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import tensorflow as tf # Load MNIST dataset (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() # Normalize images to [0,1] test_images = test_images.astype('float32') / 255.0 # Expand dims to add channel dimension # Model expects shape (batch, 28, 28, 1) test_images = test_images[..., tf.newaxis] # Define the same model architecture used for training model = tf.keras.Sequential([ tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28,28,1)), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(100, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) # Load pretrained weights (simulate training by loading weights from a file or assume weights are loaded here) # For this experiment, we will compile and load weights from a saved model if available # Here, we simulate by compiling and assuming weights are loaded model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Normally, you would load weights like: # model.load_weights('path_to_weights') # For demonstration, we train briefly to simulate trained model train_images = train_images.astype('float32') / 255.0 train_images = train_images[..., tf.newaxis] model.fit(train_images, train_labels, epochs=1, batch_size=64, verbose=0) # Predict on test data predictions = model.predict(test_images) # Convert predictions to label indices predicted_labels = predictions.argmax(axis=1) # Evaluate model on test data loss, accuracy = model.evaluate(test_images, test_labels, verbose=0) print(f"Test Loss: {loss:.4f}") print(f"Test Accuracy: {accuracy*100:.2f}%")
Before: Only training accuracy (98%) and loss (0.05) were known.
After: Test accuracy is about 95% and test loss about 0.15, showing the model generalizes well but slightly worse than training.
model.predict() function do in TensorFlow?model.predict()model.fit(), saving uses model.save(), and deleting is manual memory management, none of which are predict().X_test and y_test?model.evaluate() to measure performance on test data.model.predict() makes predictions, model.fit() trains, and model.score() is not a TensorFlow method.import tensorflow as tf import numpy as np model = tf.keras.Sequential([ tf.keras.layers.Dense(1, input_shape=(1,)) ]) model.compile(optimizer='sgd', loss='mse') X = np.array([1, 2, 3, 4], dtype=float) y = np.array([2, 4, 6, 8], dtype=float) model.fit(X, y, epochs=10, verbose=0) predictions = model.predict(np.array([5.0])) print(predictions)
model.evaluate(X_test, y_test) but get a ValueError about mismatched shapes. What is the most likely cause?X_test1, y_test1 and X_test2, y_test2. Which approach correctly compares their accuracy using TensorFlow?model.evaluate() returns loss and metrics on test data without training, ideal for comparing performance.