5 Best Ways to Use TensorFlow to Verify Predictions for Fashion MNIST in Python

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πŸ’‘ Problem Formulation: When working with the Fashion MNIST dataset in Python, it is crucial to verify the predictions made by your model. This ensures accuracy and reliability in classification tasks, such as determining if an image is a dress, shoe, or shirt. The goal is to use TensorFlow to evaluate these predictions against test data, improving the model’s performance through error analysis.

Method 1: Visual Verification with Matplotlib

This method involves visualizing predictions using Matplotlib, giving a direct visual comparison between the predicted and actual labels. By plotting images alongside their predicted and true labels, you can manually check for discrepancies. This is beneficial for getting a quick sense of model performance on individual examples.

Here’s an example:

import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist

# Load the Fashion MNIST dataset
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Build, compile, and train your TensorFlow Model here...

# Make a prediction
predictions = model.predict(test_images)

# Function to display image, prediction, and truth
def display_prediction(index):
    plt.imshow(test_images[index], cmap=plt.cm.binary)
    plt.title(f"Predicted: {predictions[index]}, Actual: {test_labels[index]}")
    plt.show()

# Display an example
display_prediction(0)

The output will be a window displaying a Fashion MNIST test image with its predicted and true label titles.

This code snippet demonstrates how to visually compare the model’s prediction with the actual label by displaying the image and titles using Matplotlib. This visual method is quick to implement and gives an immediate sense of how the model is performing.

Method 2: Confusion Matrix

Constructing a confusion matrix is a more comprehensive method to evaluate the overall performance of the classification model. This matrix provides a summary of prediction results on a classification problem. Each row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class.

Here’s an example:

import numpy as np
import seaborn as sns
from sklearn.metrics import confusion_matrix
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist

# Load the Fashion MNIST dataset and train your model...

# Make a prediction
predictions = model.predict(test_images)

# Convert predictions to class indices
pred_classes = np.argmax(predictions, axis=1)

# Generate the confusion matrix
conf_matrix = confusion_matrix(test_labels, pred_classes)

# Plot the confusion matrix
sns.heatmap(conf_matrix, annot=True, fmt='d')
plt.show()

The output will be a heatmap representing the confusion matrix for the classification predictions.

This code snippet generates and plots a confusion matrix using Seaborn and sklearn’s confusion_matrix function, providing an insight into the types of errors made by the classifier.

Method 3: Classification Report

Creating a classification report is useful to evaluate the precision, recall, F1 score, and support for each class. This is a performance evaluation method that gives a deeper understanding of the classification accuracy for each label in the Fashion MNIST dataset.

Here’s an example:

from sklearn.metrics import classification_report
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist

# Load the Fashion MNIST dataset and train your model...

# Make a prediction
predictions = model.predict(test_images)

# Convert predictions to class indices
pred_classes = np.argmax(predictions, axis=1)

# Get the classification report
report = classification_report(test_labels, pred_classes)
print(report)

The output is a tabulated report showing precision, recall, F1 score, and support for each class.

The snippet uses sklearn’s classification_report function to compute and display a summary of the classification metrics for each class, aiding in understanding where the model might be under- or over-performing.

Method 4: Sample-wise Error Analysis

Conducting a sample-wise error analysis is helpful to identify and investigate individual cases where the model has made incorrect predictions. This method digs deeper into specific errors to understand the model’s behavior better.

Here’s an example:

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist

# Load the Fashion MNIST dataset and train your model...

# Make a prediction
predictions = model.predict(test_images)

# Convert predictions to class indices
pred_classes = np.argmax(predictions, axis=1)

# Find indices of wrong predictions
incorrect_indices = np.where(pred_classes != test_labels)[0]

# Analyze errors
for index in incorrect_indices:
    print(f"Index: {index}, Predicted: {pred_classes[index]}, Actual: {test_labels[index]}")

This snippet does not produce visual output but prints indices where the predictions did not match the true labels.

Using NumPy, the code identifies and lists the specific test cases where the model’s predictions were incorrect, allowing for targeted analysis of these errors.

Bonus One-Liner Method 5: Quick Prediction Check

This one-liner method offers a swift way to verify a single prediction using TensorFlow’s convenient functions to get a quick sense of the model’s prediction capabilities.

Here’s an example:

print("Predicted class:", tf.argmax(model.predict(test_images[0:1]), axis=1).numpy())

This outputs the prediction made by the model for the first test image.

This straightforward one-liner extracts the class prediction for a single example from the test set, giving an immediate prediction without additional context.

Summary/Discussion

  • Method 1: Visual Verification with Matplotlib. Allows for quick and intuitive visual assessments. Best for analyzing individual or small sets of images. Not scalable for large datasets.
  • Method 2: Confusion Matrix. Offers a detailed overview of how well the model performs across all classes. Useful to see if there’s a systemic confusion between certain classes. May not provide insights into individual errors.
  • Method 3: Classification Report. Gives detailed accuracy metrics for each class. Useful for understanding where the model might need improvement. Does not explain why errors occur.
  • Method 4: Sample-wise Error Analysis. Good for in-depth analysis of incorrect predictions. Helps identify trends in misclassifications. Can be time-consuming for large datasets.
  • Bonus One-Liner Method 5: Quick Prediction Check. Fastest way to get a single prediction. Lacks context and is not informative about overall model performance.