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Attend the Women in ML Symposium on December 7

# tfdf.keras.RandomForestModel

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Random Forest learning algorithm.

### Used in the notebooks

A Random Forest (https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf) is a collection of deep CART decision trees trained independently and without pruning. Each tree is trained on a random subset of the original training dataset (sampled with replacement).

The algorithm is unique in that it is robust to overfitting, even in extreme cases e.g. when there are more features than training examples.

It is probably the most well-known of the Decision Forest training algorithms.

#### 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.RandomForestModel()
model.fit(tf_dataset)

print(model.summary())

Hyper-parameter tuning:

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")

tuner = tfdf.tuner.RandomSearch(num_trials=20)

# Hyper-parameters to optimize.
tuner.discret("max_depth", [4, 5, 6, 7])

model = tfdf.keras.RandomForestModel(tuner=tuner)
model.fit(tf_dataset)

print(model.summary())

task Task to solve (e.g. Task.CLASSIFICATION, Task.REGRESSION, Task.RANKING, Task.CATEGORICAL_UPLIFT, Task.NUMERICAL_UPLIFT).
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.
uplift_treatment Only for task=Task.CATEGORICAL_UPLIFT or task=Task.NUMERICAL_UPLIFT. Name of an integer feature that identifies the treatment in an uplift problem. The value 0 is reserved for the control treatment.
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 Verbosity mode. 0 = silent, 1 = small details, 2 = full details.
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: winner_take_all=True.
• benchmark_rank1@v1: Top ranking hyper-parameters on our benchmark slightly modified to run in reasonable time. The parameters are: winner_take_all=True, 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 None, num_threads will be automatically set to the number of processors (up to a maximum of 32; or set to 6 if the number of processors is not available). Making num_threads significantly larger than the number of processors can slow-down the training speed. The default value logic might change in the future.
name The name of the model.
max_vocab_count Default maximum size of the vocabulary for CATEGORICAL and CATEGORICAL_SET features stored as strings. If more unique values exist, only the most frequent values are kept, and the remaining values are considered as out-of-vocabulary. The value max_vocab_count defined in a FeatureUsage (if any) takes precedence.
try_resume_training If true, the model training resumes from the checkpoint stored in the temp_directory directory. If temp_directory does not contain any model checkpoint, the training start from the beginning. Resuming training is useful in the following situations: (1) The training was interrupted by the user (e.g. ctrl+c or "stop" button in a notebook). (2) the training job was interrupted (e.g. rescheduling), ond (3) the hyper-parameter of the model were changed such that an initially completed training is now incomplete (e.g. increasing the number of trees). Note: Training can only be resumed if the training datasets is exactly the same (i.e. no reshuffle in the tf.data.Dataset).
check_dataset If set to true, test if the dataset is well configured for the training: (1) Check if the dataset does contains any repeat operations, (2) Check if the dataset does contain a batch operation, (3) Check if the dataset has a large enough batch size (min 100 if the dataset contains more than 1k examples or if the number of examples is not available) If set to false, do not run any test.
tuner If set, automatically optimize the hyperparameters of the model using this tuner. If the model is trained with distribution (i.e. the model definition is wrapper in a TF Distribution strategy, the tuning is distributed.
discretize_numerical_features If true, discretize all the numerical features before training. Discretized numerical features are faster to train with, but they can have a negative impact on the model quality. Using discretize_numerical_features=True is equivalent as setting the feature semantic DISCRETIZED_NUMERICAL in the feature argument. See the definition of DISCRETIZED_NUMERICAL for more details.
num_discretize_numerical_bins Number of bins used when disretizing numerical features. The value num_discretized_numerical_bins defined in a FeatureUsage (if any) takes precedence.
adapt_bootstrap_size_ratio_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, adapts the size of the sampled dataset used to train each tree such that num_trees will train within maximum_training_duration. Has no effect if there is no maximum training duration specified. 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.
bootstrap_size_ratio Number of examples used to train each trees; expressed as a ratio of the training dataset size. Default: 1.0.
bootstrap_training_dataset If true (default), each tree is trained on a separate dataset sampled with replacement from the original dataset. If false, all the trees are trained on the entire same dataset. If bootstrap_training_dataset:false, OOB metrics are not available. bootstrap_training_dataset=false is used in "Extremely randomized trees" (https://link.springer.com/content/pdf/10.1007%2Fs10994-006-6226-1.pdf). 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.
compute_oob_performances If true, compute the Out-of-bag evaluation (then available in the summary and model inspector). This evaluation is a cheap alternative to cross-validation evaluation. Default: True.
compute_oob_variable_importances If true, compute the Out-of-bag feature importance (then available in the summary and model inspector). Note that the OOB feature importance can be expensive to compute. Default: False.
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".
• honest In honest trees, different training examples are used to infer the structure and the leaf values. This regularization technique trades examples for bias estimates. It might increase or reduce the quality of the model. See "Generalized Random Forests", Athey et al. In this paper, Honest trees are trained with the Random Forest algorithm with a sampling without replacement. Default: False.
honest_fixed_separation For honest trees only i.e. honest=true. If true, a new random separation is generated for each tree. If false, the same separation is used for all the trees (e.g., in Gradient Boosted Trees containing multiple trees). Default: False.
honest_ratio_leaf_examples For honest trees only i.e. honest=true. Ratio of examples used to set the leaf values. Default: 0.5.
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.
keep_non_leaf_label_distribution Whether to keep the node value (i.e. the distribution of the labels of the training examples) of non-leaf nodes. This information is not used during serving, however it can be used for model interpretation as well as hyper parameter tuning. This can take lots of space, sometimes accounting for half of the model size. Default: True.
max_depth Maximum depth of the tree. max_depth=1 means that all trees will be roots. Negative values are ignored. Default: 16.
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_model_size_in_memory_in_bytes Limit the size of the model when stored in ram. Different algorithms can enforce this limit differently. Note that when models are compiled into an inference, the size of the inference engine is generally much smaller than the original model. Default: -1.0.
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: 0.
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_oob_variable_importances_permutations Number of time the dataset is re-shuffled to compute the permutation variable importances. Increasing this value increase the training time (if "compute_oob_variable_importances:true") as well as the stability of the oob variable importance metrics. Default: 1.
num_trees Number of individual decision trees. Increasing the number of trees can increase the quality of the model at the expense of size, training speed, and inference latency. Default: 300.
pure_serving_model Clear the model from any information that is not required for model serving. This includes debugging, model interpretation and other meta-data. The size of the serialized model can be reduced significatively (50% model size reduction is common). This parameter has no impact on the quality, serving speed or RAM usage of model serving. Default: False.
random_seed Random seed for the training of the model. Learners are expected to be deterministic by the random seed. Default: 123456.
sampling_with_replacement If true, the training examples are sampled with replacement. If false, the training samples are sampled without replacement. Only used when "bootstrap_training_dataset=true". If false (sampling without replacement) and if "bootstrap_size_ratio=1" (default), all the examples are used to train all the trees (you probably do not want that). Default: True.
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.
sparse_oblique_weights For sparse oblique splits i.e. split_axis=SPARSE_OBLIQUE. Possible values:
• BINARY: The oblique weights are sampled in {-1,1} (default).
• CONTINUOUS: The oblique weights are be sampled in [-1,1]. 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".
• uplift_min_examples_in_treatment For uplift models only. Minimum number of examples per treatment in a node. Default: 5.
uplift_split_score For uplift models only. Splitter score i.e. score optimized by the splitters. The scores are introduced in "Decision trees for uplift modeling with single and multiple treatments", Rzepakowski et al. Notation: p probability / average value of the positive outcome, q probability / average value in the control group.
• KULLBACK_LEIBLER or KL: - p log (p/q)
• EUCLIDEAN_DISTANCE or ED: (p-q)^2
• CHI_SQUARED or CS: (p-q)^2/q Default: "KULLBACK_LEIBLER".
• winner_take_all Control how classification trees vote. If true, each tree votes for one class. If false, each tree vote for a distribution of classes. winner_take_all_inference=false is often preferable. Default: True.
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_reduction_method The method employed to reduce per-replica values during training.

Unless specified, the value "auto" will be assumed, indicating that the reduction strategy should be chosen based on the current running environment. See reduce_per_replica function for more details.

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

learner Name of the learning algorithm used to train the model.
learner_params Gets the dictionary of hyper-parameters passed in the model constructor.

Changing this dictionary will impact the training.

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().

num_training_examples Number of training examples.
num_validation_examples Number of validation examples.
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.

training_model_id Identifier of the model.
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 Used for backwards compatibility only.

### 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 instance, or list/dict of shapes, where shapes are tuples, integers, or TensorShape instances.

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 keyword arg 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.

### call_get_leaves

View source

Computes the index of the active leaf in each tree.

The active leaf is the leave that that receive the example during inference.

The returned value "leaves[i,j]" is the index of the active leave for the i-th example and the j-th tree. Leaves are indexed by depth first exploration with the negative child visited before the positive one (similarly as "iterate_on_nodes()" iteration). Leaf indices are also available with LeafNode.leaf_idx.

Args
inputs Input tensors. Same signature as the model's "call(inputs)".

Returns
Index of the active leaf for each tree in the model.

### capabilities

View source

Lists the capabilities of the learning algorithm.

### collect_data_step

View source

Collect examples e.g. training or validation.

### compile

View source

Configure the model for training.

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

Args
metrics List of metrics to be evaluated by the model during training and testing.
weighted_metrics List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.

Raises
ValueError Invalid arguments.

### compute_loss

Compute the total loss, validate it, and return it.

Subclasses can optionally override this method to provide custom loss computation logic.

#### Example:

class MyModel(tf.keras.Model):

def __init__(self, *args, **kwargs):
super(MyModel, self).__init__(*args, **kwargs)
self.loss_tracker = tf.keras.metrics.Mean(name='loss')

def compute_loss(self, x, y, y_pred, sample_weight):
loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y))
loss += tf.add_n(self.losses)
self.loss_tracker.update_state(loss)
return loss

def reset_metrics(self):
self.loss_tracker.reset_states()

@property
def metrics(self):
return [self.loss_tracker]

tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,))
dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)

inputs = tf.keras.layers.Input(shape=(10,), name='my_input')
outputs = tf.keras.layers.Dense(10)(inputs)
model = MyModel(inputs, outputs)
model.add_loss(tf.reduce_sum(outputs))

optimizer = tf.keras.optimizers.SGD()
model.compile(optimizer, loss='mse', steps_per_execution=10)
model.fit(dataset, epochs=2, steps_per_epoch=10)
print('My custom loss: ', model.loss_tracker.result().numpy())

Args
x Input data.
y Target data.
y_pred Predictions returned by the model (output of model(x))
sample_weight Sample weights for weighting the loss function.

Returns
The total loss as a tf.Tensor, or None if no loss results (which is the case when called by Model.test_step).

### 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_metrics

Update metric states and collect all metrics to be returned.

Subclasses can optionally override this method to provide custom metric updating and collection logic.

#### Example:

class MyModel(tf.keras.Sequential):

def compute_metrics(self, x, y, y_pred, sample_weight):

# This super call updates self.compiled_metrics and returns
# results for all metrics listed in self.metrics.
metric_results = super(MyModel, self).compute_metrics(
x, y, y_pred, sample_weight)

# Note that self.custom_metric is not listed in self.metrics.
self.custom_metric.update_state(x, y, y_pred, sample_weight)
metric_results['custom_metric_name'] = self.custom_metric.result()
return metric_results

Args
x Input data.
y Target data.
y_pred Predictions returned by the model (output of model.call(x))
sample_weight Sample weights for weighting the loss function.

Returns
A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end(). Typically, the values of the metrics listed in self.metrics are returned. Example: {'loss': 0.2, 'accuracy': 0.7}.

### compute_output_shape

Computes the output shape of the layer.

This method will cause the layer's state to be built, if that has not happened before. This requires 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 tf.TensorShape, or structure of shape tuples / tf.TensorShape instances (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns
A tf.TensorShape instance or structure of tf.TensorShape instances.

### 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

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

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.
• A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
• A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). 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.
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). If x is a dataset, generator or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from the iterator/dataset).
batch_size Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a dataset, generators, or keras.utils.Sequence instances (since they generate batches).
verbose "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" defaults to 1 for most cases, and to 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment).
sample_weight Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or 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. This argument is not supported when x is a dataset, instead pass sample weights as the third element of x.
steps Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs.
callbacks List of keras.callbacks.Callback instances. List of callbacks to apply during evaluation. 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.
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.
**kwargs Unused at this time.

See the discussion of Unpacking behavior for iterator-like inputs for Model.fit.

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.evaluate is wrapped in a tf.function.

### fit

View source

Trains the model.

# Local training

It is recommended to use a Pandas Dataframe dataset and to convert it to a TensorFlow dataset with pd_dataframe_to_tf_dataset():

  pd_dataset = pandas.Dataframe(...)
tf_dataset = pd_dataframe_to_tf_dataset(dataset, label="my_label")
model.fit(pd_dataset)

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.

1. The dataset need to be read exactly once. If you use a TensorFlow dataset, make sure NOT to add a "repeat" operation.
2. The algorithm does not benefit from shuffling the dataset. If you use a TensorFlow dataset, make sure NOT to add a "shuffle" operation.
3. The dataset needs to be batched (i.e. with a "batch" operation). However, the number of elements per batch has not impact on the model. Generally, it is recommended to use batches as large as possible as its speeds-up reading the dataset in TensorFlow.

Input features do not need to be normalized (e.g. dividing numerical values by the variance) or indexed (e.g. replacing categorical string values by an integer). Additionally, missing values can be consumed natively.

# Distributed training

Some of the learning algorithms will support distributed training with the ParameterServerStrategy.

In this case, the dataset is read asynchronously in between the workers. The distribution of the training depends on the learning algorithm.

Like for non-distributed training, the dataset should be read exactly once. The simplest solution is to divide the dataset into different files (i.e. shards) and have each of the worker read a non overlapping subset of shards.

Currently (to be changed), the validation dataset (if provided) is simply feed to the model.evaluate() method. Therefore, it should satisfy Keras' evaluate API. Notably, for distributed training, the validation dataset should be infinite (i.e. have a repeat operation).

See https://www.tensorflow.org/decision_forests/distributed_training for more details and examples.

Here is a single example of distributed training using PSS for both dataset reading and training distribution.

  def dataset_fn(context, paths, training=True):
ds_path = tf.data.Dataset.from_tensor_slices(paths)

if context is not None:
# Train on at least 2 workers.
current_worker = tfdf.keras.get_worker_idx_and_num_workers(context)
assert current_worker.num_workers > 2

# Split the dataset's examples among the workers.
ds_path = ds_path.shard(
num_shards=current_worker.num_workers,
index=current_worker.worker_idx)

def read_csv_file(path):
numerical = tf.constant([math.nan], dtype=tf.float32)
categorical_string = tf.constant([""], dtype=tf.string)
csv_columns = [
numerical,  # age
categorical_string,  # workclass
numerical,  # fnlwgt
...
]
column_names = [
"age", "workclass", "fnlwgt", ...
]
label_name = "label"
return tf.data.experimental.CsvDataset(path, csv_columns, header=True)

ds_columns = ds_path.interleave(read_csv_file)

def map_features(*columns):
assert len(column_names) == len(columns)
features = {column_names[i]: col for i, col in enumerate(columns)}
label = label_table.lookup(features.pop(label_name))
return features, label

ds_dataset = ds_columns.map(map_features)
if not training:
dataset = dataset.repeat(None)
ds_dataset = ds_dataset.batch(batch_size)
return ds_dataset

strategy = tf.distribute.experimental.ParameterServerStrategy(...)
sharded_train_paths = [list of dataset files]
with strategy.scope():
model = DistributedGradientBoostedTreesModel()
train_dataset = strategy.distribute_datasets_from_function(
lambda context: dataset_fn(context, sharded_train_paths))

test_dataset = strategy.distribute_datasets_from_function(
lambda context: dataset_fn(context, sharded_test_paths))

model.fit(sharded_train_paths)
evaluation = model.evaluate(test_dataset, steps=num_test_examples //
batch_size)

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. The training runs in a single epoch, itself run in a single step. Therefore, callback logic can be called equivalently before/after the fit function.
verbose Verbosity mode. 0 = silent, 1 = small details, 2 = full details.
validation_steps Number of steps in the evaluation dataset when evaluating the trained model with model.evaluate(). If not specified, evaluates the model on the entire dataset (generally recommended; not yet supported for distributed datasets).
validation_data Validation dataset. If specified, the learner might use this dataset to help training e.g. early stopping.
sample_weight Training weights. Note: training weights can also be provided as the third output in a tf.data.Dataset e.g. (features, label, weights).
steps_per_epoch [Parameter will be removed] Number of training batch to load before training the model. Currently, only supported for distributed training.
class_weight For binary classification only. Mapping class indices (integers) to a weight (float) value. Only available for non-Distributed training. For maximum compatibility, feed example weights through the tf.data.Dataset or using the weight argument of pd_dataframe_to_tf_dataset.
**kwargs Extra arguments passed to the core keras model's fit. Note that not all keras' model fit arguments are supported.

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().

### fit_on_dataset_path

View source

Trains the model on a dataset stored on disk.

This solution is generally more efficient and easier than loading the dataset with a tf.Dataset both for local and distributed training.

Usage example

# Local training

model = keras.GradientBoostedTreesModel()
model.fit_on_dataset_path(
train_path="/path/to/dataset.csv",
label_key="label",
dataset_format="csv")
model.save("/model/path")

# Distributed training

with tf.distribute.experimental.ParameterServerStrategy(...).scope():
model = model = keras.DistributedGradientBoostedTreesModel()
model.fit_on_dataset_path(
train_path="/path/to/dataset@10",
label_key="label",
dataset_format="tfrecord+tfe")
model.save("/model/path")

Args
train_path Path to the training dataset. Supports comma separated files, shard and glob notation.
label_key Name of the label column.
weight_key Name of the weighing column.
ranking_key Name of the ranking column.
valid_path Path to the validation dataset. If not provided, or if the learning algorithm does not supports/needs a validation dataset, valid_path is ignored.
dataset_format Format of the dataset. Should be one of the registered dataset format (see User Manual for more details). The format "csv" is always available but it is generally only suited for small datasets.
max_num_scanned_rows_to_accumulate_statistics Maximum number of examples to scan to determine the statistics of the features (i.e. the dataspec, e.g. mean value, dictionaries). (Currently) the "first" examples of the dataset are scanned (e.g. the first examples of the dataset is a single file). Therefore, it is important that the sampled dataset is relatively uniformly sampled, notably the scanned examples should contains all the possible categorical values (otherwise the not seen value will be treated as out-of-vocabulary). If set to None, the entire dataset is scanned. This parameter has no effect if the dataset is stored in a format that already contains those values.
try_resume_training If true, tries to resume training from the model checkpoint stored in the temp_directory directory. If temp_directory does not contain any model checkpoint, start the training from the start. Works in the following three situations: (1) The training was interrupted by the user (e.g. ctrl+c). (2) the training job was interrupted (e.g. rescheduling), ond (3) the hyper-parameter of the model were changed such that an initially completed training is now incomplete (e.g. increasing the number of trees).
input_model_signature_fn A lambda that returns the (Dense,Sparse,Ragged)TensorSpec (or structure of TensorSpec e.g. dictionary, list) corresponding to input signature of the model. If not specified, the input model signature is created by build_default_input_model_signature. For example, specify input_model_signature_fn if an numerical input feature (which is consumed as DenseTensorSpec(float32) by default) will be feed differently (e.g. RaggedTensor(int64)).
num_io_threads Number of threads to use for IO operations e.g. reading a dataset from disk. Increasing this value can speed-up IO operations when IO operations are either latency or cpu bounded.

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 Model.

Config is a Python dictionary (serializable) containing the configuration of an object, which in this case is a Model. This allows the Model to be be reinstantiated later (without its trained weights) from this configuration.

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.

Developers of subclassed Model are advised to override this method, and continue to update the dict from super(MyModel, self).get_config() to provide the proper configuration of this Model. The default config is an empty dict. Optionally, raise NotImplementedError to allow Keras to attempt a default serialization.

Returns
Python dictionary containing the configuration of this Model.

### 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.

### get_metrics_result

Returns the model's metrics values as a dict.

If any of the metric result is a dict (containing multiple metrics), each of them gets added to the top level returned dict of this method.

Returns
A dict containing values of the metrics listed in self.metrics.
Example {'loss': 0.2, 'accuracy': 0.7}.

### get_weight_paths

Retrieve all the variables and their paths for the model.

The variable path (string) is a stable key to indentify a tf.Variable instance owned by the model. It can be used to specify variable-specific configurations (e.g. DTensor, quantization) from a global view.

This method returns a dict with weight object paths as keys and the corresponding tf.Variable instances as values.

Note that if the model is a subclassed model and the weights haven't been initialized, an empty dict will be returned.

Returns
A dict where keys are variable paths and values are tf.Variable instances.

#### Example:

class SubclassModel(tf.keras.Model):

def __init__(self, name=None):
super().__init__(name=name)
self.d1 = tf.keras.layers.Dense(10)
self.d2 = tf.keras.layers.Dense(20)

def call(self, inputs):
x = self.d1(inputs)
return self.d2(x)

model = SubclassModel()
model(tf.zeros((10, 10)))
weight_paths = model.get_weight_paths()
# weight_paths:
# {
#    'd1.kernel': model.d1.kernel,
#    'd1.bias': model.d1.bias,
#    'd2.kernel': model.d2.kernel,
#    'd2.bias': model.d2.bias,
# }

# Functional model
inputs = tf.keras.Input((10,), batch_size=10)
x = tf.keras.layers.Dense(20, name='d1')(inputs)
output = tf.keras.layers.Dense(30, name='d2')(x)
model = tf.keras.Model(inputs, output)
d1 = model.layers[1]
d2 = model.layers[2]
weight_paths = model.get_weight_paths()
# weight_paths:
# {
#    'd1.kernel': d1.kernel,
#    'd1.bias': d1.bias,
#    'd2.kernel': d2.kernel,
#    'd2.bias': d2.bias,
# }

### get_weights

Retrieves the weights of the model.

Returns
A flat list of Numpy arrays.

### load_weights

View source

No-op for TensorFlow Decision Forests models.

load_weights is not supported by TensorFlow Decision Forests models. To save and restore a model, use the SavedModel API i.e. model.save(...) and tf.keras.models.load_model(...). To resume the training of an existing model, create the model with try_resume_training=True (default value) and with a similar temp_directory argument. See documentation of try_resume_training for more details.

Args
*args Passed through to base keras.Model implemenation.
**kwargs Passed through to base keras.Model implemenation.

### 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 outperform 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 batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.

For small numbers of inputs that fit in one batch, directly use __call__() for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behave differently during inference. You may pair the individual model call with a tf.function for additional performance inside your inner loop. If you need access to numpy array values instead of tensors after your model call, you can use tensor.numpy() to get the numpy array value of an eager tensor.

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 "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" defaults to 1 for most cases, and to 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment).
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 a 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_get_leaves

View source

Gets the index of the active leaf of each tree.

The active leaf is the leave that that receive the example during inference.

The returned value "leaves[i,j]" is the index of the active leave for the i-th example and the j-th tree. Leaves are indexed by depth first exploration with the negative child visited before the positive one (similarly as "iterate_on_nodes()" iteration). Leaf indices are also available with LeafNode.leaf_idx.

Args
x Input samples as a tf.data.Dataset.

Returns
Index of the active leaf for each tree in the model.

### 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 a tf.function.

### 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)

### 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.

### 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 a tf.function.

### 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

### 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).
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 a tf.function.

### train_step

View source

Collects training examples.

### valid_step

View source

Collects validation 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.

### yggdrasil_model_prefix

View source

Gets the prefix of the internal yggdrasil model.

### __call__

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]


text:
# Multiple variables with multiple solutions

I'm not sure how best to explain this so please ask questions to help clarify. I'm trying to find a solution to the following formula, where I know there are multiple "correct" solutions.

Essentially I have three materials, each having a different density. They need to be mixed in some proportion to create the mean density provided.

Formula for density is: $d = m/v$, or $density = {mass \over volume}$

$x$ has a mass of $8g/cm^3$
$y$ has a mass of $3g/cm^3$
$z$ has a mass of $1g/cm^3$, therefore:

$m = 8x + 3y + 1z$
$v = x + y + z$
$d = 5.515g/cm^3$

$5.515 = {8x + 3y + 1z \over x + y + z}$

How do I go about solving this? I can approximate it by plugging into values randomly ($x=3, y=1, z=1$), but is there a more exacting process?

-

You can parametrize the solution as:

$$\left(x,y,\frac{1}{5.515-1}[(8-5.515)x+(3-5.515)y]\right)$$

where all I've done is just rearrange your equation and solve for $z$ in terms of $x,y$. In other words, any choice of $x,y$ above will give you a solution. Presumably since $x,y,z$ refer to volume, they must be non-negative for any of this to make sense.

-
Thanks for that Alex. That makes a little sense, rearranging it like that. The part I'm unclear of is what I do with the $x, y,$ at the start? Are they just multiplied in? Plugging in values of $x=3, y=1$, how might it work? (sorry if this is a really basic question) –  Carl Sep 29 '12 at 19:32
@Carl: You have one equation for 3 unknowns, which effectively means that you can solve for at most one of the unknowns in terms of the other two. In other words, by solving for $z$ in terms of $x,y$, we are guaranteeing that for any choice of $x,y$, the triple I've written above shall satisfy your density equation. So, by plugging in $x=3,y=1$, you will get a value for $z$ as above, and then the triple $(x,y,z)$ shall be a solution to your equation. Again, it may be worth considering that $x,y,z\geq 0$ should be a restriction, so for some $x,y\geq 0$ you might get $z<0$. –  Alex R. Sep 29 '12 at 20:22
Thanks for the further explanation. That makes sense now. Yes, $x, y, z \ge 0$. I guess rearranging the equation to solve for $x$ or $y$ would be tricky? –  Carl Sep 29 '12 at 20:35

You can add another condition, maybe something like minimizing the price of the materials, then it restricts the solutions. If nothing else matters, pick randomly.

-
Hi Berci. Thanks for that idea too. I would love to add in another condition, but in this particular instance, I can't. Otherwise it might certainly make it easier to restrict the values of $x, y, z$. –  Carl Sep 29 '12 at 20:49

format: plain


text:
# nLab localizable measure

Localizable measures

### Context

#### Measure and probability theory

measure theory

probability theory

# Localizable measures

## Idea

Many of the important theorems of measure theory fail to hold in full generality. (See below under Theorems for which theorems we're talking about.) Often these theorems are stated for $\sigma$-finite measures, but they do hold a bit more generally than that. In fact, they hold for localizable measures, and this fact characterizes the localizable measures.

## Definition

The following definition is in elementary terms; but we will see that there are many other characterizations.

Let $\mu$ be a positive measure on an abstract set $X$. (That is, certain subsets of $X$, forming a $\sigma$-algebra, are measurable by $\mu$, and $\mu$ maps these sets to the space $[0,\infty]$ of lower real numbers in a monotone and countably additive way.)

Given two measurable subsets $E$ and $F$, $E$ essentially contains $F$ if the set

$\{ x\colon X \;|\; x \in F \;\Rightarrow\; x \in E \}$

is full; or equivalently (using excluded middle) if $F \setminus E$ is null. (This is a preorder on the measurable sets.)

Then $\mu$ is localizable if the following conditions both apply:

• Semifiniteness: Every measurable set $E$ with positive measure essentially contains a measurable set with finite positive measure. (We may strengthen ‘essentially contains’ to ‘contains’ in this clause.)

• Essential suprema: Given any collection $\mathcal{C}$ of measurable sets, there is a measurable set $E$ such that:

• $E$ essentially contains each element of $\mathcal{C}$; and
• Given any measurable set $F$ that essentially contains every element of $\mathcal{C}$, $F$ essentially contains $E$.

(This set $E$ is essentially unique, in that it essentially contains and is essentially contained in any other set with the same property; we call $E$ the essential union $\ess \bigcup \mathcal{C}$ of $\mathcal{C}$.)

We generalize to measures taking place in some space other than $[0,\infty]$: a $\mu$ is localizable if ${|\mu|}$ is, as long as $\mu$ has an absolute value (or total variation?) ${|\mu|}$ that takes values in $[0,\infty]$.

Of course, a set equipped with a localizable measure is a localizable measure space.

## Examples

Every $\sigma$-finite measure is localizable. (Since this includes so many examples, the theorems below are often stated for $\sigma$-finite measures.)

Any counting measure is localizable (but $\sigma$-finite only on a countable set).

## Properties

The following sheaf condition is fundamental: Given any cover $\mathcal{U}$ of $X$ (a localizable measure space) by measurable sets and a $\mathcal{U}$-indexed family $f$ of partial measurable functions (with $\dom f_A = A$), if always $f_A = f_B$ almost everywhere on $A \cap B$ (meaning that there is a full subset $E$ of $X$ such that $f_A = f_B$ on $A \cap B \cap E$), then there exists a (necessarily unique up to almost equality) measurable (and total) function $\ess \bigcup f$ such that always $\ess \bigcup f = f_A$ almost everywhere on $A$. (This is Theorem 213N in Fremlin. I don't know if it characterizes localizable measures.)

Slightly more generally, start with any family of partial measurable functions on $X$ and treat it as a cover of (any representative of) the essential union of its domains.

… other results such as the Radon–Nikodym theorem

These ideas are elaborated at measurable space Boolean toposes and Boolean valued model?s and forcing.

## Other localizable structures

Whether a semifinite measure $\mu$ is localizable depends only on which measurable sets are full (or null). Sometimes it is convenient to equip a measurable space with a $\delta$-filter of full sets (or a $\sigma$-ideal of null sets), without equipping it with the measure of any other set. A localizable measurable space is a measurable space so equipped, such that every collection of measurable sets has an essential union. (It is a theorem, I believe, that every localizable measurable space is capable of supporting a semifinite, hence localizable, measure with the same full/null sets.)

Knowing the full/null sets is also sufficient to define almost equality of measurable functions between measurable spaces; this gives us a category $Loc Meas$ of localizable measurable spaces. This category is dual to the category of commutative von Neumann algebras; hence an arbitrary von Neumann algebra may be viewed as a noncommutative localizable measurable space (in the sense of noncommutative geometry).

If $\mathcal{M}$ is the $\sigma$-algebra of measurable sets and $\mathcal{F}$ is the $\delta$-filter of full sets (or $\mathcal{N}$ is the $\sigma$-ideal of null sets), then we may form the quotient boolean algebra $\mathcal{M}/\mathcal{F}$ (or $\mathcal{M}/\mathcal{N}$) by identifying each full set with the entire space (or identifying each null set with the empty set). Then a measurable space is localizable iff this quotient is complete; this is the real point of the notion of localizability.

This boolean algebra is all the structure needed to specify a measure on the original space (at least one that is absolutely continuous in that it has at least the requisite full/null sets). We can now abstract away the underlying set and simply look at the complete boolean algebra. However, it is not true that every complete boolean algebra is capable of arising in this way from a measurable space. Those which do so arise may be called measurable locales, since a complete boolean algebra is a frame and measurable functions (up to almost equality) between the measurable spaces correspond to continuous maps between the frames, thought of as locales. (That is, $Loc Meas$ is equivalent to $Meas Loc$, a sort of pun.)

Without requiring localizability, the boolean algebra $\mathcal{M}/\mathcal{F}$ (or $\mathcal{M}/\mathcal{N}$) is called a measurable algebra?; equipped with a measure, we have a measure algebra?. Thus a measurable locale is the same thing as a localizable measurable algebra, and one may also speak of localizable measure algebras (with the category of these equivalent to the category localizable measure spaces, so long as morphisms in the latter are again only defined up to almost equality).

## In weak foundations

The axiom of choice is indispensable for the development above, as stated. (The reason is that one constantly makes choices among essentially equivalent measurable sets, or among almost equal measurable functions.) However, the category of localizable measurable spaces (and measurable functions up to almost equality) is (assuming choice) equivalent to the category of measurable locales, which may prove to be more tractable without choice, even in constructive mathematics. That said, nobody has worked out a constructive development of this yet. (In particular, identifying which boolean algebras we want is more difficult; perhaps surprisingly, they are still boolean, but they are no longer necessarily complete!)

## References

This giant treatise on all of measure theory is free (in both senses) online:

• D.H. Fremlin, Measure Theory, web

Last revised on May 3, 2019 at 16:21:53. See the history of this page for a list of all contributions to it.


text:
# Terminal symbol computer science

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Nonterminal complexity of programmed grammars conf, mcu 2001, lecture notes in computer science, vol păunon the number of non-terminal symbols in. A set of terminal symbols context-free grammars are strictly more there are languages that can be generated by a context-free grammar that cannot be. I'm programming a website and wanted to include the symbol of computer science, but i couldn't find the symbol somewhere does someone know where i can find it. Production (computer science) a production or production rule in computer science is a rewrite rule specifying a symbol substitution of terminal symbols that. I just ran across a screenshot of someone's terminal: awesome symbols and characters in a bash prompt computer science philosophy.

In computer science, terminal and nonterminal symbols are the lexical elements used in specifying the production rules constituting a formal grammar. The secret histories of those @#$%ing computer symbols between computer network addresses to separate the user from the terminal 2017 popular science. Compiler design spring 2011 computer science department terminal symbol in any sentential form derived from the grammar’s start symbol. Some notes on writing computer science publications for acm, ieee and elsevier in latex (this is a first version - trust it and die) comments and suggestions are. Computer science mcq questions and answers for practice , interview, competitive examination and entrance exam computer science mcq questions and answers specially. Computer science stack exchange is a question and answer can a cfg end have a non-terminal symbol in the middle lhs must be exactly 1 non-terminal. Only one flow line is used in conjunction with terminal symbol computer science sidebar classic flipcard introduction to flowchart. ## Programming essentials mcq multiple choice questions what is the standard terminal symbol for a flowchart computer science. Standard flowchart symbols and their usage a typical flowchart from older computer science textbooks may have the following kinds of symbols: terminal points. The recursive descent method of parsing is y v prasad and y n srikant department of computer science and corresponding to the terminal symbol a. In order to give the function symbols$f\in f$of a an element$a \in k$is terminal in$k$if for every$b \in k$there is a sigma-algebra (computer science. A device that enables you to communicate with a computer generally, a terminal is a combination of keyboard and display screen. Computer science stack exchange is a question and answer site for students, researchers and practitioners of computer now if$\epsilon\$ is a terminal symbol.

Computer science ss2 third term week 5 ss2 computer science third term: flow chart and symbols terminal symbol is used to denote logical beginning or end of a. What do some computer science look like glossary of symbols used in computer science organised alphabetically on symbolscom. What is computer terminal what does computer terminal mean computer terminal meaning - computer terminal definition - computer terminal explanation.

Terminal symbol computer science
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text:
# NAG Library Routine Document

## 1Purpose

x10ba_a1w_f creates a data object that can be used to pass data between procedure arguments (callbacks) of NAG AD Library computational routines and companion callbacks.

## 2Specification

Fortran Interface
 Subroutine x10ba_a1w_f (
 Integer, Intent (Inout) :: ifail Type (c_ptr), Intent (Out) :: cb_handle
extern "C" {
 void x10ba_a1w_f_ (void *&cb_handle, Integer &ifail)
}
 Declare PtrSafe Sub X10BAF Lib "libname" ( _ cb_handle As LongPtr, ifail As Long)

## 3Description

If a NAG AD Library routine contains a procedure argument (callback) which performs an operation on active variables, then that operation must also be differentiated. When the algorithmic computational mode is being used and the operation involves standard arithmetic operators and intrinsic functions then the differentiation is performed automatically. Similarly, even when in symbolic mode, algorithmic differentiation will be performed automatically for simple operators and functions used in supplied callbacks.
However, there are circumstances in which the algorithmic differentiation of a callback operation needs to be circumvented. Such a case would be where the symbolic differential of the operation is readily available and to be preferred to one calculated algorithmically. Another case is where the operation involves calls to, for example, NAG Library routines for which there is not yet a NAG AD Library equivalent routine. In such cases, algorithmic differentiation is circumvented by pushing the operations into a fixed interface companion callback; there can be several such companion callbacks. These companion callbacks have a single argument: a handle to a callback data object. x10ba_a1w_f creates this callback data object.
A callback data object created by x10ba_a1w_f is used to: pass data to the companion callback; set primal calculation values or derivatives; and, pass the location of the companion callback so that it may be called internally during adjoint evaluations.
Within a user-supplied procedure argument, the companion callbacks perform different tasks as requested: primal evaluation of output arguments; differentiation of output arguments with respect to real-valued input arguments; and, differentiation with respect to any user-supplied values (i.e., in the ruser array in c05ay_a1w_f).
Where algorithmic differentiation is to be circumvented with a supplied procedure argument, x10ba_a1w_f should be called to create a callback data object inside the supplied procedure. x10bd_a1w_f will return which mode of operation should be performed. A separate companion callback can be created for each mode of operation or the mode can be passed to a single companion callback to handle all three (potentially) cases.
Input data should be written to the callback data object using one of x10be_a1w_f, x10bf_a1w_f or x10bj_a1w_f depending on input data type. The companion callback will extract data from the callback data object, perform the required operations, and update the global data object with primal values or derivative increments.

None.

## 5Arguments

1:     $\mathbf{cb_handle}$ – Type (c_ptr)Output
On exit: will contain a handle to the callback data object.
2:     $\mathbf{ifail}$ – IntegerInput/Output
On entry: ifail must be set to $0$, . If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value  is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is $0$. When the value  is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

## 6Error Indicators and Warnings

If on entry ${\mathbf{ifail}}=0$ or $-1$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
${\mathbf{ifail}}=-99$
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

Not applicable.

## 8Parallelism and Performance

x10ba_a1w_f is not threaded in any implementation.

None.

## 10Example

The following example shows the procedure for calling a NAG AD Library routine (s01ba_a1w_f). x10aa_a1w_f is called to initialize the ad_handle; x10ac_a1w_f and x10ad_a1w_f are called to set and get the computational mode from the handle; and x10ab_a1w_f is called to destroy the handle.
 Language Source File Data Results Fortan x10ba_a1w_fe.f90 None x10ba_a1w_fe.r