tf.keras.models.Sequential([tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(n, n, 3)),;tf.keras.layers.MaxPooling2D((2, 2)),; tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),;  tf.keras.layers.MaxPooling2D((2, 2)),;tf.keras.layers.Flatten(),; tf.keras.layers.Dense(128, activation='relu'),;tf.keras.layers.Dense(n * n * 3, activation='sigmoid' ]);model.fit; (1,n,n,3); numpy.array; ValueError: Dimensions must be equal, but are 3072 and 3 for 'node mean_squared_error/SquaredDifference = SquaredDifference[T=DT_FLOAT](sequential/dense_1/Sigmoid,