mono-dense-keras 0.0.2

Monotonic Dense Layer implemented in Keras

Constrained Monotonic Neural Networks

This Python library implements Monotonic Dense Layer as described in Davor Runje, Sharath M. Shankaranarayana, “Constrained Monotonic Neural Networks”, https://https://arxiv.org/abs/2205.11775.

If you use this library, please cite:

    @misc{https://doi.org/10.48550/arxiv.2205.11775,
      doi = {10.48550/ARXIV.2205.11775},
      url = {https://arxiv.org/abs/2205.11775},
      author = {Runje, Davor and Shankaranarayana, Sharath M.},
      title = {Constrained Monotonic Neural Networks},
      publisher = {arXiv},
      year = {2022},
      copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
    }

Install

pip install mono_dense_keras

License

The full text of the license is available at:

https://github.com/airtai/mono-dense-keras/blob/main/LICENSE

You are free to: - Share — copy and redistribute the material in any medium or format

• Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms: - Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

• NonCommercial — You may not use the material for commercial purposes.

• ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

• No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

How to use

First, we’ll create a simple dataset for testing using numpy. Inputs values $x_1$, $x_2$ and $x_3$ will be sampled from the normal distribution, while the output value $y$ will be calculated according to the following formula before adding noise to it:

$y = x_1^3 + \sin\left(\frac{x_2}{2 \pi}\right) + e^{-x_3}$

import numpy as np

rng = np.random.default_rng(42)

def generate_data(no_samples: int, noise: float):
    x = rng.normal(size=(no_samples, 3))
    y = x[:, 0] ** 3
    y += np.sin(x[:, 1] / (2*np.pi))
    y += np.exp(-x[:, 2])
    y += noise * rng.normal(size=no_samples)
    return x, y

x_train, y_train = generate_data(10_000, noise=0.1)
x_val, y_val = generate_data(10_000, noise=0.)

First, we’ll build a simple feedforward neural network using Dense layer from Keras library.

import tensorflow as tf

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers.schedules import ExponentialDecay

# build a simple model with 3 hidden layer
model = Sequential()

model.add(Input(shape=(3,)))
model.add(Dense(128, activation="elu"))
model.add(Dense(128, activation="elu"))
model.add(Dense(1))

We’ll train the network using the Adam optimizer and the ExponentialDecay learning rate schedule:

def train_model(model, initial_learning_rate):
    # train the model
    lr_schedule = ExponentialDecay(
        initial_learning_rate=initial_learning_rate,
        decay_steps=10_000,
        decay_rate=0.9,
    )
    optimizer = Adam(learning_rate=lr_schedule)
    model.compile(optimizer="adam", loss="mse")

    model.fit(x=x_train, y=y_train, batch_size=32, validation_data=(x_val, y_val), epochs=10)
    
train_model(model, initial_learning_rate=.1)

Epoch 1/10
313/313 [==============================] - 1s 2ms/step - loss: 9.1098 - val_loss: 9.2552
Epoch 2/10
313/313 [==============================] - 1s 2ms/step - loss: 7.7995 - val_loss: 8.3143
Epoch 3/10
313/313 [==============================] - 1s 2ms/step - loss: 7.5270 - val_loss: 8.0499
Epoch 4/10
313/313 [==============================] - 1s 2ms/step - loss: 7.2095 - val_loss: 7.5935
Epoch 5/10
313/313 [==============================] - 1s 2ms/step - loss: 6.0665 - val_loss: 6.7911
Epoch 6/10
313/313 [==============================] - 1s 2ms/step - loss: 3.1178 - val_loss: 1.5964
Epoch 7/10
313/313 [==============================] - 1s 2ms/step - loss: 1.1686 - val_loss: 0.8541
Epoch 8/10
313/313 [==============================] - 1s 2ms/step - loss: 0.7370 - val_loss: 1.5969
Epoch 9/10
313/313 [==============================] - 1s 2ms/step - loss: 0.6011 - val_loss: 0.3739
Epoch 10/10
313/313 [==============================] - 1s 2ms/step - loss: 0.4458 - val_loss: 0.3114

Now, we’ll use the MonotonicDense layer instead of Dense layer. By default, the MonotonicDense layer assumes the output of the layer is monotonically increasing with all inputs. This assumtion is always true for all layers except possibly the first one. For the first layer, we use indicator_vector to specify which input parameters are monotonic and to specify are they increasingly or decreasingly monotonic: - set 1 for increasingly monotonic parameter, - set -1 for decreasingly monotonic parameter, and - set 0 otherwise.

In our case, the indicator_vector is [1, 0, -1] because $y$ is: - monotonically increasing w.r.t. $x_1$ $\left(\frac{\partial y}{x_1} = 3 {x_1}^2 \geq 0\right)$, and - monotonically decreasing w.r.t. $x_3$ $\left(\frac{\partial y}{x_3} = - e^{-x_2} \leq 0\right)$.

from airt.keras.layers import MonotonicDense


# build a simple model with 3 hidden layer, but this using MonotonicDense layer
mono_model = Sequential()

mono_model.add(Input(shape=(3,)))
indicator_vector = [1, 0, -1]
mono_model.add(MonotonicDense(128, activation="elu", indicator_vector=indicator_vector))
mono_model.add(MonotonicDense(128, activation="elu"))

mono_model.add(Dense(1))

train_model(model, initial_learning_rate=.001)

Epoch 1/10
313/313 [==============================] - 1s 2ms/step - loss: 0.3646 - val_loss: 0.2042
Epoch 2/10
313/313 [==============================] - 1s 2ms/step - loss: 0.2895 - val_loss: 0.1387
Epoch 3/10
313/313 [==============================] - 1s 2ms/step - loss: 0.2756 - val_loss: 0.1027
Epoch 4/10
313/313 [==============================] - 1s 2ms/step - loss: 0.2281 - val_loss: 0.0814
Epoch 5/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1816 - val_loss: 0.0634
Epoch 6/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1631 - val_loss: 0.1443
Epoch 7/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1455 - val_loss: 0.1299
Epoch 8/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1701 - val_loss: 0.0709
Epoch 9/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1250 - val_loss: 0.0644
Epoch 10/10
313/313 [==============================] - 1s 2ms/step - loss: 0.1426 - val_loss: 0.0405