Deep Neural Networks and Dense Network Configuration: A Detailed Guide
1. Introduction
This guide explores two main types of network configurations for machine learning: Deep Neural Networks (DNNs) and Dense Networks. These configurations will help you optimize your models based on their complexity, depth, and intended deployment. We’ll also address special exceptions for sparse models, low-latency applications, and models optimized for mobile devices.
2. Exceptions for Special Cases
Sparse Models
If you need a sparse model, meaning a model where most weights are zero, you can use L1 Regularization to encourage sparsity. Optionally, you can zero out tiny weights after training, further compressing the model. You can also use the TensorFlow Model Optimization Toolkit (TF-MOT) for even more efficient model optimization, though it breaks self-normalization, so SELU activations should be avoided.
Low-Latency Models
If you need a low-latency model for fast predictions, consider using fewer layers, fast activation functions like ReLU or Leaky ReLU, and folding batch normalization layers into the previous layers after training. Reducing the precision of floating-point calculations to 16 or even 8 bits can further speed up performance.
Deploying Models to Mobile or Embedded Devices
For models deployed on mobile or embedded devices, the TensorFlow Model Optimization Toolkit (TF-MOT) can help compress and optimize your model. Techniques like quantization and pruning can reduce the model’s size while maintaining performance.
3. Risk-Sensitive Applications and Inference Latency
When working with risk-sensitive applications, such as financial systems or medical diagnostics, it’s essential to get reliable uncertainty estimates. You can use Monte Carlo (MC) Dropout during inference to simulate uncertainty and improve probability estimates, which is critical in decision-making models.
Additionally, for applications where inference latency is crucial, such as real-time systems, low-latency models (as mentioned earlier) will help you achieve the necessary performance.
Most of these techniques can be achieved using the high-level Keras API in TensorFlow. However, if you need more control over your model, such as writing custom loss functions or optimizing the training process, TensorFlow’s low-level API gives you that flexibility.
Configuration Tables
Default DNN Configuration
| Hyperparameter | Default Value | Why Use It | Example Use Case | Code Snippet |
|---|---|---|---|---|
| Kernel Initializer | He Initialization | Ensures proper variance in deep layers, avoids exploding/vanishing gradients | Any deep network with ReLU activation, e.g. Image classification | initializer=tf.keras.initializers.HeNormal() |
| Activation Function | ReLU (shallow) | Swish (deep) | ReLU helps avoid vanishing gradients, Swish smooths gradients in deep layers | Shallow CNNs (ReLU) | Deep learning research models (Swish) | activation=tf.nn.relu |
| Normalization | None (shallow) | Batch Norm (deep) | Deep networks need normalization to stabilize activations and gradients | Deep networks like ResNet benefit from batch normalization | tf.keras.layers.BatchNormalization() |
| Regularization | Early Stopping | Weight Decay (L2) | Prevents overfitting by stopping training early or penalizing large weights | Regularizing large networks on large datasets | kernel_regularizer=tf.keras.regularizers.l2(0.01) |
| Optimizer | NAG | AdamW | Efficient optimization, faster convergence | Image classification or NLP models | optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) |
| Learning Rate Schedule | Performance Scheduling | 1cycle | Dynamically adjusts learning rate for better convergence | Any model that benefits from adaptive learning rates | tf.keras.callbacks.LearningRateScheduler() |
Default Dense Network Configuration
| Hyperparameter | Default Value | Why Use It | Example Use Case | Code Snippet |
|---|---|---|---|---|
| Kernel Initializer | LeCun Initialization | Works best with SELU activations to maintain proper variance | Dense layers in self-normalizing networks, e.g. simple classification tasks | initializer=tf.keras.initializers.lecun_normal() |
| Activation Function | SELU | Enables self-normalization, maintains stable activations through layers | Self-normalizing deep networks | activation=tf.keras.activations.selu |
| Normalization | None (Self-normalization) | No explicit normalization required due to SELU’s self-normalizing properties | Self-normalizing networks | N/A |
| Regularization | Alpha Dropout | Prevents overfitting while maintaining self-normalization | Regularization in SELU-based networks | tf.keras.layers.AlphaDropout() |
| Optimizer | NAG | Efficient optimization for faster convergence | Dense networks for small datasets | optimizer=tf.keras.optimizers.SGD(nesterov=True) |
| Learning Rate Schedule | Performance Scheduling | 1cycle | Dynamically adjusts learning rate for improved convergence | Small dense networks with limited layers | tf.keras.callbacks.LearningRateScheduler() |