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tfdf.keras.GradientBoostedTreesModel

Gradient Boosted Trees learning algorithm.

Inherits From: GradientBoostedTreesModel, CoreModel

Used in the notebooks

Used in the guide Used in the tutorials

A GBT (Gradient Boosted [Decision] Tree; https://statweb.stanford.edu/~jhf/ftp/trebst.pdf) is a set of shallow decision trees trained sequentially. Each tree is trained to predict and then "correct" for the errors of the previously trained trees (more precisely each tree predict the gradient of the loss relative to the model output).

Usage example:

import tensorflow_decision_forests as tfdf
import pandas as pd

dataset = pd.read_csv("project/dataset.csv")
tf_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(dataset, label="my_label")

model = tfdf.keras.GradientBoostedTreesModel()
model.fit(tf_dataset)

print(model.summary())

task Task to solve (e.g. Task.CLASSIFICATION, Task.REGRESSION, Task.RANKING).
features Specify the list and semantic of the input features of the model. If not specified, all the available features will be used. If specified and if exclude_non_specified_features=True, only the features in features will be used by the model. If "preprocessing" is used, features corresponds to the output of the preprocessing. In this case, it is recommended for the preprocessing to return a dictionary of tensors.
exclude_non_specified_features If true, only use the features specified in features.
preprocessing Functional keras model or @tf.function to apply on the input feature before the model to train. This preprocessing model can consume and return tensors, list of tensors or dictionary of tensors. If specified, the model only "sees" the output of the preprocessing (and not the raw input). Can be used to prepare the features or to stack multiple models on top of each other. Unlike preprocessing done in the tf.dataset, the operation in "preprocessing" are serialized with the model.
postprocessing Like "preprocessing" but applied on the model output.
ranking_group Only for task=Task.RANKING. Name of a tf.string feature that identifies queries in a query/document ranking task. The ranking group is not added automatically for the set of features if exclude_non_specified_features=false.
temp_directory Temporary directory used to store the model Assets after the training, and possibly as a work directory during the training. This temporary directory is necessary for the model to be exported after training e.g. model.save(path). If not specified, temp_directory is set to a temporary directory using tempfile.TemporaryDirectory. This directory is deleted when the model python object is garbage-collected.
verbose If true, displays information about the training.
hyperparameter_template Override the default value of the hyper-parameters. If None (default) the default parameters of the library are used. If set, default_hyperparameter_template refers to one of the following preconfigured hyper-parameter sets. Those sets outperforms the default hyper-parameters (either generally or in specific scenarios). You can omit the version (e.g. remove "@v5") to use the last version of the template. In this case, the hyper-parameter can change in between releases (not recommended for training in production).

  • better_default@v1: A configuration that is generally better than the default parameters without being more expensive. The parameters are: growing_strategy="BEST_FIRST_GLOBAL".
  • benchmark_rank1@v1: Top ranking hyper-parameters on our benchmark slightly modified to run in reasonable time. The parameters are: growing_strategy="BEST_FIRST_GLOBAL", categorical_algorithm="RANDOM", split_axis="SPARSE_OBLIQUE", sparse_oblique_normalization="MIN_MAX", sparse_oblique_num_projections_exponent=1.0.
advanced_arguments Advanced control of the model that most users won't need to use. See AdvancedArguments for details.
num_threads Number of threads used to train the model. Different learning algorithms use multi-threading differently and with different degree of efficiency. If specified, num_threads field of the advanced_arguments.yggdrasil_deployment_config has priority.
name The name of the model.
adapt_subsample_for_maximum_training_duration Control how the maximum training duration (if set) is applied. If false, the training stop when the time is used. If true, the size of the sampled datasets used train individual trees are adapted dynamically so that all the trees are trained in time. Default: False.
allow_na_conditions If true, the tree training evaluates conditions of the type X is NA i.e. X is missing. Default: False.
apply_link_function If true, applies the link function (a.k.a. activation function), if any, before returning the model prediction. If false, returns the pre-link function model output. For example, in the case of binary classification, the pre-link function output is a logic while the post-link function is a probability. Default: True.
categorical_algorithm How to learn splits on categorical attributes.
  • CART: CART algorithm. Find categorical splits of the form "value \in mask". The solution is exact for binary classification, regression and ranking. It is approximated for multi-class classification. This is a good first algorithm to use. In case of overfitting (very small dataset, large dictionary), the "random" algorithm is a good alternative.
  • ONE_HOT: One-hot encoding. Find the optimal categorical split of the form "attribute == param". This method is similar (but more efficient) than converting converting each possible categorical value into a boolean feature. This method is available for comparison purpose and generally performs worse than other alternatives.
  • RANDOM: Best splits among a set of random candidate. Find the a categorical split of the form "value \in mask" using a random search. This solution can be seen as an approximation of the CART algorithm. This method is a strong alternative to CART. This algorithm is inspired from section "5.1 Categorical Variables" of "Random Forest", 2001. Default: "CART".
  • categorical_set_split_greedy_sampling For categorical set splits e.g. texts. Probability for a categorical value to be a candidate for the positive set. The sampling is applied once per node (i.e. not at every step of the greedy optimization). Default: 0.1.
    categorical_set_split_max_num_items For categorical set splits e.g. texts. Maximum number of items (prior to the sampling). If more items are available, the least frequent items are ignored. Changing this value is similar to change the "max_vocab_count" before loading the dataset, with the following exception: With max_vocab_count, all the remaining items are grouped in a special Out-of-vocabulary item. With max_num_items, this is not the case. Default: -1.
    categorical_set_split_min_item_frequency For categorical set splits e.g. texts. Minimum number of occurrences of an item to be considered. Default: 1.
    dart_dropout Dropout rate applied when using the DART i.e. when forest_extraction=DART. Default: 0.01.
    early_stopping Early stopping detects the overfitting of the model and halts it training using the validation dataset controlled by validation_ratio.
  • NONE: No early stopping. The model is trained entirely.
  • MIN_LOSS_FINAL: No early stopping. However, the model is then truncated to maximize the validation loss.
  • LOSS_INCREASE: Stop the training when the validation does not decrease for early_stopping_num_trees_look_ahead trees. Default: "LOSS_INCREASE".
  • early_stopping_num_trees_look_ahead Rolling number of trees used to detect validation loss increase and trigger early stopping. Default: 30.
    forest_extraction How to construct the forest:
  • MART: For Multiple Additive Regression Trees. The "classical" way to build a GBDT i.e. each tree tries to "correct" the mistakes of the previous trees.
  • DART: For Dropout Additive Regression Trees. A modification of MART proposed in http://proceedings.mlr.press/v38/korlakaivinayak15.pdf. Here, each tree tries to "correct" the mistakes of a random subset of the previous trees. Default: "MART".
  • goss_alpha Alpha parameter for the GOSS (Gradient-based One-Side Sampling; "See LightGBM: A Highly Efficient Gradient Boosting Decision Tree") sampling method. Default: 0.2.
    goss_beta Beta parameter for the GOSS (Gradient-based One-Side Sampling) sampling method. Default: 0.1.
    growing_strategy How to grow the tree.
  • LOCAL: Each node is split independently of the other nodes. In other words, as long as a node satisfy the splits "constraints (e.g. maximum depth, minimum number of observations), the node will be split. This is the "classical" way to grow decision trees.
  • BEST_FIRST_GLOBAL: The node with the best loss reduction among all the nodes of the tree is selected for splitting. This method is also called "best first" or "leaf-wise growth". See "Best-first decision tree learning", Shi and "Additive logistic regression : A statistical view of boosting", Friedman for more details. Default: "LOCAL".
  • in_split_min_examples_check Whether to check the min_examples constraint in the split search (i.e. splits leading to one child having less than min_examples examples are considered invalid) or before the split search (i.e. a node can be derived only if it contains more than min_examples examples). If false, there can be nodes with less than min_examples training examples. Default: True.
    l1_regularization L1 regularization applied to the training loss. Impact the tree structures and lead values. Default: 0.0.
    l2_categorical_regularization L2 regularization applied to the training loss for categorical features. Impact the tree structures and lead values. Default: 1.0.
    l2_regularization L2 regularization applied to the training loss for all features except the categorical ones. Default: 0.0.
    lambda_loss Lambda regularization applied to certain training loss functions. Only for NDCG loss. Default: 1.0.
    max_depth Maximum depth of the tree. max_depth=1 means that all trees will be roots. Negative values are ignored. Default: 6.
    max_num_nodes Maximum number of nodes in the tree. Set to -1 to disable this limit. Only available for growing_strategy=BEST_FIRST_GLOBAL. Default: None.
    maximum_training_duration_seconds Maximum training duration of the model expressed in seconds. Each learning algorithm is free to use this parameter at it sees fit. Enabling maximum training duration makes the model training non-deterministic. Default: -1.0.
    min_examples Minimum number of examples in a node. Default: 5.
    missing_value_policy Method used to handle missing attribute values.
  • GLOBAL_IMPUTATION: Missing attribute values are imputed, with the mean (in case of numerical attribute) or the most-frequent-item (in case of categorical attribute) computed on the entire dataset (i.e. the information contained in the data spec).
  • LOCAL_IMPUTATION: Missing attribute values are imputed with the mean (numerical attribute) or most-frequent-item (in the case of categorical attribute) evaluated on the training examples in the current node.
  • RANDOM_LOCAL_IMPUTATION: Missing attribute values are imputed from randomly sampled values from the training examples in the current node. This method was proposed by Clinic et al. in "Random Survival Forests" (https://projecteuclid.org/download/pdfview_1/euclid.aoas/1223908043). Default: "GLOBAL_IMPUTATION".
  • num_candidate_attributes Number of unique valid attributes tested for each node. An attribute is valid if it has at least a valid split. If num_candidate_attributes=0, the value is set to the classical default value for Random Forest: sqrt(number of input attributes) in case of classification and number_of_input_attributes / 3 in case of regression. If num_candidate_attributes=-1, all the attributes are tested. Default: -1.
    num_candidate_attributes_ratio Ratio of attributes tested at each node. If set, it is equivalent to num_candidate_attributes = number_of_input_features x num_candidate_attributes_ratio. The possible values are between ]0, and 1] as well as -1. If not set or equal to -1, the num_candidate_attributes is used. Default: -1.0.
    num_trees Maximum number of decision trees. The effective number of trained tree can be smaller if early stopping is enabled. Default: 300.
    sampling_method Control the sampling of the datasets used to train individual trees.
  • NONE: No sampling is applied.
  • RANDOM: Uniform random sampling. Automatically selected if "subsample" is set.
  • GOSS: Gradient-based One-Side Sampling. Automatically selected if "goss_alpha" or "goss_beta" is set.
  • SELGB: Selective Gradient Boosting. Automatically selected if "selective_gradient_boosting_ratio" is set. Default: "NONE".
  • selective_gradient_boosting_ratio Ratio of the dataset used to train individual tree for the selective Gradient Boosting (Selective Gradient Boosting for Effective Learning to Rank; Lucchese et al; http://quickrank.isti.cnr.it/selective-data/selective-SIGIR2018.pdf) sampling method. Default: 0.01.
    shrinkage Coefficient applied to each tree prediction. A small value (0.02) tends to give more accurate results (assuming enough trees are trained), but results in larger models. Analogous to neural network learning rate. Default: 0.1.
    sorting_strategy How are sorted the numerical features in order to find the splits
  • PRESORT: The features are pre-sorted at the start of the training. This solution is faster but consumes much more memory than IN_NODE.
  • IN_NODE: The features are sorted just before being used in the node. This solution is slow but consumes little amount of memory. . Default: "PRESORT".
  • sparse_oblique_normalization For sparse oblique splits i.e. split_axis=SPARSE_OBLIQUE. Normalization applied on the features, before applying the sparse oblique projections.
  • NONE: No normalization.
  • STANDARD_DEVIATION: Normalize the feature by the estimated standard deviation on the entire train dataset. Also known as Z-Score normalization.
  • MIN_MAX: Normalize the feature by the range (i.e. max-min) estimated on the entire train dataset. Default: None.
  • sparse_oblique_num_projections_exponent For sparse oblique splits i.e. split_axis=SPARSE_OBLIQUE. Controls of the number of random projections to test at each node as num_features^num_projections_exponent. Default: None.
    sparse_oblique_projection_density_factor For sparse oblique splits i.e. split_axis=SPARSE_OBLIQUE. Controls of the number of random projections to test at each node as num_features^num_projections_exponent. Default: None.
    split_axis What structure of split to consider for numerical features.
  • AXIS_ALIGNED: Axis aligned splits (i.e. one condition at a time). This is the "classical" way to train a tree. Default value.
  • SPARSE_OBLIQUE: Sparse oblique splits (i.e. splits one a small number of features) from "Sparse Projection Oblique Random Forests", Tomita et al., 2020. Default: "AXIS_ALIGNED".
  • subsample Ratio of the dataset (sampling without replacement) used to train individual trees for the random sampling method. Default: 1.0.
    use_hessian_gain Use true, uses a formulation of split gain with a hessian term i.e. optimizes the splits to minimize the variance of "gradient / hessian. Available for all losses except regression. Default: False.
    validation_ratio Ratio of the training dataset used to monitor the training. Require to be >0 if early stopping is enabled. Default: 0.1.
    activity_regularizer Optional regularizer function for the output of this layer.
    compute_dtype The dtype of the layer's computations.

    This is equivalent to Layer.dtype_policy.compute_dtype. Unless mixed precision is used, this is the same as Layer.dtype, the dtype of the weights.

    Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in Layer.call, so you do not have to insert these casts if implementing your own layer.

    Layers often perform certain internal computations in higher precision when compute_dtype is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases.

    distribute_strategy The tf.distribute.Strategy this model was created under.
    dtype The dtype of the layer weights.

    This is equivalent to Layer.dtype_policy.variable_dtype. Unless mixed precision is used, this is the same as Layer.compute_dtype, the dtype of the layer's computations.

    dtype_policy The dtype policy associated with this layer.

    This is an instance of a tf.keras.mixed_precision.Policy.

    dynamic Whether the layer is dynamic (eager-only); set in the constructor.
    input Retrieves the input tensor(s) of a layer.

    Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

    input_spec InputSpec instance(s) describing the input format for this layer.

    When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

    self.input_spec = tf.keras.layers.InputSpec(ndim=4)
    

    Now, if you try to call the layer on an input that isn't rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

    ValueError: Input 0 of layer conv2d is incompatible with the layer:
    expected ndim=4, found ndim=1. Full shape received: [2]
    

    Input checks that can be specified via input_spec include:

    • Structure (e.g. a single input, a list of 2 inputs, etc)
    • Shape
    • Rank (ndim)
    • Dtype

    For more information, see tf.keras.layers.InputSpec.

    layers

    losses List of losses added using the add_loss() API.

    Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

    class MyLayer(tf.keras.layers.Layer):
      def call(self, inputs):
        self.add_loss(tf.abs(tf.reduce_mean(inputs)))
        return inputs
    l = MyLayer()
    l(np.ones((10, 1)))
    l.losses
    [1.0]
    
    inputs = tf.keras.Input(shape=(10,))
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    # Activity regularization.
    len(model.losses)
    0
    model.add_loss(tf.abs(tf.reduce_mean(x)))
    len(model.losses)
    1
    
    inputs = tf.keras.Input(shape=(10,))
    d = tf.keras.layers.Dense(10, kernel_initializer='ones')
    x = d(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    # Weight regularization.
    model.add_loss(lambda: tf.reduce_mean(d.kernel))
    model.losses
    [<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]
    

    metrics Returns the model's metrics added using compile, add_metric APIs.

    inputs = tf.keras.layers.Input(shape=(3,))
    outputs = tf.keras.layers.Dense(2)(inputs)
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
    [m.name for m in model.metrics]
    []
    
    x = np.random.random((2, 3))
    y = np.random.randint(0, 2, (2, 2))
    model.fit(x, y)
    [m.name for m in model.metrics]
    ['loss', 'mae']
    
    inputs = tf.keras.layers.Input(shape=(3,))
    d = tf.keras.layers.Dense(2, name='out')
    output_1 = d(inputs)
    output_2 = d(inputs)
    model = tf.keras.models.Model(
       inputs=inputs, outputs=[output_1, output_2])
    model.add_metric(
       tf.reduce_sum(output_2), name='mean', aggregation='mean')
    model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
    model.fit(x, (y, y))
    [m.name for m in model.metrics]
    ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
    'out_1_acc', 'mean']
    

    metrics_names Returns the model's display labels for all outputs.

    inputs = tf.keras.layers.Input(shape=(3,))
    outputs = tf.keras.layers.Dense(2)(inputs)
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
    model.metrics_names
    []
    
    x = np.random.random((2, 3))
    y = np.random.randint(0, 2, (2, 2))
    model.fit(x, y)
    model.metrics_names
    ['loss', 'mae']
    
    inputs = tf.keras.layers.Input(shape=(3,))
    d = tf.keras.layers.Dense(2, name='out')
    output_1 = d(inputs)
    output_2 = d(inputs)
    model = tf.keras.models.Model(
       inputs=inputs, outputs=[output_1, output_2])
    model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
    model.fit(x, (y, y))
    model.metrics_names
    ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
    'out_1_acc']
    

    name_scope Returns a tf.name_scope instance for this class.
    non_trainable_weights List of all non-trainable weights tracked by this layer.

    Non-trainable weights are not updated during training. They are expected to be updated manually in call().

    output Retrieves the output tensor(s) of a layer.

    Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

    run_eagerly Settable attribute indicating whether the model should run eagerly.

    Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls.

    By default, we will attempt to compile your model to a static graph to deliver the best execution performance.

    submodules Sequence of all sub-modules.

    Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

    a = tf.Module()
    b = tf.Module()
    c = tf.Module()
    a.b = b
    b.c = c
    list(a.submodules) == [b, c]
    True
    list(b.submodules) == [c]
    True
    list(c.submodules) == []
    True
    

    supports_masking Whether this layer supports computing a mask using compute_mask.
    trainable

    trainable_weights List of all trainable weights tracked by this layer.

    Trainable weights are updated via gradient descent during training.

    variable_dtype Alias of Layer.dtype, the dtype of the weights.
    weights Returns the list of all layer variables/weights.

    Methods

    add_loss

    Add loss tensor(s), potentially dependent on layer inputs.

    Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

    This method can be used inside a subclassed layer or model's call function, in which case losses should be a Tensor or list of Tensors.

    Example:

    class MyLayer(tf.keras.layers.Layer):
      def call(self, inputs):
        self.add_loss(tf.abs(tf.reduce_mean(inputs)))
        return inputs
    

    This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These losses become part of the model's topology and are tracked in get_config.

    Example:

    inputs = tf.keras.Input(shape=(10,))
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    # Activity regularization.
    model.add_loss(tf.abs(tf.reduce_mean(x)))
    

    If this is not the case for your loss (if, for example, your loss references a Variable of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized.

    Example:

    inputs = tf.keras.Input(shape=(10,))
    d = tf.keras.layers.Dense(10)
    x = d(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    # Weight regularization.
    model.add_loss(lambda: tf.reduce_mean(d.kernel))
    

    Args
    losses Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
    **kwargs Additional keyword arguments for backward compatibility. Accepted values: inputs - Deprecated, will be automatically inferred.

    add_metric

    Adds metric tensor to the layer.

    This method can be used inside the call() method of a subclassed layer or model.

    class MyMetricLayer(tf.keras.layers.Layer):
      def __init__(self):
        super(MyMetricLayer, self).__init__(name='my_metric_layer')
        self.mean = tf.keras.metrics.Mean(name='metric_1')
    
      def call(self, inputs):
        self.add_metric(self.mean(inputs))
        self.add_metric(tf.reduce_sum(inputs), name='metric_2')
        return inputs
    

    This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These metrics become part of the model's topology and are tracked when you save the model via save().

    inputs = tf.keras.Input(shape=(10,))
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    model.add_metric(math_ops.reduce_sum(x), name='metric_1')
    
    inputs = tf.keras.Input(shape=(10,))
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Dense(1)(x)
    model = tf.keras.Model(inputs, outputs)
    model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1')
    

    Args
    value Metric tensor.
    name String metric name.
    **kwargs Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

    build

    Builds the model based on input shapes received.

    This is to be used for subclassed models, which do not know at instantiation time what their inputs look like.

    This method only exists for users who want to call model.build() in a standalone way (as a substitute for calling the model on real data to build it). It will never be called by the framework (and thus it will never throw unexpected errors in an unrelated workflow).

    Args
    input_shape Single tuple, TensorShape, or list/dict of shapes, where shapes are tuples, integers, or TensorShapes.

    Raises
    ValueError

    1. In case of invalid user-provided data (not of type tuple, list, TensorShape, or dict).
    2. If the model requires call arguments that are agnostic to the input shapes (positional or kwarg in call signature).
    3. If not all layers were properly built.
    4. If float type inputs are not supported within the layers.

    In each of these cases, the user should build their model by calling it on real tensor data.

    call

    View source

    Inference of the model.

    This method is used for prediction and evaluation of a trained model.

    Args
    inputs Input tensors.
    training Is the model being trained. Always False.

    Returns
    Model predictions.

    compile

    View source

    Configure the model for training.

    Unlike for most Keras model, calling "compile" is optional before calling "fit".

    Args
    metrics Metrics to report during training.

    Raises
    ValueError Invalid arguments.

    compute_mask

    Computes an output mask tensor.

    Args
    inputs Tensor or list of tensors.
    mask Tensor or list of tensors.

    Returns
    None or a tensor (or list of tensors, one per output tensor of the layer).

    compute_output_shape

    Computes the output shape of the layer.

    If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

    Args
    input_shape Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

    Returns
    An input shape tuple.

    count_params

    Count the total number of scalars composing the weights.

    Returns
    An integer count.

    Raises
    ValueError if the layer isn't yet built (in which case its weights aren't yet defined).

    evaluate

    View source

    Returns the loss value & metrics values for the model.

    See details on keras.Model.evaluate.

    Args
    *args Passed to keras.Model.evaluate.
    **kwargs Passed to keras.Model.evaluate. Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). See details in keras.Model.evaluate.

    fit

    View source

    Trains the model.

    The following dataset formats are supported:

    1. "x" is a tf.data.Dataset containing a tuple "(features, labels)". "features" can be a dictionary a tensor, a list of tensors or a dictionary of tensors (recommended). "labels" is a tensor.

    2. "x" is a tensor, list of tensors or dictionary of tensors containing the input features. "y" is a tensor.

    3. "x" is a numpy-array, list of numpy-arrays or dictionary of numpy-arrays containing the input features. "y" is a numpy-array.

    Pandas Dataframe can be consumed with "dataframe_to_tf_dataset": dataset = pandas.Dataframe(...) model.fit(pd_dataframe_to_tf_dataset(dataset, label="my_label"))

    Args
    x Training dataset (See details above for the supported formats).
    y Label of the training dataset. Only used if "x" does not contains the labels.
    callbacks Callbacks triggered during the training.
    **kwargs Arguments passed to the core keras model's fit.

    Returns
    A History object. Its History.history attribute is not yet implemented for decision forests algorithms, and will return empty. All other fields are filled as usual for Keras.Mode.fit().

    from_config

    Creates a layer from its config.

    This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

    Args
    config A Python dictionary, typically the output of get_config.

    Returns
    A layer instance.

    get_config

    Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

    The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

    Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

    Returns
    Python dictionary.

    get_layer

    Retrieves a layer based on either its name (unique) or index.

    If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

    Args
    name String, name of layer.
    index Integer, index of layer.

    Returns
    A layer instance.

    Raises
    ValueError In case of invalid layer name or index.

    get_weights

    Retrieves the weights of the model.

    Returns
    A flat list of Numpy arrays.

    load_weights

    Loads all layer weights, either from a TensorFlow or an HDF5 weight file.

    If by_name is False weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.

    If by_name is True, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed.

    Only topological loading (by_name=False) is supported when loading weights from the TensorFlow format. Note that topological loading differs slightly between TensorFlow and HDF5 formats for user-defined classes inheriting from tf.keras.Model: HDF5 loads based on a flattened list of weights, while the TensorFlow format loads based on the object-local names of attributes to which layers are assigned in the Model's constructor.

    Args
    filepath String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to save_weights). This can also be a path to a SavedModel saved from model.save.
    by_name Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format.
    skip_mismatch Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when by_name=True).
    options Optional tf.train.CheckpointOptions object that specifies options for loading weights.

    Returns
    When loading a weight file in TensorFlow format, returns the same status object as tf.train.Checkpoint.restore. When graph building, restore ops are run automatically as soon as the network is built (on first call for user-defined classes inheriting from Model, immediately if it is already built).

    When loading weights in HDF5 format, returns None.

    Raises
    ImportError If h5py is not available and the weight file is in HDF5 format.
    ValueError If skip_mismatch is set to True when by_name is False.

    make_inspector

    View source

    Creates an inspector to access the internal model structure.

    Usage example:

    inspector = model.make_inspector()
    print(inspector.num_trees())
    print(inspector.variable_importances())
    

    Returns
    A model inspector.

    make_predict_function

    View source

    Prediction of the model (!= evaluation).

    make_test_function

    View source

    Predictions for evaluation.

    make_train_function

    Creates a function that executes one step of training.

    This method can be overridden to support custom training logic. This method is called by Model.fit and Model.train_on_batch.

    Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual training logic to Model.train_step.

    This function is cached the first time Model.fit or Model.train_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

    Args
    force Whether to regenerate the train function and skip the cached function if available.

    Returns
    Function. The function created by this method should accept a tf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_train_batch_end, such as {'loss': 0.2, 'accuracy': 0.7}.

    predefined_hyperparameters

    View source

    Returns a better than default set of hyper-parameters.

    They can be used directly with the hyperparameter_template argument of the model constructor.

    These hyper-parameters outperforms the default hyper-parameters (either generally or in specific scenarios). Like default hyper-parameters, existing pre-defined hyper-parameters cannot change.

    predict

    Generates output predictions for the input samples.

    Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using __call__ is recommended for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

    Args
    x Input samples. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
    • A tf.data dataset.
    • A generator or keras.utils.Sequence instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the Unpacking behavior for iterator-like inputs section of Model.fit.
    batch_size Integer or None. Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of dataset, generators, or keras.utils.Sequence instances (since they generate batches).
    verbose Verbosity mode, 0 or 1.
    steps Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, predict will run until the input dataset is exhausted.
    callbacks List of keras.callbacks.Callback instances. List of callbacks to apply during prediction. See callbacks.
    max_queue_size Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
    workers Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
    use_multiprocessing Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

    See the discussion of Unpacking behavior for iterator-like inputs for Model.fit. Note that Model.predict uses the same interpretation rules as Model.fit and Model.evaluate, so inputs must be unambiguous for all three methods.

    Returns
    Numpy array(s) of predictions.

    Raises
    RuntimeError If model.predict is wrapped in tf.function.
    ValueError In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size.

    predict_on_batch

    Returns predictions for a single batch of samples.

    Args
    x Input data. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).

    Returns
    Numpy array(s) of predictions.

    Raises
    RuntimeError If model.predict_on_batch is wrapped in tf.function.
    ValueError In case of mismatch between given number of inputs and expectations of the model.

    predict_step

    The logic for one inference step.

    This method can be overridden to support custom inference logic. This method is called by Model.make_predict_function.

    This method should contain the mathematical logic for one step of inference. This typically includes the forward pass.

    Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_predict_function, which can also be overridden.

    Args
    data A nested structure of Tensors.

    Returns
    The result of one inference step, typically the output of calling the Model on data.

    reset_metrics

    Resets the state of all the metrics in the model.

    Examples:

    inputs = tf.keras.layers.Input(shape=(3,))
    outputs = tf.keras.layers.Dense(2)(inputs)
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
    
    x = np.random.random((2, 3))
    y = np.random.randint(0, 2, (2, 2))
    _ = model.fit(x, y, verbose=0)
    assert all(float(m.result()) for m in model.metrics)
    
    model.reset_metrics()
    assert all(float(m.result()) == 0 for m in model.metrics)
    

    reset_states

    save

    View source

    Saves the model as a TensorFlow SavedModel.

    The exported SavedModel contains a standalone Yggdrasil Decision Forests model in the "assets" sub-directory. The Yggdrasil model can be used directly using the Yggdrasil API. However, this model does not contain the "preprocessing" layer (if any).

    Args
    filepath Path to the output model.
    overwrite If true, override an already existing model. If false, raise an error if a model already exist.
    **kwargs Arguments passed to the core keras model's save.

    save_spec

    Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).

    This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving:

    model = tf.keras.Model(...)
    
    @tf.function
    def serve(*args, **kwargs):
      outputs = model(*args, **kwargs)
      # Apply postprocessing steps, or add additional outputs.
      ...
      return outputs
    
    # arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this example, is
    # an empty dict since functional models do not use keyword arguments.
    arg_specs, kwarg_specs = model.save_spec()
    
    model.save(path, signatures={
      'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs)
    })
    

    Args
    dynamic_batch Whether to set the batch sizes of all the returned tf.TensorSpec to None. (Note that when defining functional or Sequential models with tf.keras.Input([...], batch_size=X), the batch size will always be preserved). Defaults to True.

    Returns
    If the model inputs are defined, returns a tuple (args, kwargs). All elements in args and kwargs are tf.TensorSpec. If the model inputs are not defined, returns None. The model inputs are automatically set when calling the model, model.fit, model.evaluate or model.predict.

    save_weights

    Saves all layer weights.

    Either saves in HDF5 or in TensorFlow format based on the save_format argument.

    When saving in HDF5 format, the weight file has:

    • layer_names (attribute), a list of strings (ordered names of model layers).
    • For every layer, a group named layer.name
      • For every such layer group, a group attribute weight_names, a list of strings (ordered names of weights tensor of the layer).
      • For every weight in the layer, a dataset storing the weight value, named after the weight tensor.

    When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs), Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

    While the formats are the same, do not mix save_weights and tf.train.Checkpoint. Checkpoints saved by Model.save_weights should be loaded using Model.load_weights. Checkpoints saved using tf.train.Checkpoint.save should be restored using the corresponding tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over save_weights for training checkpoints.

    The TensorFlow format matches objects and variables by starting at a root object, self for save_weights, and greedily matching attribute names. For Model.save this is the Model, and for Checkpoint.save this is the Checkpoint even if the Checkpoint has a model attached. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model's variables. See the guide to training checkpoints for details on the TensorFlow format.

    Args
    filepath String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.
    overwrite Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
    save_format Either 'tf' or 'h5'. A filepath ending in '.h5' or '.keras' will default to HDF5 if save_format is None. Otherwise None defaults to 'tf'.
    options Optional tf.train.CheckpointOptions object that specifies options for saving weights.

    Raises
    ImportError If h5py is not available when attempting to save in HDF5 format.
    ValueError For invalid/unknown format arguments.

    set_weights

    Sets the weights of the layer, from NumPy arrays.

    The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.

    For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

    layer_a = tf.keras.layers.Dense(1,
      kernel_initializer=tf.constant_initializer(1.))
    a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
    layer_a.get_weights()
    [array([[1.],
           [1.],
           [1.]], dtype=float32), array([0.], dtype=float32)]
    layer_b = tf.keras.layers.Dense(1,
      kernel_initializer=tf.constant_initializer(2.))
    b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
    layer_b.get_weights()
    [array([[2.],
           [2.],
           [2.]], dtype=float32), array([0.], dtype=float32)]
    layer_b.set_weights(layer_a.get_weights())
    layer_b.get_weights()
    [array([[1.],
           [1.],
           [1.]], dtype=float32), array([0.], dtype=float32)]
    

    Args
    weights a list of NumPy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).

    Raises
    ValueError If the provided weights list does not match the layer's specifications.

    summary

    View source

    Shows information about the model.

    test_on_batch

    Test the model on a single batch of samples.

    Args
    x Input data. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
    • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
    y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely).
    sample_weight Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
    reset_metrics If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
    return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

    Returns
    Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

    Raises
    RuntimeError If model.test_on_batch is wrapped in tf.function.
    ValueError In case of invalid user-provided arguments.

    test_step

    The logic for one evaluation step.

    This method can be overridden to support custom evaluation logic. This method is called by Model.make_test_function.

    This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.

    Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_test_function, which can also be overridden.

    Args
    data A nested structure of Tensors.

    Returns
    A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model's metrics are returned.

    to_json

    Returns a JSON string containing the network configuration.

    To load a network from a JSON save file, use keras.models.model_from_json(json_string, custom_objects={}).

    Args
    **kwargs Additional keyword arguments to be passed to json.dumps().

    Returns
    A JSON string.

    to_yaml

    Returns a yaml string containing the network configuration.

    To load a network from a yaml save file, use keras.models.model_from_yaml(yaml_string, custom_objects={}).

    custom_objects should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes.

    Args
    **kwargs Additional keyword arguments to be passed to yaml.dump().

    Returns
    A YAML string.

    Raises
    RuntimeError announces that the method poses a security risk (Use the safer safe_load function instead of unsafe_load when possible)

    train_on_batch

    Runs a single gradient update on a single batch of data.

    Args
    x Input data. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
    • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
    y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely).
    sample_weight Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
    class_weight Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
    reset_metrics If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
    return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

    Returns
    Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

    Raises
    RuntimeError If model.train_on_batch is wrapped in tf.function.
    ValueError In case of invalid user-provided arguments.

    train_step

    View source

    Collects training examples.

    with_name_scope

    Decorator to automatically enter the module name scope.

    class MyModule(tf.Module):
      @tf.Module.with_name_scope
      def __call__(self, x):
        if not hasattr(self, 'w'):
          self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
        return tf.matmul(x, self.w)
    

    Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

    mod = MyModule()
    mod(tf.ones([1, 2]))
    <tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
    mod.w
    <tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
    numpy=..., dtype=float32)>
    

    Args
    method The method to wrap.

    Returns
    The original method wrapped such that it enters the module's name scope.

    yggdrasil_model_path_tensor

    View source

    Gets the path to yggdrasil model, if available.

    The effective path can be obtained with:

    yggdrasil_model_path_tensor().numpy().decode("utf-8")
    

    Returns
    Path to the Yggdrasil model.

    __call__

    Wraps call, applying pre- and post-processing steps.

    Args
    *args Positional arguments to be passed to self.call.
    **kwargs Keyword arguments to be passed to self.call.

    Returns
    Output tensor(s).

    Note:

    • The following optional keyword arguments are reserved for specific uses:
      • training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
      • mask: Boolean input mask.
    • If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.
    • If the layer is not built, the method will call build.

    Raises
    ValueError if the layer's call method returns None (an invalid value).
    RuntimeError if super().__init__() was not called in the constructor.