BNNexample.py

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds
import tensorflow_probability as tfp



FEATURE_NAMES = [
    "fixed acidity",
    "volatile acidity",
    "citric acid",
    "residual sugar",
    "chlorides",
    "free sulfur dioxide",
    "total sulfur dioxide",
    "density",
    "pH",
    "sulphates",
    "alcohol",
]

hidden_units = [8, 8]
learning_rate = 0.001


def create_model_inputs():
    inputs = {}
    for feature_name in FEATURE_NAMES:
        inputs[feature_name] = layers.Input(
            name=feature_name, shape=(1,), dtype=tf.float32
        )
    return inputs

def get_train_and_test_splits(train_size, batch_size=1):
    # We prefetch with a buffer the same size as the dataset because th dataset
    # is very small and fits into memory.
    dataset = (
        tfds.load(name="wine_quality", as_supervised=True, split="train")
        .map(lambda x, y: (x, tf.cast(y, tf.float32)))
        .prefetch(buffer_size=dataset_size)
        .cache()
    )
    # We shuffle with a buffer the same size as the dataset.
    train_dataset = (
        dataset.take(train_size).shuffle(buffer_size=train_size).batch(batch_size)
    )
    test_dataset = dataset.skip(train_size).batch(batch_size)

    return train_dataset, test_dataset

def run_experiment(model, loss, train_dataset, test_dataset):

    model.compile(
        optimizer=keras.optimizers.RMSprop(learning_rate=learning_rate),
        loss=loss,
        metrics=[keras.metrics.RootMeanSquaredError()],
    )

    print("Start training the model...")
    model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset)
    print("Model training finished.")
    _, rmse = model.evaluate(train_dataset, verbose=0)
    print(f"Train RMSE: {round(rmse, 3)}")

    print("Evaluating model performance...")
    _, rmse = model.evaluate(test_dataset, verbose=0)
    print(f"Test RMSE: {round(rmse, 3)}")







dataset_size = 4898
batch_size = 256
num_epochs = 100
sample = 10
train_size = int(dataset_size * 0.85)

mse_loss = keras.losses.MeanSquaredError()

# Split the datasets into two groups
train_dataset, test_dataset = get_train_and_test_splits(train_size, batch_size)

# Sample some example targets from the test dataset
examples, targets = list(test_dataset.unbatch().shuffle(batch_size * 10).batch(sample))[
    0
]
HI=2


def prior(kernel_size, bias_size, dtype=None):
    n = kernel_size + bias_size
    prior_model = keras.Sequential(
        [
            tfp.layers.DistributionLambda(
                lambda t: tfp.distributions.MultivariateNormalDiag(
                    loc=tf.zeros(n), scale_diag=tf.ones(n)
                )
            )
        ]
    )
    return prior_model

def posterior(kernel_size, bias_size, dtype=None):
    n = kernel_size + bias_size
    posterior_model = keras.Sequential(
        [
            tfp.layers.VariableLayer(
                tfp.layers.MultivariateNormalTriL.params_size(n), dtype=dtype
            ),
            tfp.layers.MultivariateNormalTriL(n),
        ]
    )
    return posterior_model





def create_bnn_model(train_size):
    inputs = create_model_inputs()
    features = keras.layers.concatenate(list(inputs.values()))
    features = layers.BatchNormalization()(features)

    # Create hidden layers with weight uncertainty using the DenseVariational layer.
    for units in hidden_units:
        features = tfp.layers.DenseVariational(
            units=units,
            make_prior_fn=prior,
            make_posterior_fn=posterior,
            kl_weight=1 / train_size,
            activation="sigmoid",
        )(features)

    # The output is deterministic: a single point estimate.
    outputs = layers.Dense(units=1)(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model

def compute_predictions(model, iterations=100):
    predicted = []
    for _ in range(iterations):
        predicted.append(model(examples).numpy())
    predicted = np.concatenate(predicted, axis=1)

    prediction_mean = np.mean(predicted, axis=1).tolist()
    prediction_min = np.min(predicted, axis=1).tolist()
    prediction_max = np.max(predicted, axis=1).tolist()
    prediction_range = (np.max(predicted, axis=1) - np.min(predicted, axis=1)).tolist()

    for idx in range(sample):
        print(
            f"Predictions mean: {round(prediction_mean[idx], 2)}, "
            f"min: {round(prediction_min[idx], 2)}, "
            f"max: {round(prediction_max[idx], 2)}, "
            f"range: {round(prediction_range[idx], 2)} - "
            f"Actual: {targets[idx]}"
        )



num_epochs = 3
bnn_model_full = create_bnn_model(train_size)
run_experiment(bnn_model_full, mse_loss, train_dataset, test_dataset)

compute_predictions(bnn_model_full)