Keras Accuracy Not Changing: Troubleshooting Guide
Understanding the Problem
Encountering stagnant accuracy in your Keras model can be frustrating. This usually signifies that your model is not learning from the data. Here’s a breakdown of potential causes and solutions.
Common Causes and Solutions
1. Data Issues
- Data Scaling: Unnormalized or poorly scaled data can hinder training. Apply normalization techniques like MinMaxScaler or StandardScaler.
- Data Imbalance: Unequal class distribution can bias the model towards the majority class. Use techniques like oversampling, undersampling, or weighted loss functions to address this.
- Data Leakage: Accidental inclusion of information from the test set in training can lead to artificially inflated accuracy. Ensure proper data splitting and validation procedures.
2. Model Architecture
- Overfitting: The model memorizes the training data, leading to poor generalization. Use techniques like regularization (L1/L2), dropout, or early stopping.
- Underfitting: The model is too simple and cannot learn complex patterns. Increase model complexity by adding layers, neurons, or using more complex activation functions.
- Incorrect Activation Function: Using an inappropriate activation function in the output layer can prevent the model from achieving desired accuracy.
3. Training Process
- Learning Rate: A high learning rate can cause the model to jump over the optimal solution. Try decreasing the learning rate or using learning rate schedulers.
- Optimizer: Choosing an inappropriate optimizer can impact training convergence. Experiment with different optimizers like Adam, SGD, or RMSprop.
- Batch Size: The batch size affects the learning process. Experiment with different batch sizes to find an optimal setting for your data and model.
- Epochs: Running the training for insufficient epochs might not allow the model to reach its full potential. Increase the number of epochs.
Debugging and Troubleshooting
When faced with stagnant accuracy, a systematic debugging approach is crucial. Here are some steps:
1. Visualize Training Progress
Plot the training and validation loss and accuracy curves to identify patterns and understand model behavior.
import matplotlib.pyplot as plt
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.show()
2. Evaluate Model on Test Data
Ensure the model performs well on unseen data to confirm the accuracy problem isn’t due to overfitting.
3. Monitor Layer Activations
Analyze the activations of different layers to identify potential bottlenecks or areas where learning is stalled.
Example Code
Let’s consider a simple example to illustrate these concepts:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784) / 255.0
x_test = x_test.reshape(10000, 784) / 255.0
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(784,)))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))
# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print('Test Loss:', loss)
print('Test Accuracy:', accuracy)
Conclusion
Stagnant accuracy in Keras can be attributed to a variety of factors. By systematically examining the data, model, and training process, you can identify and address the root cause, leading to improved model performance. Remember, debugging is a crucial part of the machine learning journey.