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Introduction To Machine Learning
  1. What Is Machine Learning Beginners Guide
  2. Supervised Vs Unsupervised Learning Key Differences
  3. Scikit Learn Tensorflow Keras Beginners Guide
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Data Preprocessing And Feature Engineering
  1. Understanding Data Types Machine Learning
  2. Handling Missing Data Outliers Data Preprocessing
  3. Feature Scaling Normalization Vs Standardization
  4. Feature Selection Dimensionality Reduction Pca Lda
Supervised Learning With Scikit Learn
  1. Master Scikit Learn Basics Api Data Splitting Workflows
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  5. Master Support Vector Machines Svm Classification
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Unsupervised Learning With Scikit Learn
  1. Introduction To Clustering Kmeans Dbscan Hierarchical
  2. Master Pca Dimensionality Reduction Scikit Learn
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Introduction To Deep Learning Tensorflow Keras
  1. What Is Deep Learning Differences Applications
  2. Introduction To Tensorflow Keras Deep Learning
  3. Understanding Neural Networks Beginners Guide
  4. Activation Functions Relu Sigmoid Softmax Neural Networks
  5. Backpropagation Optimization Deep Learning
Building Neural Networks With Keras
  1. Build Simple Neural Network Keras Guide
  2. Improve Neural Network Performance Keras Dropout Batch Norm
  3. Hyperparameter Tuning Keras Tuner Guide
Cnns For Image Processing
  1. Introduction To Cnns For Image Processing
  2. Build Cnn Mnist Image Classification Keras
  3. Boost Cnn Performance Data Augmentation Transfer Learning
Rnns And Lstms
  1. Understanding Rnns Lstms Time Series Data
  2. Build Lstm Stock Price Prediction Tensorflow
  3. Text Generation Lstms Tensorflow Keras
Natural Language Processing
  1. Text Preprocessing Nlp Tokenization Word Embeddings
  2. Sentiment Analysis Lstm Tensorflow Keras
  3. Text Classification Bert Tensorflow Keras Guide
Deploying Machine Learning Models
  1. Exporting Models Tensorflow Scikit Learn
  2. Deploy Machine Learning Models Flask Fastapi
  3. Deploying Ml Models To Cloud Platforms
All Course > Python Machine Learning > Building Neural Networks With Keras Oct 24, 2024

How to Split Data for Training, Validation, and Testing in Keras

In the previous lesson, we built a simple neural network using Keras. We learned how to define layers, compile the model, and run it on a dataset. However, we didn't focus on how to evaluate the model's performance properly. This is where splitting data into training, validation, and testing sets becomes crucial. Without proper data splitting, we risk overfitting, which means our model might perform well on training data but fail on new, unseen data.

In this lesson, we’ll dive into why splitting data is necessary, the roles of each dataset, and how to implement this in Keras. By the end, you’ll know how to monitor your model’s performance during training and ensure it generalizes well to new data.

Why Splitting Data is Necessary to Avoid Overfitting

When I first started building neural networks, I made the mistake of training my model on the entire dataset without splitting it. The model achieved 99% accuracy during training, but when I tested it on new data, the accuracy dropped to 60%. This happened because the model memorized the training data instead of learning patterns that generalize to new data. This is called overfitting.

Splitting data into training, validation, and testing sets helps avoid overfitting. The training set is used to teach the model, the validation set helps tune hyperparameters, and the testing set evaluates the final model’s performance. By using separate datasets, we ensure the model learns patterns that work well on unseen data.

The Roles of Training, Validation, and Testing Sets

Each dataset plays a unique role in building a robust neural network. The training set is the largest portion of the data, and it’s used to train the model. The model learns patterns from this data by adjusting its weights and biases.

The validation set is used to evaluate the model during training. It helps us tune hyperparameters like learning rate or the number of layers. For example, if the model performs well on the training set but poorly on the validation set, it’s a sign of overfitting.

The testing set is used only once, after training is complete. It gives us an unbiased evaluation of the model’s performance. Think of it as the final exam for your model. If the model performs well on the testing set, it’s ready for real-world use.

How to Split Data Using Keras Utilities

Keras provides simple tools to split data into training, validation, and testing sets. One of the most common methods is using the train_test_split function from the sklearn.model_selection module. Here’s how I implemented it in one of my projects:

from sklearn.model_selection import train_test_split

# Load your dataset
X, y = load_data()

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Further split training data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

In this example, 20% of the data is reserved for testing, and 20% of the remaining data is used for validation. The random_state ensures the split is reproducible. This method is simple and works well for most cases.

Monitoring Model Performance During Training

Once the data is split, we need to monitor the model’s performance during training. Keras makes this easy by allowing us to pass the validation set to the fit method. Here’s an example:

model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))

During training, Keras will print the loss and accuracy for both the training and validation sets after each epoch. If the training accuracy keeps improving while the validation accuracy stalls or drops, it’s a sign of overfitting. In such cases, we can adjust the model’s architecture or add regularization techniques, which we’ll cover in the next lesson.

Conclusion

Splitting data into training, validation, and testing sets is a critical step in building neural networks. It helps us avoid overfitting and ensures our model generalizes well to new data. By using tools like train_test_split and monitoring performance with validation data, we can build models that perform reliably in real-world scenarios.

In the next lesson, we’ll explore techniques like Dropou and batch normalization to further improve model performance. These methods help prevent overfitting and make training more stable.

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