CIFAR-10 CNN: Training & Evaluation (Phase 3)

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Hey guys! In this article, we're diving deep into Phase 3 of our CIFAR-10 Convolutional Neural Network (CNN) project. This is where the rubber meets the road – we're going to train the CNN we built earlier using the CIFAR-10 dataset and then rigorously evaluate its performance. Our primary goal here is to see how well our model learns to recognize those visual patterns hidden within the images and analyze its overall behavior using key metrics like precision and loss.

Objectives

Before we get our hands dirty with the code, let's lay out our objectives for this phase. We've got a few key targets we're aiming for:

  1. Compile the Model: First, we need to compile our model, making sure we're using the right parameters for multi-class classification. This step is crucial because it sets the stage for how our model will learn.
  2. Train the CNN: Next up, we'll train our CNN. This is where the magic happens! We'll feed the model the CIFAR-10 data and watch it learn over time. We'll also keep a close eye on the training process, recording how the model's learning evolves.
  3. Visualize Training Progress: To really understand what's going on, we'll visualize the training and validation curves. These curves will show us the trends in precision and loss, giving us valuable insights into the model's learning behavior.
  4. Evaluate Final Accuracy: Once the training is done, we'll evaluate the final accuracy of our model using the test dataset. This will give us a solid number to represent how well our model performs on unseen data.
  5. Analyze Overall Performance: Finally, we'll take a step back and analyze the overall performance of our model. We'll look for any signs of overfitting or other issues that might affect the model's ability to generalize.

Tasks

Alright, let's break down the tasks we need to accomplish to achieve these objectives. We'll be working through these steps methodically to ensure we get the best results.

1. Compiling the Model

The first thing we need to do is compile our model. This involves configuring the learning process by specifying the optimizer, loss function, and metrics we want to track. For our multi-class classification task, we'll use the following:

model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
  • Optimizer: We're using the Adam optimizer, which is a popular choice for its efficiency and adaptability. Adam adjusts the learning rates of each weight individually, making the training process smoother and faster.
  • Loss Function: For our loss function, we're using categorical_crossentropy. This is the go-to loss function for multi-class classification problems. It measures the difference between the predicted probability distribution and the true distribution of classes.
  • Metrics: We're tracking accuracy as our primary metric. Accuracy gives us a straightforward measure of how many images our model is classifying correctly.

2. Training the Model with .fit()

Now comes the exciting part: training our CNN! We'll use the .fit() method to feed the CIFAR-10 data to our model and let it learn. Here are the key parameters we'll set:

history = model.fit(
    x_train,
    y_train,
    epochs=10,
    validation_split=0.1
)
  • Epochs: We're setting the number of epochs to 10, which means the model will go through the entire training dataset 10 times. Ten epochs is a good starting point, but we might need to adjust this based on our results. More epochs can lead to better learning, but also a higher risk of overfitting.
  • Validation Split: We're using validation_split=0.1, which means 10% of our training data will be set aside for validation. This validation set helps us monitor the model's performance on data it hasn't seen before, giving us an idea of how well it will generalize to new images.
  • History: We're saving the training history in a variable called history. This object will contain a record of the loss and accuracy at each epoch, which we'll use to visualize the training process later.

3. Graphing the Learning Evolution

Visualizing the learning process is crucial for understanding how our model is performing. We'll create two plots:

  • Training vs. Validation Accuracy
  • Training vs. Validation Loss

These plots will show us how the accuracy and loss change over epochs, both for the training data and the validation data. This will help us identify potential issues like overfitting or underfitting. Here's how we can create these plots using matplotlib:

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))

plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Model Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Model Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

Let's break down what each part of this code does:

  • import matplotlib.pyplot as plt: This line imports the matplotlib.pyplot library, which we'll use for plotting.
  • plt.figure(figsize=(12, 6)): This creates a new figure with a specified size. Adjusting the figsize ensures the plots are clear and readable.
  • plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2): These lines create subplots within our figure. We're creating two subplots side by side: one for accuracy and one for loss.
  • plt.plot(...): These lines plot the training and validation accuracy and loss values. We're using the history object (from the .fit() method) to access these values.
  • plt.title(...), plt.xlabel(...), plt.ylabel(...): These lines add titles and labels to our plots, making them easier to understand.
  • plt.legend(): This adds a legend to our plots, so we can easily distinguish between the training and validation curves.
  • plt.show(): This displays the plots.

4. Evaluating the Model with .evaluate()

Once our model is trained, we need to evaluate its performance on the test dataset. This will give us an unbiased estimate of how well our model will perform on new, unseen images. We'll use the .evaluate() method for this:

loss, accuracy = model.evaluate(x_test, y_test)
print(f'Test Accuracy: {accuracy}')
  • model.evaluate(x_test, y_test): This line evaluates the model on the test data (x_test and y_test). It returns the loss and accuracy.
  • print(f'Test Accuracy: {accuracy}'): This line prints the final test accuracy, giving us a clear indication of our model's performance.

5. Analyzing for Overfitting

One of the most critical aspects of training a neural network is to ensure it's not overfitting the training data. Overfitting occurs when the model learns the training data too well, including its noise and specific details, and fails to generalize to new data. We can detect overfitting by comparing the training and validation performance.

Here’s what to look for:

  • High training accuracy and low validation accuracy: This is a classic sign of overfitting. The model is performing well on the training data but poorly on the validation data, indicating it’s memorized the training set rather than learning general patterns.
  • Training loss decreasing while validation loss increases: This also suggests overfitting. As the model trains, it gets better at minimizing the loss on the training set, but its ability to generalize diminishes, leading to higher loss on the validation set.
  • Large gap between training and validation curves: In our accuracy and loss plots, a significant gap between the training and validation curves indicates overfitting. The training curve might show excellent performance, while the validation curve plateaus or worsens.

If we detect overfitting, we can take several measures to address it:

  • Increase the amount of training data: More data can help the model learn more general patterns and reduce overfitting.
  • Use data augmentation: Augmenting the training data by applying transformations such as rotations, flips, and zooms can help the model generalize better.
  • Add regularization techniques: Techniques like L1 or L2 regularization can penalize large weights, preventing the model from becoming too complex and overfitting.
  • Use dropout: Dropout randomly sets a fraction of the neurons to zero during training, which prevents the model from relying too heavily on specific neurons.
  • Early stopping: Monitor the validation loss and stop training when it starts to increase, even if the training loss is still decreasing.

6. Documenting Results and Conclusions

Finally, it’s crucial to document all our results and conclusions in a structured manner. This includes:

  • Final Test Accuracy: Clearly state the final accuracy achieved on the test set.
  • Graphs: Include the generated plots of training and validation accuracy and loss.
  • Analysis of Overfitting: Discuss whether overfitting occurred, the signs you observed, and any steps taken to mitigate it.
  • Overall Performance: Provide a summary of the model's performance, including strengths and weaknesses.

Code Example for Graphs

Here’s the Python code we’ll use to generate the accuracy and loss graphs using matplotlib:

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='Training')
plt.plot(history.history['val_accuracy'], label='Validation')
plt.title('Model Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

plt.plot(history.history['loss'], label='Training')
plt.plot(history.history['val_loss'], label='Validation')
plt.title('Model Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

This code snippet will generate two graphs: one showing the training and validation accuracy over epochs, and another showing the training and validation loss over epochs. These graphs are crucial for diagnosing the model's learning behavior and detecting overfitting.

Completion Criteria

To ensure we've successfully completed this phase, we need to meet the following criteria:

  • The model is compiled and trained correctly.
  • Graphs of accuracy and loss are generated and interpreted in Markdown.
  • The final accuracy on the test set (test_acc) is reported.
  • An analysis of the model’s performance and potential overfitting is documented.

By systematically following these steps and documenting our findings, we'll gain valuable insights into our CNN's performance and be well-prepared for the next phase of our project.

Conclusion

Alright, guys, we've covered a lot in this article! We've gone through the process of training and evaluating our CIFAR-10 CNN, including compiling the model, visualizing training progress, evaluating final accuracy, and analyzing for overfitting. Remember, this phase is crucial for understanding how well our model learns and generalizes. By carefully following these steps and documenting our results, we're setting ourselves up for success in the next phases of our project. Keep up the great work, and I'll see you in the next article!