tf2-bfgs 0.1.0

Wrapper to train a tf.Module or tf.keras.Model using (L)BFGS optimizer from Tensorflow Probability

tf2-bfgs

Use BFGS optimizer in Tensorflow 2 almost as it was with Tensorflow 1 and tf.contrib

tf2-bfgs is a wrapper to train a tf.Module or tf.keras.Model using (L-)BFGS optimizer from Tensorflow Probability. This work uses the code snippet from Pi-Yueh Chuang available here.

Install

You can run the following command in a dedicated virtual environment:

pip install tf2-bfgs

or the following one to get the latest version

pip install git+https://github.com/mBarreau/tf2-bfgs

You might also need to install extra dependencies such as

pip install tensorflow-probabilities tf_keras

Examples

We will train a regression model to fit a cos. Here are the training data.

import tensorflow as tf
import numpy as np
from tf2_bfgs import LBFGS

t = np.linspace(0, 1, 10).reshape((-1, 1)).astype(np.float32)
x = np.cos(t)

Using Keras

We define the model first, a multilayer perceptron.

omega = tf.keras.Sequential(
    [tf.keras.Input(shape=[1,]),
     tf.keras.layers.Dense(10, "tanh"),
     tf.keras.layers.Dense(10, "tanh"),
     tf.keras.layers.Dense(10, "tanh"),
     tf.keras.layers.Dense(1, None)])

The loss function is the traditional mean squared error:

def get_cost(model, t, x):
    x_hat = model(t)
    return tf.keras.losses.MeanSquaredError()(x_hat, x)

NOTE: for differentiablility reasons, the output of the model x_hat = model(t) should be computed inside the cost function.

optimizer_BFGS = LBFGS(get_cost, omega.trainable_variables)
results = optimizer_BFGS.minimize(omega, t, x)

The number of parameters to the minimize function is the same as those of get_cost.

Using Tensorflow tf.Module

We will define our own multilayer perceptron as follows:

def init(layers):
    Ws, bs = [], []
    for i in range(len(layers) - 1):
        W = xavier_init(size=[layers[i], layers[i + 1]])
        b = tf.zeros([1, layers[i + 1]])
        Ws.append(tf.Variable(W, dtype=tf.float32, name=f"W_{i}"))
        bs.append(tf.Variable(b, dtype=tf.float32, name=f"b_{i}"))
    return Ws, bs


def xavier_init(size):
    in_dim = size[0]
    out_dim = size[1]
    xavier_stddev = np.sqrt(2 / (in_dim + out_dim))
    return np.random.normal(size=[in_dim, out_dim], scale=xavier_stddev)


class NeuralNetwork(tf.Module):
    def __init__(self, hidden_layers, **kwargs):
        super().__init__(**kwargs)
        self.layers = [1] + hidden_layers + [1]
        self.Ws, self.bs = init(layers=self.layers)

    @tf.function
    def __call__(self, input):
        num_layers = len(self.Ws)
        H = tf.cast(input, tf.float32)
        for layer in range(0, num_layers - 1):
            W = self.Ws[layer]
            b = self.bs[layer]
            H = tf.tanh(tf.add(tf.matmul(H, W), b))
        W = self.Ws[-1]
        b = self.bs[-1]
        return tf.add(tf.matmul(H, W), b)

omega = NeuralNetwork([10]*3)

NOTE: It is of main importance that the class inherits from tf.Module.

Define the cost function as follows:

@tf.function
def get_cost(model, t, x):
    return tf.reduce_mean(tf.square(model(t) - x))

NOTE: for differentiablility reasons, the output of the model x_hat = model(t) should be computed inside the cost function. Note that it is also recommended to use the decorator @tf.function for performance reasons.

The training is perfomed by the following snippet:

optimizer_BFGS = LBFGS(get_cost, omega.trainable_variables)
results = optimizer_BFGS.minimize(omega, t, x)

The number of parameters to the minimize function is the same as those of get_cost.

Using physics-informed neural networks

This optimizer might be useful when dealing with physics-informed neural network. To this end, we must change the cost from the previous subsection to include the minimization of the physics residual.

@tf.function
def get_pinn_cost(model, t, x, t_phys):
    # Data cost
    data_cost = tf.reduce_mean(tf.square(model(t) - x))
    # Physics cost
    t_phys = tf.convert_to_tensor(t)
    with tf.GradientTape(watch_accessed_variables=False) as tape:
        tape.watch(t)
        model_tf = model(t)
    dmodel_dt = tape.gradient(model_tf, t_phys)
    physics_cost = tf.reduce_mean(tf.square(dmodel_dt + tf.sin(t)))
    return data_cost + 0.1 * physics_cost

Training is done in a similar manner.

t_phys = np.linspace(0, 1, 100).reshape((-1, 1)).astype(np.float32)
optimizer_BFGS = LBFGS(get_pinn_cost, omega.trainable_variables)
results = optimizer_BFGS.minimize(omega, t, x, t_phys)

Options

The optimizer has the same options as the function tfp.optimizer.bfgs_minimize in the case of BFGS or tfp.optimizer.lbfgs_minimize in the case of LBFGS.

For instance, to set the maximum number of iterations to 100, we can use the following code when defining the optimizer:

options = {'max_iterations': 100}
optimizer_BFGS = LBFGS(get_cost, omega.trainable_variables, options)