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load function

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  1. load_bnn_model.py +100 -0
load_bnn_model.py ADDED
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+ import tensorflow as tf
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+ from tensorflow import keras
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+ from tensorflow.keras import layers
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+ import tensorflow_probability as tfp
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+
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+ def load_bnn_model():
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+ FEATURE_NAMES = [
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+ "fixed acidity",
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+ "volatile acidity",
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+ "citric acid",
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+ "residual sugar",
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+ "chlorides",
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+ "free sulfur dioxide",
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+ "total sulfur dioxide",
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+ "density",
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+ "pH",
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+ "sulphates",
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+ "alcohol",
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+ ]
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+
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+ hidden_units=[8,8]
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+ learning_rate = 0.001
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+ def create_model_inputs():
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+ inputs = {}
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+ for feature_name in FEATURE_NAMES:
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+ inputs[feature_name] = layers.Input(
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+ name=feature_name, shape=(1,), dtype=tf.float32
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+ )
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+ return inputs
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+
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+ # Define the prior weight distribution as Normal of mean=0 and stddev=1.
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+ # Note that, in this example, the we prior distribution is not trainable,
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+ # as we fix its parameters.
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+ def prior(kernel_size, bias_size, dtype=None):
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+ n = kernel_size + bias_size
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+ prior_model = keras.Sequential(
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+ [
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+ tfp.layers.DistributionLambda(
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+ lambda t: tfp.distributions.MultivariateNormalDiag(
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+ loc=tf.zeros(n), scale_diag=tf.ones(n)
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+ )
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+ )
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+ ]
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+ )
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+ return prior_model
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+
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+
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+ # Define variational posterior weight distribution as multivariate Gaussian.
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+ # Note that the learnable parameters for this distribution are the means,
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+ # variances, and covariances.
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+ def posterior(kernel_size, bias_size, dtype=None):
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+ n = kernel_size + bias_size
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+ posterior_model = keras.Sequential(
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+ [
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+ tfp.layers.VariableLayer(
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+ tfp.layers.MultivariateNormalTriL.params_size(n), dtype=dtype
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+ ),
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+ tfp.layers.MultivariateNormalTriL(n),
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+ ]
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+ )
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+ return posterior_model
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+
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+ def create_probablistic_bnn_model(train_size):
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+ inputs = create_model_inputs()
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+ features = keras.layers.concatenate(list(inputs.values()))
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+ features = layers.BatchNormalization()(features)
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+
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+ # Create hidden layers with weight uncertainty using the DenseVariational layer.
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+ for units in hidden_units:
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+ features = tfp.layers.DenseVariational(
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+ units=units,
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+ make_prior_fn=prior,
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+ make_posterior_fn=posterior,
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+ kl_weight=1 / train_size,
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+ activation="sigmoid",
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+ )(features)
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+
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+ # Create a probabilistic output (Normal distribution), and use the `Dense` layer
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+ # to produce the parameters of the distribution.
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+ # We set units=2 to learn both the mean and the variance of the Normal distribution.
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+ distribution_params = layers.Dense(units=2)(features)
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+ outputs = tfp.layers.IndependentNormal(1)(distribution_params)
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+
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+ model = keras.Model(inputs=inputs,
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+ outputs=outputs)
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+
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+ return model
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+
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+ def negative_loglikelihood(targets, estimated_distribution):
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+ estimated_distirbution = tfp.distributions.MultivariateNormalTriL(estimated_distribution)
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+ return -estimated_distribution.log_prob(targets)
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+
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+ model = create_probablistic_bnn_model(4163)
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+ model.compile(
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+ optimizer=keras.optimizers.RMSprop(learning_rate=learning_rate),
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+ loss=negative_loglikelihood,
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+ metrics=[keras.metrics.RootMeanSquaredError()],
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+ )
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+ model.load_weights('bnn_wine_model.h5')
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+ return model