Simple training and evaluation

Oct 13, 2025 by wbrum.org 137 views

**Using KerasClassifier for Training Neural Network: A Comprehensive Guide** ===========================================================

Introduction

In the world of machine learning, neural networks have become a crucial tool for solving complex problems. One such problem is text classification, where we need to classify text into different categories. In this article, we will explore how to use KerasClassifier for training a neural network for binary spam/ham text classification using pre-trained BERT transformer.

What is KerasClassifier?

KerasClassifier is a wrapper around the Keras API that allows us to use scikit-learn's pipeline functionality with Keras models. It provides a simple way to train and evaluate neural networks using scikit-learn's API.

Why Use KerasClassifier?

There are several reasons why you might want to use KerasClassifier:

Easy integration with scikit-learn: KerasClassifier allows you to use scikit-learn's pipeline functionality with Keras models, making it easy to integrate with other scikit-learn tools and techniques.
Simple training and evaluation: KerasClassifier provides a simple way to train and evaluate neural networks using scikit-learn's API.
Flexibility: KerasClassifier allows you to use any Keras model, including pre-trained models like BERT.

Binary Spam/Ham Text Classification using Pre-trained BERT Transformer

In this section, we will explore how to use KerasClassifier for binary spam/ham text classification using pre-trained BERT transformer.

Step 1: Install Required Libraries

To use KerasClassifier, you will need to install the following libraries:

keras
scikit-learn
transformers
pandas
numpy

You can install these libraries using pip:

pip install keras scikit-learn transformers pandas numpy

Step 2: Load Pre-trained BERT Model

To use pre-trained BERT model, you will need to load it using the transformers library:

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

Step 3: Prepare Data

To train a neural network, you will need to prepare your data. In this case, we will use a simple dataset of spam and ham emails:

import pandas as pd
import numpy as np
df = pd.read_csv('spam_ham_dataset.csv')
train_text, test_text, train_labels, test_labels = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)

Step 4: Create KerasClassifier Object

To use KerasClassifier, you will need to create a KerasClassifier object:

from keras.wrappers.scikit_learn import KerasClassifier
def create_model():
model = Sequential()
model.add(Bidirectional(LSTM(64, return_sequences=True), input_shape=(max_length, 1)))
model.add(Bidirectional(LSTM(64)))
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
keras_classifier = KerasClassifier(build_fn=create_model)

Step 5: Train Model

To train the model, you will need to use the fit method:

keras_classifier.fit(train_text, train_labels, epochs=10, batch_size=32, verbose=2)

Step 6: Evaluate Model

To evaluate the model, you will need to use the score method:

accuracy = keras_classifier.score(test_text, test_labels)
print(f'Accuracy: {accuracy:.3f}')

Q&A

Q: What is the difference between KerasClassifier and Keras?

A: KerasClassifier is a wrapper around the Keras API that allows us to use scikit-learn's pipeline functionality with Keras models. Keras is a deep learning library that provides a simple way to build and train neural networks.

Q: How do I use KerasClassifier with pre-trained models like BERT?

A: To use KerasClassifier with pre-trained models like BERT, you will need to load the pre-trained model using the transformers library and then create a KerasClassifier object using the pre-trained model.

Q: How do I evaluate the performance of a KerasClassifier model?

A: To evaluate the performance of a KerasClassifier model, you will need to use the score method to get the accuracy of the model.

Q: Can I use KerasClassifier with other machine learning algorithms?

A: Yes, you can use KerasClassifier with other machine learning algorithms by creating a pipeline that includes the KerasClassifier model and other machine learning algorithms.

Conclusion

In this article, we explored how to use KerasClassifier for training a neural network for binary spam/ham text classification using pre-trained BERT transformer. We also answered some common questions about KerasClassifier and its usage. By following the steps outlined in this article, you should be able to use KerasClassifier to train a neural network for text classification tasks.