hidden units in the model.
During the process of dropout, hidden units or inputs (or both) are randomly removed from training for a period of weight updates. Removing the hidden unit from the model is as simple as multiplying the unit’s output by zero. The removed unit’s weights are not lost but rather frozen. Each time that units are removed, the resulting network is referred to as a thinned network. After several weight updates, all hidden and input units are returned to the network. Afterward, a new subset of hidden or input units (or both) are randomly selected and removed for several weight updates. The process is repeated until the maximum training iterations are reached or the optimization procedure converges.
In SAS Viya, you can specify the DROPOUT= option in an ADDLAYER statement to implement dropout. DROPOUT=ratio specifies the dropout ratio of the layer.
Below is an example of dropout implementation in an ADDLAYER statement.
AddLayer/model=’DLNN’ name=”HLayer1” layer={type=’FULLCONNECT’ n=30
act=’ELU’ init=’xavier’ dropout=.05} srcLayers={“data”};
Note: The ADDLAYER syntax is described shortly and further expanded upon throughout this book.
Batch Normalization
The batch normalization (Ioffe and Szegedy, 2015) operation normalizes information passed between hidden layers per mini-batch by performing a standardizing calculation to each piece of input data. The standardizing calculation subtracts the mean of the data and then divides by the standard deviation. It then follows this calculation by multiplying the data by the value of a learned constant and then adding the value of another learned constant.
Thus, the normalization formula is
where gamma
Some deep learning practitioners have dismissed the use of sigmoidal activations in the hidden units. Their dismissal might have been premature, however, with the discovery of batch normalization. Without batch normalization, each hidden layer is, in essence, learning from information that is constantly changing when multiple hidden layers are present in a neural network. That is, a weight update is reliant on second-order, third-order (and so on) effects (weights in the other layers). This phenomenon is known as the internal covariance shift (ICS) (Ioffe and Szegedy, 2015).
There are two schools of thought as to why batch normalization improves the learning process. The first comes from Ioffe and Szegedy who believe batch normalization reduces ICS. The second comes from Santurkar, Tsipras, Ilyas, and Madry who argue that batch normalization is not really reducing ICS but is instead smoothing the error landscape (Santurkar, Tsipras, Ilyas, and Madry 2018). Regardless of which thought prevails, batch normalization has empirically shown to improve the learning process and reduce neuron saturation.
In the SAS deep learning actions, batch normalization is implemented as a separate layer type and can be placed anywhere after the input layer and before the output layer.
Note: With regard to convolutional neural networks, the batch normalization layer is typically inserted after a convolution or pooling layer.
Batch Normalization with Mini-Batches
In the case where the source layer to a batch normalization layer contains feature maps, the batch normalization layer computes statistics based on all of the pixels in each feature map, over all of the observations in a mini-batch. For example, suppose that your network is configured for a mini-batch size of 3, and the input to the batch normalization layer consists of two 5 x 5 feature maps. In this case, the batch normalization layer computes two means and two standard deviations. The first mean would be the mean of all the pixels in the first feature map for the first observation, the first feature map of the second observation, and the first feature map of the third observation. The second mean would be the mean of all of the pixels in the second feature map of the first observation, the second feature map of the second observation, and the second feature map of the third observation, and so on. Numerically, each mean would be the mean of (3 x 5 x 5) = 75 values.
In the case where the source layer to a batch normalization layer does not contain feature maps (for example, a fully connected layer), then the batch normalization layer computes statistics for each neuron in the input, rather than for each feature map in the input. For example, suppose that your network has a mini-batch size of 3, and the input to the batch normalization layer contains 50 neurons. In this case, the batch normalization layer would compute 50 means and 50 standard deviations. The first mean would be the mean of the first neuron of the first observation, the first neuron of the second observation, and the first neuron of the third observation. The second mean would be the mean of the second neuron of the first observation, the second neuron of the second observation, and the second neuron of the third observation, and so on. Numerically, each mean would be the mean of three values. NVIDIA refers to this calculation as per activation mode.
In order for the batch normalization computations to conform to those described in Sergey Ioffe and Christian Szegedy’s batch normalization research (Ioffe and Szegedy, 2015), the source layer should have settings of ACT=IDENTITY and INCLUDEBIAS=FALSE. The activation function that would normally have been specified in the source layer should instead be specified on the batch normalization layer. If you do not configure your model to follow these option settings, the computation will still work, but it will not match the computation as described by Ioffe and Szegedy.
When using multiple GPUs, efficient calculation of the batch normalization transform requires a modification to the original algorithm specified by Ioffe and Szegedy. The algorithm specifies that during training, you must calculate the mean and standard deviation of the pixel values in each feature map, over all of the observations in a mini-batch.
However, when using multiple GPUs, the observations in the mini-batch are distributed over the GPUs. It would be very inefficient to try to synchronize each GPU’s batch normalization calculations for each batch normalization layer. Instead, each GPU calculates the required statistics using a subset of available observations and uses those statistics to perform the transformation on those observations.
Research communities are still debating whether small or large minibatch sizes yield better performance. However, when a minibatch of observations is distributed across multiple GPUs, and the model contains batch normalization layers, the deep learning team at SAS recommends that you use reasonably large-sized mini-batches on each GPU so that the statistics will be stable.
In addition to calculating feature map statistics on each mini-batch, the batch normalization algorithm also needs to calculate statistics over the entire training data set before saving the training weights. These statistics are the ones used for scoring (whereas the mini-batch statistics are used for training). Rather than perform an extra epoch at the end of training, the statistics from each mini-batch are averaged over the course of the last training epoch to create the epoch statistics.
The statistics computed in this way are a close approximation to the more complicated computation that uses an extra epoch with fixed weights (as long as the weights in the last epoch do not change much) after each mini-batch of the epoch. (This is usually the case for the last training epoch.) When using multiple GPUs, this calculation is performed exactly the same way as when using a single GPU. That is, the statistics for each mini-batch on each GPU are averaged after each mini-batch to compute the final epoch statistics for scoring.
Traditional Neural Networks versus Deep Learning
Recall the differences between traditional neural networks and deep learning are shown in Table 1.2. Traditional neural networks leveraged the computation of a single central processing unit (CPU) to train the model. However, graphical processing units (GPUs) have a design that naturally fits well with the structure and learning process of neural networks. There have been promising developments in the use of CPUs grouped together that use