Model trainer: use less preferred activation function because tfjs can deserialize it

Dependencies

• [2] F5KOGCLK Expand model capacity
• [3] EB3DTD43 Model trainer: use ground altitude instead of crust mass distribution
• [4] 7ML3OFE7 Model trainer: initial train and visualize thread pair (total crust mass; todo: altitude instead)
• [5] 6W7MFV2F D20-based neural network architecture

Change contents

• replacement in trainmodel/src/model.py at line 7
  [3.26]→[3.26:376](∅→∅)
  − layer1 = keras.layers.Dense(20, activation=tf.math.asinh)(inputs)

  − layer2 = keras.layers.Dense(20, activation=tf.math.asinh)(layer1)

  − layer3 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer1,layer2]))

  − layer4 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer1,layer2]))
  [3.26]

  [2.0]
  + layer1 = keras.layers.Dense(20, activation=keras.activations.softplus)(inputs)

  + layer2 = keras.layers.Dense(20, activation=keras.activations.softplus)(layer1)

  + layer3 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer2]))

  + layer4 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer2]))
• replacement in trainmodel/src/model.py at line 14
  [2.349]→[3.725:1312](∅→∅),[3.725]→[3.725:1312](∅→∅)
  − layer8 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer3,layer5,layer7]))

  − layer9 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer4,layer6,layer7]))

  − layer10 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer7,layer8,layer9]))

  − layer11 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer1,layer3,layer8,layer10]))

  − layer12 = keras.layers.Dense(20, activation=tf.math.asinh)(keras.layers.concatenate([layer1,layer4,layer9,layer10,layer11]))
  [2.349]

  [3.1312]
  + layer8 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer3,layer5,layer7]))

  + layer9 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer4,layer6,layer7]))

  + layer10 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer7,layer8,layer9]))

  + layer11 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer3,layer8,layer10]))

  + layer12 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer4,layer9,layer10,layer11]))