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predictions = model.predict(X_test) model.fit(X_train, y_train, epochs=10, batch_size=32) loss, accuracy = model.evaluate(X_test, y_test) print(f'Loss: {loss}, Accuracy: {accuracy}') model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model = keras.Sequential([ layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)), layers.Dense(64, activation='relu'), layers.Dense(1, activation='sigmoid') # Cambia según la naturaleza de tu tarea ]) # Suponiendo que tienes una columna 'target' y el resto son características X = data.drop('target', axis=1) y = data['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) data = pd.read_csv('ruta/a/tu/dataset.csv') 01010001001001101010