How to implement learning rate schedules in Keras

by vigneshchennai74 Updated: Oct 26, 2023

Solution Kit

Learning rate schedules are essential tools in optimizing the learning process. It helps with deep learning models. They help adjust the learning rate. This determines the step size taken during gradient descent. Thus leading to faster convergence and improved model performance. By adapting the learning rate over time, learning rate schedulers can overcome challenges. This includes slow convergence, oscillations, or getting stuck in local minima.

There are different types of learning rate schedulers available. These include simple linear regression models or more complex neural networks. Popular learning rate schedules include constant learning rates. Here, the learning rate remains fixed throughout training and adaptive learning rates. It adjusts the learning rate based on the model's performance or other criteria.

Adjust Learning rates in various ways, including per-layer adjustments or global tuning. Per-layer adjustments involve setting different learning rates. It helps different layers of a neural network, allowing more fine-grained control. Global tuning adjusts a single learning rate for the entire network. It simplifies the training process but sacrifices optimization for individual layers. To improve the educational experience, it's vital to influence a learning rate scheduler. This includes choosing the right kind of information for preparing and testing. Additionally, it ensures access to the validation set for monitoring the model's performance. Their effect on the preparation interaction is essential for accomplishing ideal outcomes.

Learning rate schedulers help enhance the experience for learning models. Models can improve performance. It can converge faster and reduce errors by adjusting the learning rate. Custom learning rate schedulers and famous learning rate plans have different changes. Tuning methods enhance the effectiveness of learning rate schedulers. This can happen by understanding associated ideas and best practices. AI Architects can calibrate the preparation cycle and work on model exactness. This helps them achieve quicker and more proficient outcomes in their learning.

Preview of the output that you will get on running this code from your IDE

Code

Keras, a high-level neural networks API, simplifies model construction, training, and evaluation in this program through its customizable callbacks, facilitating the implementation of learning rate schedules and dynamic training adjustments.

Early stopping and learning rate schedule based on custom metric in Keras

PythonLines of Code : 141License : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries :

 def on_epoch_end(self, epoch, logs=None):
     """Called at the end of an epoch.
     ...
     """

from sklearn.metrics import average_precision_score

import keras
from keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau


class MAPHub:
    def __init__(self):
        self.map_value = None

def on_epoch_end(self, epoch, logs):
    """self just a callbcak instance"""
    if self.last_metric_for_epoch == epoch:
        map_ = self.hub.map_value
    else:
        prediction = self.model.predict(self._data, verbose=1)
        map_ = average_precision_score(self._target, prediction)
        self.hub.map_value = map_
        self.last_metric_for_epoch = epoch

class EarlyStoppingByMAP(EarlyStopping):
    def __init__(self, data, target, hub, *args, **kwargs):
        """
        data, target - values and target for the map calculation
        hub - shared object to store _map_ value 
        *args, **kwargs for the super __init__
        """
        # I've set monitor to 'acc' here, because you're interested in metric, not loss
        super(EarlyStoppingByMAP, self).__init__(monitor='acc', *args, **kwargs)
        self._target = target
        self._data = data 
        self.last_metric_for_epoch = -1
        self.hub = hub

    def on_epoch_end(self, epoch, logs):
        """
        epoch is the number of epoch, logs is a dict logs with 'loss' value 
        and metric 'acc' values
        """
        on_epoch_end(self, epoch, logs)      
        logs['acc'] = self.hub.map_value  # "fake" metric with calculated value
        print('Go callback from the {}, logs: \n{}'.format(EarlyStoppingByMAP.__name__, logs))
        super(EarlyStoppingByMAP, self).on_epoch_end(epoch, logs)  # works as a callback fn


class ReduceLROnPlateauByMAP(ReduceLROnPlateau):
    def __init__(self, data, target, hub, *args, **kwargs):
        # the same as in previous
        # I've set monitor to 'acc' here, because you're interested in metric, not loss
        super(ReduceLROnPlateauByMAP, self).__init__(monitor='acc', *args, **kwargs)
        self._target = target
        self._data = data 
        self.last_metric_for_epoch = -1
        self.hub = hub


    def on_epoch_end(self, epoch, logs):
        on_epoch_end(self, epoch, logs)
        logs['acc'] = self.hub.map_value   # "fake" metric with calculated value
        print('Go callback from the {}, logs: \n{}'.format(ReduceLROnPlateau.__name__, logs))
        super(ReduceLROnPlateauByMAP, self).on_epoch_end(epoch, logs)  # works as a callback fn

from keras.datasets import mnist
from keras.models import Model
from keras.layers import Dense, Input
import numpy as np

(X_tr, y_tr), (X_te, y_te) = mnist.load_data()
X_tr = (X_tr / 255.).reshape((60000, 784))
X_te = (X_te / 255.).reshape((10000, 784))


def binarize_labels(y):
    y_bin = np.zeros((len(y), len(np.unique(y)))) 
    y_bin[range(len(y)), y] = 1
    return y_bin

y_train_bin, y_test_bin = binarize_labels(y_tr), binarize_labels(y_te)


inp = Input(shape=(784,))
x = Dense(784, activation='relu')(inp)
x = Dense(256, activation='relu')(x)
out = Dense(10, activation='softmax')(x)

model = Model(inp, out)
model.compile(loss='categorical_crossentropy', optimizer='adam')

hub = MAPHub()  # instentiate a hub
# I will use default params except patience as example, set it to 1 and 5
early_stop = EarlyStoppingByMAP(X_te, y_test_bin, hub, patience=1)  # Patience is EarlyStopping's param
reduce_lt = ReduceLROnPlateauByMAP(X_te, y_test_bin, hub, patience=5)  # Patience is ReduceLR's param

history = model.fit(X_tr, y_train_bin, epochs=10, callbacks=[early_stop, reduce_lt])
Out:
Epoch 1/10
60000/60000 [==============================] - 12s 207us/step - loss: 0.1815
10000/10000 [==============================] - 1s 59us/step
Go callback from the EarlyStoppingByMAP, logs: 
{'loss': 0.18147853660446903, 'acc': 0.9934216252519924}
10000/10000 [==============================] - 0s 40us/step
Go callback from the ReduceLROnPlateau, logs: 
{'loss': 0.18147853660446903, 'acc': 0.9934216252519924}
Epoch 2/10
60000/60000 [==============================] - 12s 197us/step - loss: 0.0784
10000/10000 [==============================] - 0s 40us/step
Go callback from the EarlyStoppingByMAP, logs: 
{'loss': 0.07844233275586739, 'acc': 0.9962269038764738}
10000/10000 [==============================] - 0s 41us/step
Go callback from the ReduceLROnPlateau, logs: 
{'loss': 0.07844233275586739, 'acc': 0.9962269038764738}
Epoch 3/10
60000/60000 [==============================] - 12s 197us/step - loss: 0.0556
10000/10000 [==============================] - 0s 40us/step
Go callback from the EarlyStoppingByMAP, logs: 
{'loss': 0.05562876497630107, 'acc': 0.9972085346550085}
10000/10000 [==============================] - 0s 40us/step
Go callback from the ReduceLROnPlateau, logs: 
{'loss': 0.05562876497630107, 'acc': 0.9972085346550085}
Epoch 4/10
60000/60000 [==============================] - 12s 198us/step - loss: 0.0389
10000/10000 [==============================] - 0s 41us/step
Go callback from the EarlyStoppingByMAP, logs: 
{'loss': 0.0388911374788188, 'acc': 0.9972696414934574}
10000/10000 [==============================] - 0s 41us/step
Go callback from the ReduceLROnPlateau, logs: 
{'loss': 0.0388911374788188, 'acc': 0.9972696414934574}
Epoch 5/10
60000/60000 [==============================] - 12s 197us/step - loss: 0.0330
10000/10000 [==============================] - 0s 39us/step
Go callback from the EarlyStoppingByMAP, logs: 
{'loss': 0.03298293751536124, 'acc': 0.9959456176387349}
10000/10000 [==============================] - 0s 39us/step
Go callback from the ReduceLROnPlateau, logs: 
{'loss': 0.03298293751536124, 'acc': 0.9959456176387349}

Download and install VS Code on your desktop.
Open VS Code and create a new file in the editor.
Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac). The output of your code will appear in the VS Code output console.
Paste the code into your file in VS Code.
Save the file with a meaningful name and the appropriate file extension for Python use (.py).
Open the Python script file in a text editor (e.g., VS Code).
Locate the line of code that starts with Out: in line number 100 (approximately) in your script's output.
Remove the code that starts from this line and continues until the end of the output. This portion of the code is not essential for running the main script.
Before running the code, you need to install the necessary Python packages. Open your command prompt or terminal.
Install the 'absl-py' package Using: pip install absl-py
Install the 'google' package Using: pip install google
Install the 'tensorflow' package (which includes Keras) Using: pip install tensorflow
Install Keras Library using: pip install Keras
Install Scikit-learn Using: pip install scikit-learn
Run the Code

I hope this is useful to you. I have added the version information in the following section.

I found this code snippet by searching "Early stopping and learning rate schedule based on custom metric in Keras" in Kandi. you can try any use case.

Environment Tested

I tested this solution in the following versions. Please be aware of any changes when working with other versions.

The solution is created and tested using Vscode 1.77.2 version
The solution is created in Python 3.7.15 version
The solution is created in Keras 2.12.0 version

This code utilizes the Keras to streamline neural network construction, training, and dynamic adjustment of learning rates using custom callbacks, simplifying complex tasks and improving model performance. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help with the construction, training, and dynamic adjustment of learning rates Keras in Python.

Dependent Library

kerasby keras-team

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58594

Version:v2.13.1-rc0

License: Permissive (Apache-2.0)

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kerasby keras-team

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scikit-learnby scikit-learn

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54584

Version:1.2.2

License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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If you do not have Keras that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Scikit-learn page in kandi.

You can search for any dependent library on kandi like Keras,scikit-learn,tensorflow

FAQ

1. How do custom learning rate schedulers boost deep learning model training speed?

Custom learning rate schedulers improve training speed by adjusting the learning rate. They help models converge faster and achieve better results through adaptive learning rates.

2. What are the benefits of a Constant learning rate in Keras for deep learning model training?

The advantage of using a constant learning rate in Keras is simplicity and stability. It ensures consistent updates to model weights by making the optimization process. It also eliminates the need for complex scheduling algorithms.

3. How to leverage training and testing data for an effective LearningRateScheduler?

To create an effective LearningRateScheduler, manage and use training and testing data. Design the schedule based on data complexity and performance requirements.

4. What are the advantages of using the Keras API when creating a LearningRateScheduler?

The Keras API offers pre-defined schedules and allows customizations. This simplifies implementation and integration with other callbacks for improved performance.

5. How to leverage training and testing data for an effective LearningRateScheduler?

Callback methods can help with LearningRateScheduler to optimize performance. Early Stopping adjusts the learning rate based on validation loss. It also prevents overfitting and improves generalization.

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