

# MLP for Pima Indians Dataset with 10-fold cross validation
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import StratifiedKFold
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=”,”)
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define 10-fold cross validation test harness
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation=’relu’))
model.add(Dense(8, activation=’relu’))
model.add(Dense(1, activation=’sigmoid’))
# Compile model
model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
# Fit the model
model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X[test], Y[test], verbose=0)
print(“%s: %.2f%%” % (model.metrics_names[1], scores[1]*100))
cvscores.append(scores[1] * 100)
print(“%.2f%% (+/- %.2f%%)” % (numpy.mean(cvscores), numpy.std(cvscores)))

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# MLP for Pima Indians Dataset with 10-fold cross validation

from keras.models import Sequential

from keras.layers import Dense

from sklearn.model_selection import StratifiedKFold

import numpy

# fix random seed for reproducibility

seed = 7

numpy.random.seed(seed)

# load pima indians dataset

dataset = numpy.loadtxt(“pima-indians-diabetes.csv”, delimiter=”,”)

# split into input (X) and output (Y) variables

X = dataset[:,0:8]

Y = dataset[:,8]

# define 10-fold cross validation test harness

kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)

cvscores = []

for train, test in kfold.split(X, Y):

  # create model

model = Sequential()

model.add(Dense(12, input_dim=8, activation=’relu’))

model.add(Dense(8, activation=’relu’))

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

# Compile model

model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

# Fit the model

model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)

# evaluate the model

scores = model.evaluate(X[test], Y[test], verbose=0)

print(“%s: %.2f%%” % (model.metrics_names[1], scores[1]*100))

cvscores.append(scores[1] * 100)

print(“%.2f%% (+/- %.2f%%)” % (numpy.mean(cvscores), numpy.std(cvscores)))

Running the example will take less than a minute and will produce the following output:

acc: 77.92%
acc: 68.83%
acc: 72.73%
acc: 64.94%
acc: 77.92%
acc: 35.06%
acc: 74.03%
acc: 68.83%
acc: 34.21%
acc: 72.37%
64.68% (+/- 15.50%)

acc: 77.92%

acc: 68.83%

acc: 72.73%

acc: 64.94%

acc: 77.92%

acc: 35.06%

acc: 74.03%

acc: 68.83%

acc: 34.21%

acc: 72.37%

64.68% (+/- 15.50%)

Summary

In this post you discovered the importance of having a robust way to estimate the performance of your deep learning models on unseen data.

You discovered three ways that you can estimate the performance of your deep learning models in Python using the Keras library:

  • Use Automatic Verification Datasets.
  • Use Manual Verification Datasets.
  • Use Manual k-Fold Cross Validation.

Do you have any questions about deep learning with Keras or this post? Ask your question in the comments and I will do my best to answer it.

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