Solved: adam optimizer keras learning rate degrade

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Deep learning models have become a significant aspect of technology in today’s era, and different optimization algorithms like Adam Optimizer play a crucial role in their execution. Keras, a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models, wraps the efficient numerical computation libraries Theano and TensorFlow. The importance of adjusting the learning rate in such optimization algorithms is paramount, as it can directly influence the model’s learning process. In this article, we’ll discuss how to degrade the learning rate in Adam optimizer in Keras in a step-by-step manner. Here we will also cover libraries and functions involved in this process.

The Necessity of Learning Rate Adjustment

Learning rate is an important hyperparameter in optimization algorithms, including Adam optimizer. It determines the step size at each iteration while moving towards the minimum of the loss function. Specifically, a lower learning rate requires more training epochs given the smaller steps in the weight updates, whereas a larger learning rate might reach a convergence point faster, but risks overshooting the minimum of the loss function.

Hence, it’s a common approach to adjust and degrade the learning rate over epochs, often referred to as learning rate decay. Learning rate decay ensures that the learning model gets to the bottom of the loss function, avoiding large steps in the later training phase that might cause significant fluctuations.

Implementation of Learning Rate Decay in Keras

In Keras, this adjustment can be attained with the help of LearningRateScheduler and ReduceLROnPlateau callback functions.

```from keras.callbacks import LearningRateScheduler
import numpy as np

# Learning rate schedule
initial_learning_rate = 0.1
decay = initial_learning_rate / epochs

def lr_time_based_decay(epoch, lr):
return lr * 1 / (1 + decay * epoch)

# Fit the model on the batches generated
model.fit(X_train, Y_train, epochs=epochs,callbacks=[LearningRateScheduler(lr_time_based_decay, verbose=1)])
```

The LearningRateScheduler, in accordance with epochs, can alter the learning rate. Whereas, ReduceLROnPlateau monitors a quantity and if no improvement is seen for a ‘patience’ number of epochs, the learning rate is reduced.

Working with Adam Optimizer

When dealing with Adam optimizer, we initialize its instance by specifying the learning rate. During the model compilation process, we input this optimizer instance.

```from keras.optimizers import Adam

# Applying learning rate decay
adam_opt = Adam(lr=0.001, decay=1e-6)
model.compile(loss='binary_crossentropy', optimizer=adam_opt)
```

In the code above, we’re assigning adam_opt the Adam optimizer with a learning rate of 0.001 and a decay rate of 1e-6.

In conclusion, the learning rate regulates how we navigate towards the minimum cost function. By efficiently adjusting this learning rate, we can enhance our model’s performance and efficiency. The combination of Keras with Python makes it a straightforward task to adjust learning rates giving us more control over our model optimization processes.

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