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We return to explore the intricate effects a deep learning model's learning rate has on its outputs. By understanding how tuning the learning rate influences its model's performance, less maintenance and time would be required to create an accurate and adaptive deep learning model.

How learning rate affects model performance

If the learning rate is too large, gradient descent can overshoot the minimum, leading to instability and potentially preventing convergence. Conversely, if the learning rate is too small, gradient descent can be slow to converge, especially in flat regions of the loss landscape.

In the right linear plot of the image below are various outputs from different learning rates. A good learning rate decreases the loss function consistently, reaches a near-zero global minimum, and avoids overfitting.

Effects of different learning rates and optimization algorithms on Python

Every time you want your deep learning model to analyze images, you need to convert them into a readable format for the neural network architecture. A technique for optimizing image deep learning is image scaling, which resizes images to a desired size while preserving their aspect ratio. This is done to ensure that all input images have the same dimensions and can be processed efficiently by the neural network.

Varying the number of neurons in a neural network can teach it to analyze different representations of the input data, potentially improving its ability to generalize. Having fewer neurons in later layers can act as a form of regularization, preventing the model from overfitting.

This method additionally allows different layers to extract features at different levels of abstraction, from more complex to simpler details as seen in the image below.

The following training are run in the following learning rate order: 0.1, 0.01, and 0.001. Over 50 epochs and a batch size of 256 per forward and backward pass, we will mainly observe the plotted outputs of different runs for future cross-comparison.

The first optimization algorithm to be tested is stochastic gradient descent (SGD): 

The second algorithm is root mean square propagation (RMSprop):

The third algorithm is Adaptive Gradient Algorithm (Adagrad):

The final algorithm is Adaptive Moment Estimation (Adam):