import os

                              activation=keras.activations.softplus)(inputs)

                              activation=tf.math.asinh)(inputs)

                              activation=keras.activations.softplus)(layer1)
  layer3 = keras.layers.Dense(20, activation=keras.activations.softplus)(

                              activation=tf.math.asinh)(layer1)
  layer3 = keras.layers.Dense(20, activation=tf.math.asinh)(

  layer4 = keras.layers.Dense(20, activation=keras.activations.softplus)(

  layer4 = keras.layers.Dense(20, activation=tf.math.asinh)(

  layer8 = keras.layers.Dense(20, activation=keras.activations.softplus)(

  layer8 = keras.layers.Dense(20, activation=tf.math.asinh)(

  layer9 = keras.layers.Dense(20, activation=keras.activations.softplus)(

  layer9 = keras.layers.Dense(20, activation=tf.math.asinh)(

  layer10 = keras.layers.Dense(20, activation=keras.activations.softplus)(

  layer10 = keras.layers.Dense(20, activation=tf.math.asinh)(

  layer11 = keras.layers.Dense(20, activation=keras.activations.softplus)(

  layer11 = keras.layers.Dense(20, activation=tf.math.asinh)(

  layer12 = keras.layers.Dense(20, activation=keras.activations.softplus)(

  layer12 = keras.layers.Dense(20, activation=tf.math.asinh)(

def model():

def model(chans = 1):

  outputs = keras.layers.Dense(1, activation=keras.activations.sigmoid)(
      keras.layers.concatenate([d1, d2]))

  m = keras.layers.Dense(20, activation=tf.math.asinh)(
          keras.layers.concatenate([d1,d2]))
  outputs = keras.layers.Dense(chans, activation=keras.activations.sigmoid)(m)

        self.heightmap = model()
        self.heightmap.compile(optimizer=keras.optimizers.RMSprop(),
            loss=shore_focused_loss)

        self.members = {}
    def heightmap(self):
        if 'heightmap' not in self.members:
            self.members['heightmap'] = model()
            self.members['heightmap'].compile(
                    optimizer=keras.optimizers.RMSprop(),
                    loss=shore_focused_loss)
        return self.members['heightmap']
    def save(self, path):
        path = os.fspath(path)
        try:
            os.makedirs(path)
        except FileExistsError:
            None
        for name, model in self.members.items():
            model.save(f'{path}{os.sep}{name}')

      outputs = m.heightmap(inputs)

      outputs = m.heightmap()(inputs)

import tensorflowjs as tfjs

  m.heightmap.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)
  tfjs.converters.save_keras_model(m.heightmap, 'tfjs_model')

  m.heightmap().fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)
  m.save('models')

  outputs = tf.reshape(m.heightmap(inputs), (1024, 2048, 1))

  outputs = tf.reshape(m.heightmap()(inputs), (1024, 2048, 1))

	env PATH=$(PWD)/venv/bin:$(PATH) pip install tensorflowjs