keras.layers.Conv2D(256, input_shape=(1024, 1024, 3), kernel_size=(3, 3), strides=2, activation="relu"),
keras.layers.Conv2D(256, kernel_size=(3, 3), strides=2, activation="relu"),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(128, kernel_size=(3, 3), strides=1, activation="relu"),
keras.layers.Conv2D(128, kernel_size=(3, 3), strides=1, activation="relu"),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(128, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.Conv2D(128, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(64, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.Conv2D(64, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(32, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.Conv2D(32, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(30, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.Conv2D(30, kernel_size=(3, 3), strides=1, activation='relu'),
keras.layers.Conv2D(30, kernel_size=(3, 3), strides=1)
По сути значения все равно будут (количество точек(70), координата x, координата y)