keras-nightly

import os import keras import numpy as np # Configure the Keras backend (e.g., 'tensorflow', 'jax', 'torch') os.environ["KERAS_BACKEND"] = "tensorflow" # Load a dataset (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() x_train = x_train.astype("float32") / 255 x_test = x_test.astype("float32") / 255 x_train = np.expand_dims(x_train, -1) x_test = np.expand_dims(x_test, -1) # Build a simple model model = keras.Sequential( [ keras.layers.Input(shape=(28, 28, 1)), keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), keras.layers.MaxPooling2D(pool_size=(2, 2)), keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), keras.layers.MaxPooling2D(pool_size=(2, 2)), keras.layers.Flatten(), keras.layers.Dropout(0.5), keras.layers.Dense(10, activation="softmax"), ] ) # Compile the model model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) # Train the model model.fit(x_train, y_train, batch_size=128, epochs=5, validation_split=0.1) # Evaluate the model loss, accuracy = model.evaluate(x_test, y_test) print(f"Test accuracy: {accuracy:.4f}")