tf.keras.metrics.Precision

Class Precision

Defined in tensorflow/python/keras/metrics.py.

Computes the precision of the predictions with respect to the labels.

For example, if y_true is [0, 1, 1, 1] and y_pred is [1, 0, 1, 1] then the precision value is 2/(2+1) ie. 0.66. If the weights were specified as [0, 0, 1, 0] then the precision value would be 1.

The metric creates two local variables, true_positives and false_positives that are used to compute the precision. This value is ultimately returned as precision, an idempotent operation that simply divides true_positives by the sum of true_positives and false_positives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Usage:

m = tf.keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
print('Final result: ', m.result().numpy())  # Final result: 0.66

Usage with tf.keras API:

model = keras.models.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.Precision()])

__init__

__init__(
    thresholds=None,
    name=None,
    dtype=None
)

Creates a Precision instance.

Args:

  • thresholds: (Optional) Defaults to 0.5. A float value or a python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.
  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.

__new__

__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

Properties

activity_regularizer

Optional regularizer function for the output of this layer.

dtype

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:

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

Raises:

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

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.

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.

losses

Losses which are associated with this Layer.

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.

Returns:

A list of tensors.

name

non_trainable_variables

non_trainable_weights

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.

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.

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.

trainable_variables

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

tf.keras.metrics.Precision.__call__

__call__(
    *args,
    **kwargs
)

Accumulates statistics and then computes metric result value.

Args:

  • *args: * **kwargs: A mini-batch of inputs to the Metric, passed on to update_state().

Returns:

The metric value tensor.

tf.keras.metrics.Precision.__setattr__

__setattr__(
    name,
    value
)

Implement setattr(self, name, value).

tf.keras.metrics.Precision.apply

apply(
    inputs,
    *args,
    **kwargs
)

Apply the layer on a input.

This is an alias of self.__call__.

Arguments:

  • inputs: Input tensor(s).
  • *args: additional positional arguments to be passed to self.call.
  • **kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

tf.keras.metrics.Precision.build

build(input_shape)

Creates the variables of the layer (optional, 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.

This is typically used to create the weights of Layer subclasses.

Arguments:

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

tf.keras.metrics.Precision.compute_mask

compute_mask(
    inputs,
    mask=None
)

Computes an output mask tensor.

Arguments:

  • 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).

tf.keras.metrics.Precision.compute_output_shape

compute_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

Arguments:

  • 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.

tf.keras.metrics.Precision.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).

tf.keras.metrics.Precision.from_config

from_config(
    cls,
    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).

Arguments:

  • config: A Python dictionary, typically the output of get_config.

Returns:

A layer instance.

tf.keras.metrics.Precision.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).

Returns:

Python dictionary.

tf.keras.metrics.Precision.get_input_at

get_input_at(node_index)

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

Arguments:

  • 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 tensor (or list of tensors if the layer has multiple inputs).

Raises:

  • RuntimeError: If called in Eager mode.

tf.keras.metrics.Precision.get_input_mask_at

get_input_mask_at(node_index)

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

Arguments:

  • 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).

tf.keras.metrics.Precision.get_input_shape_at

get_input_shape_at(node_index)

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

Arguments:

  • 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.

tf.keras.metrics.Precision.get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors.

Returns:

List of loss tensors of the layer that depend on inputs.

Raises:

  • RuntimeError: If called in Eager mode.

tf.keras.metrics.Precision.get_output_at

get_output_at(node_index)

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

Arguments:

  • 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 tensor (or list of tensors if the layer has multiple outputs).

Raises:

  • RuntimeError: If called in Eager mode.

tf.keras.metrics.Precision.get_output_mask_at

get_output_mask_at(node_index)

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

Arguments:

  • 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).

tf.keras.metrics.Precision.get_output_shape_at

get_output_shape_at(node_index)

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

Arguments:

  • 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.

tf.keras.metrics.Precision.get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors.

Returns:

List of update ops of the layer that depend on inputs.

Raises:

  • RuntimeError: If called in Eager mode.

tf.keras.metrics.Precision.get_weights

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

tf.keras.metrics.Precision.reset_states

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

tf.keras.metrics.Precision.result

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

tf.keras.metrics.Precision.set_weights

set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

Arguments:

  • 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.

tf.keras.metrics.Precision.update_state

update_state(
    y_true,
    y_pred,
    sample_weight=None
)

Accumulates true positive and false positive statistics.

Args:

  • y_true: The ground truth values.
  • y_pred: The predicted values.
  • sample_weight: Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns:

Update op.