ProbabalisticBayesianModel-Wine / load_bnn_model.py

load function

c2f467a almost 2 years ago

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	import tensorflow as tf
	from tensorflow import keras
	from tensorflow.keras import layers
	import tensorflow_probability as tfp

	def load_bnn_model():
	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

	# Define the prior weight distribution as Normal of mean=0 and stddev=1.
	# Note that, in this example, the we prior distribution is not trainable,
	# as we fix its parameters.
	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


	# Define variational posterior weight distribution as multivariate Gaussian.
	# Note that the learnable parameters for this distribution are the means,
	# variances, and covariances.
	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_probablistic_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)

	# Create a probabilistic output (Normal distribution), and use the `Dense` layer
	# to produce the parameters of the distribution.
	# We set units=2 to learn both the mean and the variance of the Normal distribution.
	distribution_params = layers.Dense(units=2)(features)
	outputs = tfp.layers.IndependentNormal(1)(distribution_params)

	model = keras.Model(inputs=inputs,
	outputs=outputs)

	return model

	def negative_loglikelihood(targets, estimated_distribution):
	estimated_distirbution = tfp.distributions.MultivariateNormalTriL(estimated_distribution)
	return -estimated_distribution.log_prob(targets)

	model = create_probablistic_bnn_model(4163)
	model.compile(
	optimizer=keras.optimizers.RMSprop(learning_rate=learning_rate),
	loss=negative_loglikelihood,
	metrics=[keras.metrics.RootMeanSquaredError()],
	)
	model.load_weights('bnn_wine_model.h5')
	return model