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Random Forest learning algorithm.
Inherits From: CoreModel
tfdf.keras.wrappers.RandomForestModel(
*args, **kargs
)
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 is more features than training examples.
It is probably the most wellknown 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())
Attributes  

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 garbagecollected.

verbose

If true, displays information about the training. 
hyperparameter_template

Override the default value of the hyperparameters.
If None (default) the default parameters of the library are used. If set,
default_hyperparameter_template refers to one of the following
preconfigured hyperparameter sets. Those sets outperforms the default
hyperparameters (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 hyperparameter can change in between
releases (not recommended for training in production).

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 multithreading 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_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.

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 multiclass 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 : Onehot 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 Outofvocabulary 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 Outofbag evaluation (then available in the summary and model inspector). This evaluation is a cheap alternative to crossvalidation evaluation. Default: True. 
compute_oob_variable_importances

If true, compute the Outofbag 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 "leafwise growth". See "Bestfirst 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.

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_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 nondeterministic. 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 mostfrequentitem (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 mostfrequentitem (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_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. 
sorting_strategy

How are sorted the numerical features in order to find
the splits

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 ZScore
normalization.MIN_MAX : Normalize the feature by the range (i.e. maxmin) 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".

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 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 Layers often perform certain internal computations in higher precision when

distribute_strategy

The tf.distribute.Strategy this model was created under.

dtype

The dtype of the layer weights.
This is equivalent to 
dtype_policy

The dtype policy associated with this layer.
This is an instance of a 
dynamic

Whether the layer is dynamic (eageronly); 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
Now, if you try to call the layer on an input that isn't rank 4
(for instance, an input of shape
Input checks that can be specified via
For more information, see 
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

metrics

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

metrics_names

Returns the model's display labels for all outputs.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all nontrainable weights tracked by this layer.
Nontrainable weights are not updated during training. They are expected
to be updated manually in 
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 submodules.
Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

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(
losses, **kwargs
)
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 Input
s. 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
zeroargument 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 zeroargument callables which create a loss tensor. 
**kwargs

Additional keyword arguments for backward compatibility. Accepted values: inputs  Deprecated, will be automatically inferred. 
add_metric
add_metric(
value, name=None, **kwargs
)
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 Input
s. 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
build(
input_shape
)
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

In each of these cases, the user should build their model by calling it on real tensor data. 
call
call(
inputs, training=False
)
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
compile(
metrics=None
)
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
compute_mask(
inputs, mask=None
)
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
compute_output_shape(
input_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_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
evaluate(
*args, **kwargs
)
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
fit(
x=None, y=None, callbacks=None, **kwargs
) > tf.keras.callbacks.History
Trains the model.
The following dataset formats are supported:
"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.
"x" is a tensor, list of tensors or dictionary of tensors containing the input features. "y" is a tensor.
"x" is a numpyarray, list of numpyarrays or dictionary of numpyarrays containing the input features. "y" is a numpyarray.
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
@classmethod
from_config( config, custom_objects=None )
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
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
get_layer(
name=None, index=None
)
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 (bottomup).
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
get_weights()
Retrieves the weights of the model.
Returns  

A flat list of Numpy arrays. 
load_weights
load_weights(
filepath, by_name=False, skip_mismatch=False, options=None
)
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 finetuning or transferlearning 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 userdefined classes inheriting from
tf.keras.Model
: HDF5 loads based on a flattened list of weights, while the
TensorFlow format loads based on the objectlocal 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 userdefined classes inheriting from Model , immediately if it is
already built).
When loading weights in HDF5 format, returns 
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
make_inspector() > tfdf.inspector.AbstractInspector
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
make_predict_function()
Prediction of the model (!= evaluation).
make_test_function
make_test_function()
Predictions for evaluation.
make_train_function
make_train_function(
force=False
)
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
@staticmethod
predefined_hyperparameters() > List[
tfdf.keras.core.HyperParameterTemplate
]
Returns a better than default set of hyperparameters.
They can be used directly with the hyperparameter_template
argument of the
model constructor.
These hyperparameters outperforms the default hyperparameters (either generally or in specific scenarios). Like default hyperparameters, existing predefined hyperparameters cannot change.
predict
predict(
x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10,
workers=1, use_multiprocessing=False
)
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:

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
processbased threading. If unspecified, workers will default
to 1.

use_multiprocessing

Boolean. Used for generator or
keras.utils.Sequence input only. If True , use processbased
threading. If unspecified, use_multiprocessing will default to
False . Note that because this implementation relies on
multiprocessing, you should not pass nonpicklable arguments to
the generator as they can't be passed easily to children processes.

See the discussion of Unpacking behavior for iteratorlike 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
predict_on_batch(
x
)
Returns predictions for a single batch of samples.
Args  

x

Input data. It could be:

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
predict_step(
data
)
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 Tensor s.

Returns  

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

reset_metrics
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
reset_states()
save
save(
filepath: str,
overwrite: Optional[bool] = True,
**kwargs
)
Saves the model as a TensorFlow SavedModel.
The exported SavedModel contains a standalone Yggdrasil Decision Forests model in the "assets" subdirectory. 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
save_spec(
dynamic_batch=True
)
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
save_weights(
filepath, overwrite=True, save_format=None, options=None
)
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
namedlayer.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.
 For every such layer group, a group attribute
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 userdefined 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
set_weights(
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
summary(
line_length=None, positions=None, print_fn=None
)
Shows information about the model.
test_on_batch
test_on_batch(
x, y=None, sample_weight=None, reset_metrics=True, return_dict=False
)
Test the model on a single batch of samples.
Args  

x

Input data. It could be:

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 userprovided arguments. 
test_step
test_step(
data
)
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 Tensor s.

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
to_json(
**kwargs
)
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
to_yaml(
**kwargs
)
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
train_on_batch(
x, y=None, sample_weight=None, class_weight=None, reset_metrics=True,
return_dict=False
)
Runs a single gradient update on a single batch of data.
Args  

x

Input data. It could be:

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 underrepresented 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 userprovided arguments. 
train_step
train_step(
data
)
Collects training examples.
with_name_scope
@classmethod
with_name_scope( method )
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.Variable
s and tf.Tensor
s 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
yggdrasil_model_path_tensor() > Optional[tf.Tensor]
Gets the path to yggdrasil model, if available.
The effective path can be obtained with:
yggdrasil_model_path_tensor().numpy().decode("utf8")
Returns  

Path to the Yggdrasil model. 
__call__
__call__(
*args, **kwargs
)
Wraps call
, applying pre and postprocessing 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 thecall
is meant for training or inference.mask
: Boolean input mask.
 If the layer's
call
method takes amask
argument (as some Keras layers do), its default value will be set to the mask generated forinputs
by the previous layer (ifinput
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.
