AI News,

Neural Network Lab Dropout training is a relatively new algorithm which appears to be highly effective for improving the quality of neural network predictions.

major challenge when working with a neural network is training the network in such a way that the resulting model doesn't over-fit the training data -- that is, generate weights and bias values that predict the dependent y-values of the training data with very high accuracy, but predict the y-values for new data with poor accuracy.

The demo program creates and trains a neural network classifier that predicts the species of an iris flower (setosa, versicolor or virginica) based on four numeric x-values for sepal length and width and petal length and width.

After training, the resulting neural network model with (4 * 9) + (9 * 3) + (9 + 3) = 75 weights and bias values correctly predicts the species of 29 of the 30 data items (0.9667 accuracy) in the test set.

This article assumes you have a solid understanding of neural network concepts, including the feed-forward mechanism, and the back-propagation algorithm, and that you have at least intermediate level programming skills, but does not assume you know anything about dropout training.

and the three hidden node bias value are -2.0, -2.3, -2.6 and -3.0, and the input x-values are 1.0, 3.0, 5.0, then hSums[0] = (1.0)(0.01) + (3.0)(0.05) + (5.0)(0.09) + (-2.0) = -1.39.

and the two output node bias values are 4.0 and 5.0, and the outputs for hidden nodes [0] and [2] are -0.88 and -0.97, then oSums[0] = (-0.88)(0.13) + (-0.97)(0.19) + 4.0 = 3.70.

If the softmax function is used for output layer activation, then the two final outputs of the neural network are softmax(0, 3.70, 4.68) = 0.27 and softmax(1, 3.70, 4.68) = 0.73.

For example, if a neural network has nine hidden nodes and the return from MakeDropNodes is an array of size four with values 0, 2, 6, 8 then there are four drop-nodes at indices [0], [2], [6] and [8].

Dropout in (Deep) Machine learning

Now that we know a little bit about dropout and the motivation, let’s go into some detail.

Dropout is an approach to regularization in neural networks which helps reducing interdependent learning amongst the neurons.

Training Phase: For each hidden layer, for each training sample, for each iteration, ignore (zero out) a random fraction, p, of nodes (and corresponding activations).

Neural Network Lab Dropout training is a relatively new algorithm which appears to be highly effective for improving the quality of neural network predictions.

major challenge when working with a neural network is training the network in such a way that the resulting model doesn't over-fit the training data -- that is, generate weights and bias values that predict the dependent y-values of the training data with very high accuracy, but predict the y-values for new data with poor accuracy.

The demo program creates and trains a neural network classifier that predicts the species of an iris flower (setosa, versicolor or virginica) based on four numeric x-values for sepal length and width and petal length and width.

After training, the resulting neural network model with (4 * 9) + (9 * 3) + (9 + 3) = 75 weights and bias values correctly predicts the species of 29 of the 30 data items (0.9667 accuracy) in the test set.

This article assumes you have a solid understanding of neural network concepts, including the feed-forward mechanism, and the back-propagation algorithm, and that you have at least intermediate level programming skills, but does not assume you know anything about dropout training.

and the three hidden node bias value are -2.0, -2.3, -2.6 and -3.0, and the input x-values are 1.0, 3.0, 5.0, then hSums[0] = (1.0)(0.01) + (3.0)(0.05) + (5.0)(0.09) + (-2.0) = -1.39.

and the two output node bias values are 4.0 and 5.0, and the outputs for hidden nodes [0] and [2] are -0.88 and -0.97, then oSums[0] = (-0.88)(0.13) + (-0.97)(0.19) + 4.0 = 3.70.

If the softmax function is used for output layer activation, then the two final outputs of the neural network are softmax(0, 3.70, 4.68) = 0.27 and softmax(1, 3.70, 4.68) = 0.73.

For example, if a neural network has nine hidden nodes and the return from MakeDropNodes is an array of size four with values 0, 2, 6, 8 then there are four drop-nodes at indices [0], [2], [6] and [8].

You obtain a set of training data which has known input values, and known, correct output values, and then use some algorithm to find values for the weights so that computed output values, using the training inputs, closely match the known, correct output values in the training data.

The resulting weight values create a neural network that has extremely high accuracy on the training data, but when presented with new, previously unseen data (test data), the trained neural network has low accuracy.

The most common form of neural network dropout randomly selects half of a neural network's hidden nodes, at each training iteration, and programmatically drops them -- it's as if the nodes do not exist.

For example, the labels could represent the color of a car purchased and the features could represent normalized values such as buyer age, buyer income, buyer credit rating and so on.

The demo creates a 10-15-4 neural network classifier, that is, one with 10 input nodes, 15 hidden processing nodes and four output nodes.

Using back-propagation training without dropout, with 500 iterations and a learning rate set to 0.010, the network slowly improves (the mean squared error gradually becomes smaller during training).

After training, the model achieves 90.50 percent accuracy (181 out of 200 correct) on the test data, and 72.50 percent accuracy (29 out of 40 correct) on the test data.

Although this is just a demonstration, this behavior is often representative of dropout training -- model accuracy on the test data is slightly worse, but accuracy on the test data, which is the metric that's most important, is slightly better.

This article assumes you have a reasonably solid understanding of neural network concepts, including the feed-forward mechanism and the back-propagation algorithm, and that you have at least intermediate level programming skills.

The approach used is to create a utility neural network with random, but known weight and bias values between -9.0 and +9.0, then feed the network random input values:

Dropout Regularization in Deep Learning Models With Keras

A simple and powerful regularization technique for neural networks and deep learning models is dropout.

This means that their contribution to the activation of downstream neurons is temporally removed on the forward pass and any weight updates are not applied to the neuron on the backward pass.

Neighboring neurons become to rely on this specialization, which if taken too far can result in a fragile model too specialized to the training data.

You can imagine that if neurons are randomly dropped out of the network during training, that other neurons will have to step in and handle the representation required to make predictions for the missing neurons.

This in turn results in a network that is capable of better generalization and is less likely to overfit the training data.

This is a binary classification problem where the objective is to correctly identify rocks and mock-mines from sonar chirp returns.

We will evaluate the developed models using scikit-learn with 10-fold cross validation, in order to better tease out differences in the results.

There are 60 input values and a single output value and the input values are standardized before being used in the network.

The baseline neural network model has two hidden layers, the first with 60 units and the second with 30.

The dropout rate is set to 20%, meaning one in 5 inputs will be randomly excluded from each update cycle.

Additionally, as recommended in the original paper on Dropout, a constraint is imposed on the weights for each hidden layer, ensuring that the maximum norm of the weights does not exceed a value of 3.

Neural networks [2.4] : Training neural networks - hidden layer gradient

Dropout

This video is part of the Udacity course "Deep Learning". Watch the full course at

How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)

A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to ...

Lecture 13: Convolutional Neural Networks

Lecture 13 provides a mini tutorial on Azure and GPUs followed by research highlight "Character-Aware Neural Language Models." Also covered are CNN ...

Deep Learning with Tensorflow - Introduction to Autoencoders

Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..

Estimators Revisited: Deep Neural Networks

In this episode of AI Adventures, Yufeng explains how to build models for more complex datasets, using TensorFlow to build deep neural networks! Associated ...

Deep Learning with Tensorflow - Autoencoder Structure

Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..

Simbrain Backprop Part 2: Analyzing Test Data

How to test a Simbrain backprop network on new data, after it's been trained. Also a bit of an introduction to scripting. 3:19 The MTCars dataset. 6:01 Method 1: ...

Reinforcement learning game play example ball receiving 2D - one hidden layer

AI is trying to align the pad with the ball position in x-direction. Neural network with one hidden layer was used.

Lecture 15: Coreference Resolution

Lecture 15 covers what is coreference via a working example. Also includes research highlight "Summarizing Source Code", an introduction to coreference ...