![]() reshape ( mu, ) # probabilities = keras. tile ( mu, ( 1, 1, 2 )) # level_decisions = decisions # mu = mu * level_decisions # begin_idx = end_idx end_idx = begin_idx + 2 ** ( level + 1 ) mu = tf. ones () begin_idx = 1 end_idx = 2 # Traverse the tree in breadth-first order. decision_fn ( features ), axis = 2 ) # Concatenate the routing probabilities with their complements. used_features_mask, transpose_b = True ) # Compute the routing probabilities. shape ( features ) # Apply the feature mask to the input features. num_leaves, activation = "sigmoid", name = "decision" ) def call ( self, features ): batch_size = tf. random_normal_initializer ()( shape = ), dtype = "float32", trainable = True, ) # Initialize the stochastic routing layer. used_features_mask = one_hot # Initialize the weights of the classes in leaves. arange ( num_features ), num_used_features, replace = False ) self. eye ( num_features ) sampled_feature_indicies = np. num_used_features = int ( num_features * used_features_rate ) one_hot = np. num_classes = num_classes # Create a mask for the randomly selected features. Model ): def _init_ ( self, depth, num_features, used_features_rate, num_classes ): super (). Finally, the probabilities of reaching the leaves are combined by the class probabilities at theĬlass NeuralDecisionTree ( keras.Then, the model computes the probabilities ( mu) for the input instances to reach the tree leavesīy iteratively performing a stochastic routing throughout the tree levels.The model first applies a used_features_mask to randomly select a subset of input features to use.Or dense transformations applied to structured data features. This vector can be generated from a Convolution Neural Network (CNN) applied to images The model expects input features as a single vector encoding all the features of an instance.The forward pass of the model works as follows: The second set is the weights of the routing layer decision_fn, which represents the probability Which represents the probability distribution of the classes in the tree leaves. concatenate ( encoded_features ) return encoded_featuresĪ neural decision tree model has two sets of weights to learn. append ( encoded_feature ) encoded_features = layers. expand_dims ( encoded_feature, - 1 ) encoded_features. encoded_feature = embedding ( value_index ) else : # Use the numerical features as-is. vocabulary_size (), output_dim = embedding_dims ) # Convert the index values to embedding representations. vocabulary_size ())) # Create an embedding layer with the specified dimensions. value_index = lookup ( inputs ) embedding_dims = int ( math. lookup = StringLookup ( vocabulary = vocabulary, mask_token = None, num_oov_indices = 0 ) # Convert the string input values into integer indices. # Since we are not using a mask token, nor expecting any out of vocabulary # (oov) token, we set mask_token to None and num_oov_indices to 0. We also preprocess the input by mapping the target labelĭef encode_inputs ( inputs ): encoded_features = for feature_name in inputs : if feature_name in CATEGORICAL_FEATURE_NAMES : vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY # Create a lookup to convert a string values to an integer indices. We create an input function to read and parse the file, and convert features and labelsįor training and validation. TARGET_LABELS = Ĭreate tf.data.Dataset objects for training and validation TARGET_FEATURE_NAME = "income_bracket" # A list of the labels of the target features. COLUMN_DEFAULTS = if feature_name in NUMERIC_FEATURE_NAMES + IGNORE_COLUMN_NAMES else for feature_name in CSV_HEADER ] # The name of the target feature. FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES # A list of column default values for each feature. keys ()) # A list of all the input features. CATEGORICAL_FEATURE_NAMES = list ( CATEGORICAL_FEATURES_WITH_VOCABULARY. IGNORE_COLUMN_NAMES = # A list of the categorical feature names. CATEGORICAL_FEATURES_WITH_VOCABULARY = # A list of the columns to ignore from the dataset. NUMERIC_FEATURE_NAMES = # A dictionary of the categorical features and their vocabulary.
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