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Configuring neural network models is often referred to as a “dark art.”
This is because there are no hard and fast rules for configuring a network for a given problem. We cannot analytically calculate the optimal model type or model configuration for a given dataset.
Fortunately, there are techniques that are known to address specific issues when configuring and training a neural network that are available in modern deep learning libraries such as Keras.
In this crash course, you will discover how you can confidently get better performance from your deep learning models in seven days.
This is a big and important post. You might want to bookmark it.
Discover how to train faster, reduce overfitting, and make better predictions with deep learning models in my new book, with 26 step-by-step tutorials and full source code.
Let’s get started.
How to Get Better Deep Learning Performance (7 Day Mini-Course)
Photo by Damian Gadal, some rights reserved.
Who Is This Crash-Course For?
Before we get started, let’s make sure you are in the right place.
The list below provides some general guidelines as to who this course was designed for.
You need to know:
- Your way around basic Python and NumPy.
- The basics of Keras for deep learning.
You do NOT need to know:
- How to be a math wiz!
- How to be a deep learning expert!
This crash course will take you from a developer that knows a little deep learning to a developer who can get better performance on your deep learning project.
Note: This crash course assumes you have a working Python 2 or 3 SciPy environment with at least NumPy and Keras 2 installed. If you need help with your environment, you can follow the step-by-step tutorial here:
- How to Set Up a Python Environment for Machine Learning and Deep Learning With Anaconda
Crash-Course Overview
This crash course is broken down into seven lessons.
You could complete one lesson per day (recommended) or complete all of the lessons in one day (hardcore). It really depends on the time you have available and your level of enthusiasm.
Below are seven lessons that will allow you to confidently improve the performance of your deep learning model:
- Lesson 01: Better Deep Learning Framework
- Lesson 02: Batch Size
- Lesson 03: Learning Rate Schedule
- Lesson 04: Batch Normalization
- Lesson 05: Weight Regularization
- Lesson 06: Adding Noise
- Lesson 07: Early Stopping
Each lesson could take you 60 seconds or up to 30 minutes. Take your time and complete the lessons at your own pace. Ask questions and even post results in the comments below.
The lessons expect you to go off and find out how to do things. I will give you hints, but part of the point of each lesson is to force you to learn where to go to look for help (hint, I have all of the answers directly on this blog; use the search box).
I do provide more help in the form of links to related posts because I want you to build up some confidence and inertia.
Post your results in the comments; I’ll cheer you on!
Hang in there; don’t give up.
Note: This is just a crash course. For a lot more detail and fleshed out tutorials, see my book on the topic titled “Better Deep Learning.”
Lesson 01: Better Deep Learning Framework
In this lesson, you will discover a framework that you can use to systematically improve the performance of your deep learning model.
Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network models in minutes with just a few lines of code.
Nevertheless, it is still challenging to configure a neural network to get good performance on a new predictive modeling problem.
There are three types of problems that are straightforward to diagnose with regard to the poor performance of a deep learning neural network model; they are:
- Problems with Learning. Problems with learning manifest in a model that cannot effectively learn a training dataset or shows slow progress or bad performance when learning the training dataset.
- Problems with Generalization. Problems with generalization manifest in a model that overfits the training dataset and makes poor performance on a holdout dataset.
- Problems with Predictions. Problems with predictions manifest as the stochastic training algorithm having a strong influence on the final model, causing a high variance in behavior and performance.
The sequential relationship between the three areas in the proposed breakdown allows the issue of deep learning model performance to be first isolated, then targeted with a specific technique or methodology.
We can summarize techniques that assist with each of these problems as follows:
- Better Learning. Techniques that improve or accelerate the adaptation of neural network model weights in response to a training dataset.
- Better Generalization. Techniques that improve the performance of a neural network model on a holdout dataset.
- Better Predictions. Techniques that reduce the variance in the performance of a final model.
You can use this framework to first diagnose the type of problem that you have and then identify a technique to evaluate to attempt to address your problem.
Your Task
For this lesson, you must list two techniques or areas of focus that belong to each of the three areas of the framework.
Having trouble? Note that we will be looking some examples from two of the three areas as part of this mini-course.
Post your answer in the comments below. I would love to see what you discover.
Next
In the next lesson, you will discover how to control the speed of learning with the batch size.
Lesson 02: Batch Size
In this lesson, you will discover the importance of the batch size when training neural networks.
Neural networks are trained using gradient descent where the estimate of the error used to update the weights is calculated based on a subset of the training dataset.
The number of examples from the training dataset used in the estimate of the error gradient is called the batch size and is an important hyperparameter that influences the dynamics of the learning algorithm.
The choice of batch size controls how quickly the algorithm learns, for example:
- Batch Gradient Descent. Batch size is set to the number of examples in the training dataset, more accurate estimate of error but longer time between weight updates.
- Stochastic Gradient Descent. Batch size is set to 1, noisy estimate of error but frequent updates to weights.
- Minibatch Gradient Descent. Batch size is set to a value more than 1 and less than the number of training examples, trade-off between batch and stochastic gradient descent.
Keras allows you to configure the batch size via the batch_size argument to the fit() function, for example:
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# fit model
history = model.fit(trainX, trainy, epochs=1000, batch_size=len(trainX))
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The example below demonstrates a Multilayer Perceptron with batch gradient descent on a binary classification problem.
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# example of batch gradient descent
from sklearn.datasets import make_circles
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import SGD
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_train = 500
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# define model
model = Sequential()
model.add(Dense(50, input_dim=2, activation=‘relu’))
model.add(Dense(1, activation=‘sigmoid’))
# compile model
opt = SGD(lr=0.01, momentum=0.9)
model.compile(loss=‘binary_crossentropy’, optimizer=opt, metrics=[‘accuracy’])
# fit model
history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=1000, batch_size=len(trainX), 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 learning curves
pyplot.subplot(211)
pyplot.title(‘Cross-Entropy Loss’, pad=–40)
pyplot.plot(history.history[‘loss’], label=‘train’)
pyplot.plot(history.history[‘val_loss’], label=‘test’)
pyplot.legend()
# plot accuracy learning curves
pyplot.subplot(212)
pyplot.title(‘Accuracy’, pad=–40)
pyplot.plot(history.history[‘acc’], label=‘train’)
pyplot.plot(history.history[‘val_acc’], label=‘test’)
pyplot.legend()
pyplot.show()
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Your Task
For this lesson, you must run the code example with each type of gradient descent (batch, minibatch, and stochastic) and describe the effect that it has on the learning curves during training.
Post your answer in the comments below. I would love to see what you discover.
Next
In the next lesson, you will discover how to fine tune a model during training with a learning rate schedule
Lesson 03: Learning Rate Schedule
In this lesson, you will discover how to configure an adaptive learning rate schedule to fine tune the model during the training run.
The amount of change to the model during each step of this search process, or the step size, is called the “learning rate” and provides perhaps the most important hyperparameter to tune for your neural network in order to achieve good performance on your problem.
Configuring a fixed learning rate is very challenging and requires careful experimentation. An alternative to using a fixed learning rate is to instead vary the learning rate over the training process.
Keras provides the ReduceLROnPlateau learning rate schedule that will adjust the learning rate when a plateau in model performance is detected, e.g. no change for a given number of training epochs. For example:
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# define learning rate schedule
rlrp = ReduceLROnPlateau(monitor=‘val_loss’, factor=0.1, patience=5, min_delta=1E–7, verbose=1)
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This callback is designed to reduce the learning rate after the model stops improving with the hope of fine-tuning model weights during training.
The example below demonstrates a Multilayer Perceptron with a learning rate schedule on a binary classification problem, where the learning rate will be reduced by an order of magnitude if no change is detected in validation loss over 5 training epochs.
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# example of a learning rate schedule
from sklearn.datasets import make_circles
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import SGD
from keras.callbacks import ReduceLROnPlateau
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_train = 500
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# define model
model = Sequential()
model.add(Dense(50, input_dim=2, activation=‘relu’))
model.add(Dense(1, activation=‘sigmoid’))
# compile model
opt = SGD(lr=0.01, momentum=0.9)
model.compile(loss=‘binary_crossentropy’, optimizer=opt, metrics=[‘accuracy’])
# define learning rate schedule
rlrp = ReduceLROnPlateau(monitor=‘val_loss’, factor=0.1, patience=5, min_delta=1E–7, verbose=1)
# fit model
history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=300, verbose=0, callbacks=[rlrp])
# 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 learning curves
pyplot.subplot(211)
pyplot.title(‘Cross-Entropy Loss’, pad=–40)
pyplot.plot(history.history[‘loss’], label=‘train’)
pyplot.plot(history.history[‘val_loss’], label=‘test’)
pyplot.legend()
# plot accuracy learning curves
pyplot.subplot(212)
pyplot.title(‘Accuracy’, pad=–40)
pyplot.plot(history.history[‘acc’], label=‘train’)
pyplot.plot(history.history[‘val_acc’], label=‘test’)
pyplot.legend()
pyplot.show()
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Your Task
For this lesson, you must run the code example with and without the learning rate schedule and describe the effect that the learning rate schedule has on the learning curves during training.
Post your answer in the comments below. I would love to see what you discover.
Next
In the next lesson, you will discover how you can accelerate the training process with batch normalization
Lesson 04: Batch Normalization
In this lesson, you will discover how to accelerate the training process of your deep learning neural network using batch normalization.
Batch normalization, or batchnorm for short, is proposed as a technique to help coordinate the update of multiple layers in the model.
The authors of the paper introducing batch normalization refer to change in the distribution of inputs during training as “internal covariate shift“. Batch normalization was designed to counter the internal covariate shift by scaling the output of the previous layer, specifically by standardizing the activations of each input variable per mini-batch, such as the activations of a node from the previous layer.
Keras supports Batch Normalization via a separate BatchNormalization layer that can be added between the hidden layers of your model. For example:
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model.add(BatchNormalization())
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The example below demonstrates a Multilayer Perceptron model with batch normalization on a binary classification problem.
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# example of batch normalization
from sklearn.datasets import make_circles
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.layers import BatchNormalization
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_train = 500
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# define model
model = Sequential()
model.add(Dense(50, input_dim=2, activation=‘relu’))
model.add(BatchNormalization())
model.add(Dense(1, activation=‘sigmoid’))
# compile model
opt = SGD(lr=0.01, momentum=0.9)
model.compile(loss=‘binary_crossentropy’, optimizer=opt, 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 learning curves
pyplot.subplot(211)
pyplot.title(‘Cross-Entropy Loss’, pad=–40)
pyplot.plot(history.history[‘loss’], label=‘train’)
pyplot.plot(history.history[‘val_loss’], label=‘test’)
pyplot.legend()
# plot accuracy learning curves
pyplot.subplot(212)
pyplot.title(‘Accuracy’, pad=–40)
pyplot.plot(history.history[‘acc’], label=‘train’)
pyplot.plot(history.history[‘val_acc’], label=‘test’)
pyplot.legend()
pyplot.show()
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Your Task
For this lesson, you must run the code example with and without batch normalization and describe the effect that batch normalization has on the learning curves during training.
Post your answer in the comments below. I would love to see what you discover.
Next
In the next lesson, you will discover how to reduce overfitting using weight regularization.
Lesson 05: Weight Regularization
In this lesson, you will discover how to reduce overfitting of your deep learning neural network using weight regularization.
A model with large weights is more complex than a model with smaller weights. It is a sign of a network that may be overly specialized to training data.
The learning algorithm can be updated to encourage the network toward using small weights.
One way to do this is to change the calculation of loss used in the optimization of the network to also consider the size of the weights. This is called weight regularization or weight decay.
Keras supports weight regularization via the kernel_regularizer argument on a layer, which can be configured to use the L1 or L2 vector norm, for example:
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model.add(Dense(500, input_dim=2, activation=‘relu’, kernel_regularizer=l2(0.01)))
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The example below demonstrates a Multilayer Perceptron model with weight decay on a binary classification problem.
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# example of weight decay
from sklearn.datasets import make_circles
from keras.models import Sequential
from keras.layers import Dense
from keras.regularizers import l2
from matplotlib import pyplot
# generate dataset
X, y = make_circles(n_samples=100, noise=0.1, random_state=1)
# split into train and test
n_train = 30
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# define model
model = Sequential()
model.add(Dense(500, input_dim=2, activation=‘relu’, kernel_regularizer=l2(0.01)))
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=4000, 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 learning curves
pyplot.subplot(211)
pyplot.title(‘Cross-Entropy Loss’, pad=–40)
pyplot.plot(history.history[‘loss’], label=‘train’)
pyplot.plot(history.history[‘val_loss’], label=‘test’)
pyplot.legend()
# plot accuracy learning curves
pyplot.subplot(212)
pyplot.title(‘Accuracy’, pad=–40)
pyplot.plot(history.history[‘acc’], label=‘train’)
pyplot.plot(history.history[‘val_acc’], label=‘test’)
pyplot.legend()
pyplot.show()
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Your Task
For this lesson, you must run the code example with and without weight regularization and describe the effect that it has on the learning curves during training.
Post your answer in the comments below. I would love to see what you discover.
Next
In the next lesson, you will discover how to reduce overfitting by adding noise to your model
Lesson 06: Adding Noise
In this lesson, you will discover that adding noise to a neural network during training can improve the robustness of the network, resulting in better generalization and faster learning.
Training a neural network with a small dataset can cause the network to memorize all training examples, in turn leading to poor performance on a holdout dataset.
One approach to making the input space smoother and easier to learn is to add noise to inputs during training.
The addition of noise during the training of a neural network model has a regularization effect and, in turn, improves the robustness of the model.
Noise can be added to your model in Keras via the GaussianNoise layer. For example:
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model.add(GaussianNoise(0.1))
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Noise can be added to a model at the input layer or between hidden layers.
The example below demonstrates a Multilayer Perceptron model with added noise between the hidden layers on a binary classification problem.
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# example of adding noise
from sklearn.datasets import make_circles
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import GaussianNoise
from matplotlib import pyplot
# generate dataset
X, y = make_circles(n_samples=100, noise=0.1, random_state=1)
# split into train and test
n_train = 30
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# define model
model = Sequential()
model.add(Dense(500, input_dim=2, activation=‘relu’))
model.add(GaussianNoise(0.1))
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=4000, 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 learning curves
pyplot.subplot(211)
pyplot.title(‘Cross-Entropy Loss’, pad=–40)
pyplot.plot(history.history[‘loss’], label=‘train’)
pyplot.plot(history.history[‘val_loss’], label=‘test’)
pyplot.legend()
# plot accuracy learning curves
pyplot.subplot(212)
pyplot.title(‘Accuracy’, pad=–40)
pyplot.plot(history.history[‘acc’], label=‘train’)
pyplot.plot(history.history[‘val_acc’], label=‘test’)
pyplot.legend()
pyplot.show()
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Your Task
For this lesson, you must run the code example with and without the addition of noise and describe the effect that it has on the learning curves during training.
Post your answer in the comments below. I would love to see what you discover.
Next
In the next lesson, you will discover how to reduce overfitting using early stopping.
Lesson 07: Early Stopping
In this lesson, you will discover that stopping the training of a neural network early before it has overfit the training dataset can reduce overfitting and improve the generalization of deep neural networks.
A major challenge in training neural networks is how long to train them.
Too little training will mean that the model will underfit the train and the test sets. Too much training will mean that the model will overfit the training dataset and have poor performance on the test set.
A compromise is to train on the training dataset but to stop training at the point when performance on a validation dataset starts to degrade. This simple, effective, and widely used approach to training neural networks is called early stopping.
Keras supports early stopping via the EarlyStopping callback that allows you to specify the metric to monitor during training.
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# patient early stopping
es = EarlyStopping(monitor=‘val_loss’, mode=‘min’, verbose=1, patience=200)
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The example below demonstrates a Multilayer Perceptron with early stopping on a binary classification problem that will stop when the validation loss has not improved for 200 training epochs.
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# example of early stopping
from sklearn.datasets import make_circles
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from matplotlib import pyplot
# generate dataset
X, y = make_circles(n_samples=100, noise=0.1, random_state=1)
# split into train and test
n_train = 30
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# define model
model = Sequential()
model.add(Dense(500, input_dim=2, activation=‘relu’))
model.add(Dense(1, activation=‘sigmoid’))
# compile model
model.compile(loss=‘binary_crossentropy’, optimizer=‘adam’, metrics=[‘accuracy’])
# patient early stopping
es = EarlyStopping(monitor=‘val_loss’, mode=‘min’, verbose=1, patience=200)
# fit model
history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=4000, verbose=0, callbacks=[es])
# 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 learning curves
pyplot.subplot(211)
pyplot.title(‘Cross-Entropy Loss’, pad=–40)
pyplot.plot(history.history[‘loss’], label=‘train’)
pyplot.plot(history.history[‘val_loss’], label=‘test’)
pyplot.legend()
# plot accuracy learning curves
pyplot.subplot(212)
pyplot.title(‘Accuracy’, pad=–40)
pyplot.plot(history.history[‘acc’], label=‘train’)
pyplot.plot(history.history[‘val_acc’], label=‘test’)
pyplot.legend()
pyplot.show()
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Your Task
For this lesson, you must run the code example with and without early stopping and describe the effect it has on the learning curves during training.
Post your answer in the comments below. I would love to see what you discover.
Next
This was your final lesson.
The End!
(Look how far you have come!)
You made it. Well done!
Take a moment and look back at how far you have come.
You discovered:
- A framework that you can use to systematically diagnose and improve the performance of your deep learning model.
- Batch size can be used to control the precision of the estimated error and the speed of learning during training.
- Learning rate schedule can be used to fine tune the model weights during training.
- Batch normalization can be used to dramatically accelerate the training process of neural network models.
- Weight regularization will penalize models based on the size of the weights and reduce overfitting.
- Adding noise will make the model more robust to differences in input and reduce overfitting
- Early stopping will halt the training process at the right time and reduce overfitting.
This is just the beginning of your journey with deep learning performance improvement. Keep practicing and developing your skills.
Take the next step and check out my book on getting better performance with deep learning.
Summary
How did you do with the mini-course?
Did you enjoy this crash course?
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