How to Change Learning Rate in Keras

In deep learning, the learning rate is an important hyperparameter that controls the weights of a neural network during the training process. It helps to control the speed or rate of the model learns from the training data. A higher learning rate updates the weights more quickly and a lower learning rate updates the weights more slowly. The optimal learning rate depends on the model architecture and optimizer such as Adagrad, RMSprop, and SGD. The learning rate for deep learning models is usually between 0.001 and 0.1. It often requires experiments and tuning to find out the optimal value.

Here is some fact about the learning rate –

• Larger size neural networks require smaller learning rates.
• Poor quality training data may require smaller learning rates.
• Initialization of the learning rate always starts with a smaller value.

Some Common Strategies for Finding an Appropriate Learning Rate

1. Manual tuning – Start with a smaller learning rate and increase it as your choice until a satisfactory result is achieved. Observed the training process and updated the learning rate based on model behavior.

import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense

model = keras.Sequential()
#addind dense layers
model.add(Dense(10,activation='relu'))
model.add(Dense(10,activation='relu'))
model.add(Dense(1,activation='sigmoid'))
# Set the learning rate to 0.01
optimizer = keras.optimizers.Adam(learning_rate=0.001)
#compike the model
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

This code creates a sequential model with three dense layers. The learning rate is  0.001 and the model is compiled by the Adam optimizer.

• loss='binary_crossentropy' sets the loss function to binary cross-entropy. It is commonly used for binary classification.
• optimizer=optimizer assigns the created optimizer (RMSprop with the specified parameters) to the model.
• metrics=['accuracy']indicates that we want to monitor and track the accuracy metrics during the training process
• learning_rate=0.001  the learning rate for this optimizer is 0.001

2. Learning rate scheduler – Implementing a predefined scheduler, such as reducing the learning rate by a certain factor after a fixed number of epochs, can be beneficial in tuning the learning rate during training to achieve better model performance.

import tensorflow as tf

# Define a simple neural network.
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Define the learning rate schedule.
learning_rate_scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate=0.01,
    decay_steps=1000,
    decay_rate=0.96
)

# Compile the model.
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_scheduler)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

• tf.keras.optimizers.schedules.ExponentialDecay(...): This line creates an instance of the ExponentialDecay class from the tf.keras.optimizers.schedules module. The ExponentialDecay class represents an exponential decay learning rate schedule.
• initial_learning_rate=0.01: This parameter specifies the initial learning rate for the scheduler. In this example, the initial learning rate is  0.01.
• decay_steps=1000: The specified parameter determines the frequency at which the learning rate will decay, occurring every 1000 steps during the training process.
• decay_rate=0.96:This parameter controls the rate at which the learning rate decreases at each step during training.
• tf.keras.optimizers.Adam(learning_rate=learning_rate_scheduler): This line creates an instance of the Adam optimizer from the tf.keras.optimizers module. The learning_rate parameter is set to the previously created learning_rate_scheduler object.
• model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']): This line compiles the model by specifying the optimizer, loss function, and metrics to be used during training. The optimizer argument is set to the previously created optimizer object. The metrics argument is set to [‘accuracy’], indicating that the accuracy metric should be computed and reported during training.

3. Adaptive learning rate methods – Adam (Adaptive Moment Estimation) and RMSProp are optimization techniques commonly used in deep learning. Adaptive learning rate methods adjust the learning rate based on the gradient and previous updates, optimizing convergence speed and accuracy by adapting to the specific characteristics of the optimization problem.

import tensorflow as tf

# Define your model architecture
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Compile the model with RMSProp optimizer
optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001,
                                         rho=0.9,
                                         momentum=0.5)  # Set the learning rate
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

• The line above creates an instance of the RMSprop optimizer from tf.keras.optimizers the module.
• rho=0.9 is a parameter specific to RMSprop. It represents the decay rate used for the moving average of the squared gradient. It typically ranges between 0.8 and 0.9.
• momentum=0.5 float defaults value 0.0. When the momentum value is not 0.0, the optimizer will track the momentum value with a decay rate equal to 1 – momentum. This means that the momentum value will decrease over time, giving more weight to recent gradients and less weight to older gradients.

Overall, finding the correct learning rate requires iterative experimentation and careful observation of the model’s behavior. It involves striking a balance between convergence speed and achieving desirable model performance in deep learning tasks.

Also read: Convert Numpy Array into Keras Tensor