import tensorflow as tf import matplotlib.pyplot as plt # Load example dataset mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() # Preprocess data x_train, x_test = x_train / 255.0, x_test / 255.0 # Use only 3 classes for simplicity train_filter = y_train < 3 test_filter = y_test < 3 x_train, y_train = x_train[train_filter], y_train[train_filter] x_test, y_test = x_test[test_filter], y_test[test_filter] # Build simple model model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dense(3, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train model and save history history = model.fit(x_train, y_train, epochs=20, validation_data=(x_test, y_test)) # Plot training & validation accuracy values plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) plt.plot(history.history['accuracy'], label='Train Accuracy') plt.plot(history.history['val_accuracy'], label='Validation Accuracy') plt.title('Model Accuracy') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.legend() # Plot training & validation loss values plt.subplot(1, 2, 2) plt.plot(history.history['loss'], label='Train Loss') plt.plot(history.history['val_loss'], label='Validation Loss') plt.title('Model Loss') plt.xlabel('Epoch') plt.ylabel('Loss') plt.legend() plt.tight_layout() plt.show()
Before visualization: You only had numbers showing training and validation accuracy and loss.
After visualization: You see clear graphs showing training accuracy steadily increasing and validation accuracy plateauing or fluctuating, indicating overfitting. Loss graphs show training loss decreasing smoothly while validation loss decreases less or increases.