tf-keras

import os import numpy as np import tf_keras as keras from tf_keras import layers # Set environment variable to ensure Keras 2 is used if TensorFlow >= 2.16 is also installed os.environ['TF_USE_LEGACY_KERAS'] = '1' # Load a dataset (MNIST handwritten digits) (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Preprocess the data x_train = x_train.reshape(-1, 28 * 28).astype('float32') / 255.0 x_test = x_test.reshape(-1, 28 * 28).astype('float32') / 255.0 # Define the model using the Sequential API model = keras.Sequential([ layers.Dense(128, activation='relu', input_shape=(784,)), layers.Dropout(0.2), layers.Dense(10, activation='softmax') ]) # Compile the model model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) # Train the model print("\nTraining the model...") history = model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.1) # Evaluate the model print("\nEvaluating the model...") loss, accuracy = model.evaluate(x_test, y_test) print(f"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}") # Make predictions predictions = model.predict(x_test[:5]) predicted_classes = np.argmax(predictions, axis=1) print(f"\nFirst 5 test samples predictions: {predicted_classes}") print(f"True labels: {y_test[:5]}")