Model Training Process for Neural Network

Model Training

  • Objective: Train the neural network model to recognize features from images.

Overview of the Training Process

  • Built model architecture previously, discussed filters and feature maps.
  • Each feature map reflects different characteristics of the input image for model learning.
  • Moving to training where model will learn to minimize loss based on input data.

Code for Model Training

  • Use model.fit() to start training.
    • Parameters:
    • x: Training dataset.
    • validation_data: Validation dataset to check model performance.
    • epochs: Set to 10 initially.
    • training_history: Variable to store the entire training history for future visualization.

Monitoring Loss

  • Track performance by monitoring loss after each epoch.
  • E.g., loss in the first epoch was 4.44, but during the second epoch, loss increased indicating an overshooting issue.

Problems Identified

  • Overshooting Loss Function: Model fails to minimize loss effectively due to issues like high learning rate leading to oscillation.
    • Solution: Decrease Learning Rate.
    • Default learning rate for ADAM is 0.001; adjusted to 0.0001 to improve learning stability.
  • Potential Underfitting: Model may not adequately capture dataset information.
    • Solutions:
    • Increase Number of Neurons: Raise neurons in the dense layer from 1024 to 1500.
    • Add More Convolutional Layers: Add layers to improve feature extraction and model learning.

Dropout Layers

  • Added dropout layers to prevent overfitting by randomly dropping a percentage of neurons during training.
    • First dropout layer: Drop 25% of neurons.
    • Second dropout layer: Drop 40% of neurons after hidden layers.

Adjusting Convolution Layers

  • Implemented padding in the first convolution layer to maintain input feature size and improve learning.
    • Padding set to 'same' for the first layer but removed for subsequent layers to increase training speed by reducing parameters.
  • Resulting model architecture includes:
    • Convolutional layers (with respective filters)
    • Dropout layers to manage overfitting
    • Flattening operation followed by dense layers and output layer setup with softmax activation.

Training Results

  • Trained model showed decreasing loss with:
    • Training Accuracy: 96.84%
    • Validation Accuracy: 95.52%
  • Model training took approximately 30 minutes and confirmed effectiveness of the adjustments made during training setup.

Summary of Key Adjustments

  1. Learning Rate: Decreased from 0.001 to 0.0001 to address overshooting.
  2. Increased Neurons: Raised from 1024 to 1500 to mitigate underfitting.
  3. Additional Convolutional Layer: Improved feature extraction capabilities.
  4. Implemented Dropout: Enhanced model robustness against overfitting.
  5. Padding Adjustments: Balanced model speed and accuracy by modifying padding configurations across layers.

Next Steps

  • In upcoming videos, we will evaluate model performance and explore how to save the trained model.