Labeler Utils - Data Profiler v0.10.4

dataprofiler.labelers.labeler_utils.f1_report_dict_to_str(f1_report: dict, label_names: list) → str¶

Return the report string from the f1_report dict.

Example Output:: precision recall f1-score support

class 0 0.00 0.00 0.00 1 class 1 1.00 0.67 0.80 3

micro avg 0.67 0.50 0.57 4 macro avg 0.50 0.33 0.40 4

weighted avg 0.75 0.50 0.60 4

Note: this is generally taken from the classification_report function inside sklearn. :param f1_report: f1 report dictionary from sklearn :type f1_report: dict :param label_names: names of labels included in the report :type label_names: list(str) :return: string representing f1_report printout :rtype: str

dataprofiler.labelers.labeler_utils.evaluate_accuracy(predicted_entities_in_index: list[list[int]], true_entities_in_index: list[list[int]], num_labels: int, entity_rev_dict: dict[int, str], verbose: bool = True, omitted_labels: tuple[str, ...] = ('PAD', 'UNKNOWN'), confusion_matrix_file: str | None = None) → tuple[float, dict]¶

Evaluate accuracy from comparing predicted labels with true labels.

Parameters

predicted_entities_in_index (list(array(int))) – predicted encoded labels for input sentences
true_entities_in_index (list(array(int))) – true encoded labels for input sentences
entity_rev_dict (dict([index, entity])) – dictionary to convert indices to entities
verbose (boolean) – print additional information for debugging
omitted_labels (list() of text labels) – labels to omit from the accuracy evaluation
confusion_matrix_file (str) – File name (and dir) for confusion matrix

:return : f1-score :rtype: float

dataprofiler.labelers.labeler_utils.get_tf_layer_index_from_name(model: tf.keras.Model, layer_name: str) → int | None¶

Return the index of the layer given the layer name within a tf model.

Parameters

model – tf keras model to search
layer_name – name of the layer to find

Returns

layer index if it exists or None

dataprofiler.labelers.labeler_utils.hide_tf_logger_warnings() → None¶

Filter out a set of warnings from the tf logger.

dataprofiler.labelers.labeler_utils.protected_register_keras_serializable(package: str = 'Custom', name: str | None = None) → Callable¶

Protect against already registered keras serializable layers.

Ensures that if it was already registered, it will not try to register it again.

class dataprofiler.labelers.labeler_utils.FBetaScore(*args, **kwargs)¶

Bases: keras.src.metrics.base_metric.Metric

Computes F-Beta score.

Adapted and slightly modified from https://github.com/tensorflow/addons/blob/v0.12.0/tensorflow_addons/metrics/f_scores.py#L211-L283

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # https://github.com/tensorflow/addons/blob/v0.12.0/LICENSE # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================

It is the weighted harmonic mean of precision and recall. Output range is [0, 1]. Works for both multi-class and multi-label classification. $$ F_{beta} = (1 + beta^2) * frac{textrm{precision} * textrm{precision}}{(beta^2 cdot textrm{precision}) + textrm{recall}} $$ :param num_classes: Number of unique classes in the dataset. :param average: Type of averaging to be performed on data.

Acceptable values are None, micro, macro and weighted. Default value is None.

Parameters

beta – Determines the weight of precision and recall in harmonic mean. Determines the weight given to the precision and recall. Default value is 1.
threshold – Elements of y_pred greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax is converted to 1, and the rest 0.
name – (Optional) String name of the metric instance.
dtype – (Optional) Data type of the metric result.

Returns

float.

Return type

F-Beta Score

Initialize FBetaScore class.

update_state(y_true: tf.Tensor, y_pred: tf.Tensor, sample_weight: tf.Tensor | None = None) → None¶: Update state.

result() → tensorflow.python.framework.ops.Tensor¶: Return f1 score.

get_config() → dict¶: Return the serializable config of the metric.

reset_state() → None¶: Reset state.

property activity_regularizer¶: Optional regularizer function for the output of this layer.

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:

```python class MyLayer(tf.keras.layers.Layer):

def call(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

The same code works in distributed training: the input to add_loss() is treated like a regularization loss and averaged across replicas by the training loop (both built-in Model.fit() and compliant custom training loops).

The add_loss 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:

`python 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:

`python 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)) `

Parameters

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(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.

```python 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().

`python 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') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`python 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') `

Parameters

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.

add_update(updates)¶

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) 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.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.

add_variable(*args, **kwargs)¶: Deprecated, do NOT use! Alias for add_weight.

add_weight(name, shape=(), aggregation=VariableAggregationV2.SUM, synchronization=VariableSynchronization.ON_READ, initializer=None, dtype=None)¶: Adds state variable. Only for use by subclasses.

build(input_shape)¶

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

build_from_config(config)¶

Builds the layer’s states with the supplied config dict.

By default, this method calls the build(config[“input_shape”]) method, which creates weights based on the layer’s input shape in the supplied config. If your config contains other information needed to load the layer’s state, you should override this method.

Parameters: config – Dict containing the input shape associated with this layer.

call(inputs, *args, **kwargs)¶

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Parameters

inputs –
Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.
- NumPy array or Python scalar values in inputs get cast as tensors.
- Keras mask metadata is only collected from inputs.
- Layers are built (build(input_shape) method) using shape info from inputs only.
- input_spec compatibility is only checked against inputs.
- Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
- The SavedModel input specification is generated using inputs only.
- Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs –
Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.
- mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns

A tensor or list/tuple of tensors.

property 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.

Returns: The layer’s compute dtype.

compute_mask(inputs, mask=None)¶

Computes an output mask tensor.

Parameters

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(input_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.

Parameters: 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.

compute_output_signature(input_signature)¶

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters

input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns

Single TensorSpec or nested structure of TensorSpec objects,: describing how the layer would transform the provided input.

Raises

TypeError – If input_signature contains a non-TensorSpec object.

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).

property 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.

property dtype_policy¶

The dtype policy associated with this layer.

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

property dynamic¶: Whether the layer is dynamic (eager-only); set in the constructor.

finalize_state()¶

Finalizes the layers state after updating layer weights.

This function can be subclassed in a layer and will be called after updating a layer weights. It can be overridden to finalize any additional layer state after a weight update.

This function will be called after weights of a layer have been restored from a loaded model.

classmethod from_config(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).

Parameters: config – A Python dictionary, typically the output of get_config.
Returns: A layer instance.

get_build_config()¶

Returns a dictionary with the layer’s input shape.

This method returns a config dict that can be used by build_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you’re writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.

Returns: A dict containing the input shape associated with the layer.

get_input_at(node_index)¶

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first input node of the layer.
Returns: A tensor (or list of tensors if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_input_mask_at(node_index)¶

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)¶

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_output_at(node_index)¶

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first output node of the layer.
Returns: A tensor (or list of tensors if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_output_mask_at(node_index)¶

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)¶

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_weights()¶

Returns the current weights of the layer, as NumPy arrays.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of NumPy arrays, which can in turn be used to load state into similarly parameterized layers.

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)]

Returns: Weights values as a list of NumPy arrays.

property inbound_nodes¶: Return Functional API nodes upstream of this layer.

property 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.

Returns

Input tensor or list of input tensors.

Raises

RuntimeError – If called in Eager mode.
AttributeError – If no inbound nodes are found.

property input_mask¶

Retrieves the input mask tensor(s) of a layer.

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

Returns

Input mask tensor (potentially None) or list of input mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property input_shape¶

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises

AttributeError – if the layer has no defined input_shape.
RuntimeError – if called in Eager mode.

property 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__():

`python 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.

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.

load_own_variables(store)¶

Loads the state of the layer.

You can override this method to take full control of how the state of the layer is loaded upon calling keras.models.load_model().

Parameters: store – Dict from which the state of the model will be loaded.

property 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.

Examples:

>>> 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>]

Returns: A list of tensors.

merge_state(metrics)¶

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric’s weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric’s states could be combined as follows:

>>> m1 = tf.keras.metrics.Accuracy()
>>> _ = m1.update_state([[1], [2]], [[0], [2]])

>>> m2 = tf.keras.metrics.Accuracy()
>>> _ = m2.update_state([[3], [4]], [[3], [4]])

>>> m2.merge_state([m1])
>>> m2.result().numpy()
0.75

Parameters: metrics – an iterable of metrics. The metrics must have compatible state.
Raises: ValueError – If the provided iterable does not contain metrics matching the metric’s required specifications.

property metrics¶

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']

Returns: A list of Metric objects.

property name¶: Name of the layer (string), set in the constructor.

property name_scope¶: Returns a tf.name_scope instance for this class.

property non_trainable_variables¶

Sequence of non-trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property 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().

Returns: A list of non-trainable variables.

property outbound_nodes¶: Return Functional API nodes downstream of this layer.

property 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.

Returns

Output tensor or list of output tensors.

Raises

AttributeError – if the layer is connected to more than one incoming layers.
RuntimeError – if called in Eager mode.

property output_mask¶

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

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

Returns

Output mask tensor (potentially None) or list of output mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property output_shape¶

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises

AttributeError – if the layer has no defined output shape.
RuntimeError – if called in Eager mode.

reset_states()¶

save_own_variables(store)¶

Saves the state of the layer.

You can override this method to take full control of how the state of the layer is saved upon calling model.save().

Parameters: store – Dict where the state of the model will be saved.

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)]

Parameters: 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.

property stateful¶

property 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

Returns: A sequence of all submodules.

property supports_masking¶: Whether this layer supports computing a mask using compute_mask.

property trainable¶

property trainable_variables¶

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property trainable_weights¶

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.

property updates¶

property variable_dtype¶: Alias of Layer.dtype, the dtype of the weights.

property variables¶

Returns the list of all layer variables/weights.

Alias of self.weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of variables.

property weights¶

Returns the list of all layer variables/weights.

Returns: A list of variables.

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)>

Parameters: method – The method to wrap.
Returns: The original method wrapped such that it enters the module’s name scope.

class dataprofiler.labelers.labeler_utils.F1Score(*args, **kwargs)¶

Bases: dataprofiler.labelers.labeler_utils.FBetaScore

Computes F-1 Score.

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # https://github.com/tensorflow/addons/blob/v0.12.0/LICENSE # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================

It is the harmonic mean of precision and recall. Output range is [0, 1]. Works for both multi-class and multi-label classification. $$ F_1 = 2 cdot frac{textrm{precision} cdot textrm{recall}}{textrm{precision} + textrm{recall}} $$ :param num_classes: Number of unique classes in the dataset. :param average: Type of averaging to be performed on data.

Acceptable values are None, micro, macro and weighted. Default value is None.

Parameters

threshold – Elements of y_pred above threshold are considered to be 1, and the rest 0. If threshold is None, the argmax is converted to 1, and the rest 0.
name – (Optional) String name of the metric instance.
dtype – (Optional) Data type of the metric result.

Returns

float.

Return type

F-1 Score

Initialize F1Score object.

property activity_regularizer¶: Optional regularizer function for the output of this layer.

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:

```python class MyLayer(tf.keras.layers.Layer):

def call(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

The same code works in distributed training: the input to add_loss() is treated like a regularization loss and averaged across replicas by the training loop (both built-in Model.fit() and compliant custom training loops).

The add_loss 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:

`python 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:

`python 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)) `

Parameters

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(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.

```python 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().

`python 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') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`python 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') `

Parameters

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.

add_update(updates)¶

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) 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.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters: updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.

add_variable(*args, **kwargs)¶: Deprecated, do NOT use! Alias for add_weight.

add_weight(name, shape=(), aggregation=VariableAggregationV2.SUM, synchronization=VariableSynchronization.ON_READ, initializer=None, dtype=None)¶: Adds state variable. Only for use by subclasses.

build(input_shape)¶

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

build_from_config(config)¶

Builds the layer’s states with the supplied config dict.

By default, this method calls the build(config[“input_shape”]) method, which creates weights based on the layer’s input shape in the supplied config. If your config contains other information needed to load the layer’s state, you should override this method.

Parameters: config – Dict containing the input shape associated with this layer.

call(inputs, *args, **kwargs)¶

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Parameters

inputs –
Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.
- NumPy array or Python scalar values in inputs get cast as tensors.
- Keras mask metadata is only collected from inputs.
- Layers are built (build(input_shape) method) using shape info from inputs only.
- input_spec compatibility is only checked against inputs.
- Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
- The SavedModel input specification is generated using inputs only.
- Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs –
Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.
- mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns

A tensor or list/tuple of tensors.

property 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.

Returns: The layer’s compute dtype.

compute_mask(inputs, mask=None)¶

Computes an output mask tensor.

Parameters

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(input_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.

Parameters: 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.

compute_output_signature(input_signature)¶

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters

input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns

Single TensorSpec or nested structure of TensorSpec objects,: describing how the layer would transform the provided input.

Raises

TypeError – If input_signature contains a non-TensorSpec object.

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).

property 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.

property dtype_policy¶

The dtype policy associated with this layer.

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

property dynamic¶: Whether the layer is dynamic (eager-only); set in the constructor.

finalize_state()¶

Finalizes the layers state after updating layer weights.

This function can be subclassed in a layer and will be called after updating a layer weights. It can be overridden to finalize any additional layer state after a weight update.

This function will be called after weights of a layer have been restored from a loaded model.

classmethod from_config(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).

Parameters: config – A Python dictionary, typically the output of get_config.
Returns: A layer instance.

get_build_config()¶

Returns a dictionary with the layer’s input shape.

This method returns a config dict that can be used by build_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you’re writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.

Returns: A dict containing the input shape associated with the layer.

get_input_at(node_index)¶

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first input node of the layer.
Returns: A tensor (or list of tensors if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_input_mask_at(node_index)¶

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)¶

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_output_at(node_index)¶

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first output node of the layer.
Returns: A tensor (or list of tensors if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_output_mask_at(node_index)¶

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)¶

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_weights()¶

Returns the current weights of the layer, as NumPy arrays.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of NumPy arrays, which can in turn be used to load state into similarly parameterized layers.

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)]

Returns: Weights values as a list of NumPy arrays.

property inbound_nodes¶: Return Functional API nodes upstream of this layer.

property 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.

Returns

Input tensor or list of input tensors.

Raises

RuntimeError – If called in Eager mode.
AttributeError – If no inbound nodes are found.

property input_mask¶

Retrieves the input mask tensor(s) of a layer.

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

Returns

Input mask tensor (potentially None) or list of input mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property input_shape¶

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises

AttributeError – if the layer has no defined input_shape.
RuntimeError – if called in Eager mode.

property 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__():

`python 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.

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.

load_own_variables(store)¶

Loads the state of the layer.

You can override this method to take full control of how the state of the layer is loaded upon calling keras.models.load_model().

Parameters: store – Dict from which the state of the model will be loaded.

property 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.

Examples:

>>> 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>]

Returns: A list of tensors.

merge_state(metrics)¶

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric’s weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric’s states could be combined as follows:

>>> m1 = tf.keras.metrics.Accuracy()
>>> _ = m1.update_state([[1], [2]], [[0], [2]])

>>> m2 = tf.keras.metrics.Accuracy()
>>> _ = m2.update_state([[3], [4]], [[3], [4]])

>>> m2.merge_state([m1])
>>> m2.result().numpy()
0.75

Parameters: metrics – an iterable of metrics. The metrics must have compatible state.
Raises: ValueError – If the provided iterable does not contain metrics matching the metric’s required specifications.

property metrics¶

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']

Returns: A list of Metric objects.

property name¶: Name of the layer (string), set in the constructor.

property name_scope¶: Returns a tf.name_scope instance for this class.

property non_trainable_variables¶

Sequence of non-trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property 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().

Returns: A list of non-trainable variables.

property outbound_nodes¶: Return Functional API nodes downstream of this layer.

property 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.

Returns

Output tensor or list of output tensors.

Raises

AttributeError – if the layer is connected to more than one incoming layers.
RuntimeError – if called in Eager mode.

property output_mask¶

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

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

Returns

Output mask tensor (potentially None) or list of output mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property output_shape¶

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises

AttributeError – if the layer has no defined output shape.
RuntimeError – if called in Eager mode.

reset_state() → None¶: Reset state.

reset_states()¶

result() → tensorflow.python.framework.ops.Tensor¶: Return f1 score.

save_own_variables(store)¶

Saves the state of the layer.

You can override this method to take full control of how the state of the layer is saved upon calling model.save().

Parameters: store – Dict where the state of the model will be saved.

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)]

Parameters: 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.

property stateful¶

property 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

Returns: A sequence of all submodules.

property supports_masking¶: Whether this layer supports computing a mask using compute_mask.

property trainable¶

property trainable_variables¶

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property trainable_weights¶

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns: A list of trainable variables.

update_state(y_true: tf.Tensor, y_pred: tf.Tensor, sample_weight: tf.Tensor | None = None) → None¶: Update state.

property updates¶

property variable_dtype¶: Alias of Layer.dtype, the dtype of the weights.

property variables¶

Returns the list of all layer variables/weights.

Alias of self.weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of variables.

property weights¶

Returns the list of all layer variables/weights.

Returns: A list of variables.

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)>

Parameters: method – The method to wrap.
Returns: The original method wrapped such that it enters the module’s name scope.

get_config() → dict¶: Get configuration.

Labeler Utils¶