Training

Model training refers to the process of teaching a machine learning model to make predictions or perform a specific task by adjusting its internal parameters based on the input data. During training, the model learns to associate input data with corresponding output labels or targets, gradually improving its ability to make accurate predictions.

Here's an overview of the model training process:

1. Initialization: The model's parameters (weights and biases) are initialized randomly or with predefined values.

2. Forward Pass: Input data is passed through the model, and predictions are generated based on the current parameter values. These predictions are compared to the actual targets using a predefined loss function.

3. Loss Calculation: The loss function quantifies the difference between the model's predictions and the actual targets. It provides a measure of how well the model is performing on the given data.

4. Backpropagation: The gradients of the loss function with respect to the model's parameters are computed using backpropagation. This involves calculating the partial derivatives of the loss function with respect to each parameter, which indicates how much each parameter contributed to the overall error.

5. Parameter Update: The model's parameters are updated using an optimization algorithm (e.g., gradient descent) to minimize the loss function. The gradients computed during backpropagation guide the direction and magnitude of parameter updates, aiming to reduce the loss and improve the model's performance.

6. Iteration: Steps 2-5 are repeated for multiple iterations or epochs, with the model gradually refining its parameters to better fit the training data. Each iteration consists of a forward pass, loss calculation, backpropagation, and parameter update.

7. Validation: Optionally, the model's performance is evaluated on a separate validation dataset to monitor its generalization ability and prevent overfitting. This helps ensure that the model is learning meaningful patterns from the data rather than memorizing specific examples.

8. Convergence: Training continues until a stopping criterion is met, such as reaching a maximum number of epochs, achieving satisfactory performance on the validation dataset, or observing no significant improvement in performance over several iterations.

9. Evaluation: Once training is complete, the final trained model can be evaluated on unseen test data to assess its performance and generalization ability. This provides an estimate of how well the model is expected to perform in real-world scenarios.

Let's train or model then:

1. Batch Size and Epochs:

   • Before training the model, we define two important parameters: batch_size and epochs.

   • batch_size: Specifies the number of samples processed at once before updating the model's parameters. A smaller batch size consumes less memory but may result in slower training, while a larger batch size may lead to faster training but requires more memory.

   • epochs: Specifies the number of times the entire training dataset is passed forward and backward through the model. Each epoch consists of multiple iterations over the dataset, with the model learning from each iteration.

   batch_size = 32
   epochs = 10

2. Model Training:

   • We use the fit() method to train the model on the training data (train_data) and corresponding labels (train_labels).

   • validation_split=0.2: Specifies that 20% of the training data will be used as validation data, which is used to monitor the model's performance during training and prevent overfitting.

   • The batch_size and epochs parameters are passed to the fit() method to control the training process.

   
   history = model.fit(
       train_data, 
       train_labels, 
       validation_split=0.2, 
       batch_size=batch_size, 
       epochs=epochs
   )

3. Monitoring Training Progress:

   • During training, the model's loss and accuracy are continuously calculated and recorded for both the training and validation datasets.

   • After training is complete, the fit() method returns a history object containing this training history, which includes the loss and accuracy values for each epoch.

4. Visualization:

   • We plot the training and validation loss as well as the training and validation accuracy over epochs to visualize how these metrics change during training.

   • This helps us assess whether the model is learning effectively and whether it's overfitting or underfitting the data.

# plot loss and accuracy
import matplotlib.pyplot as plt
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
plt.show()

plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.legend()
plt.show()