tf.keras.metrics.Precision     View source on GitHub    Computes the precision of the predictions with respect to the labels. Inherits From: Metric, Layer, Module  View aliases  Main aliases 
tf.metrics.Precision Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.metrics.Precision  
tf.keras.metrics.Precision(
    thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)
 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. If top_k is set, we'll calculate precision as how often on average a class among the top-k classes with the highest predicted values of a batch entry is correct and can be found in the label for that entry. If class_id is specified, we calculate precision by considering only the entries in the batch for which class_id is above the threshold and/or in the top-k highest predictions, and computing the fraction of them for which class_id is indeed a correct label.
 


 Args
  thresholds   (Optional) 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. If neither thresholds nor top_k are set, the default is to calculate precision with thresholds=0.5.  
  top_k   (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating precision.  
  class_id   (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.  
  name   (Optional) string name of the metric instance.  
  dtype   (Optional) data type of the metric result.    Standalone usage: 
m = tf.keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
m.result().numpy()
0.6666667
 
m.reset_states()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
m.result().numpy()
1.0
 
# With top_k=2, it will calculate precision over y_true[:2] and y_pred[:2]
m = tf.keras.metrics.Precision(top_k=2)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result().numpy()
0.0
 
# With top_k=4, it will calculate precision over y_true[:4] and y_pred[:4]
m = tf.keras.metrics.Precision(top_k=4)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result().numpy()
0.5
 Usage with compile() API: model.compile(optimizer='sgd',
              loss='mse',
              metrics=[tf.keras.metrics.Precision()])
 Methods reset_states View source 
reset_states()
 Resets all of the metric state variables. This function is called between epochs/steps, when a metric is evaluated during training. result View source 
result()
 Computes and returns the metric value tensor. Result computation is an idempotent operation that simply calculates the metric value using the state variables. update_state View source 
update_state(
    y_true, y_pred, sample_weight=None
)
 Accumulates true positive and false positive statistics.
 


 Args
  y_true   The ground truth values, with the same dimensions as y_pred. Will be cast to bool.  
  y_pred   The predicted values. Each element must be in the range [0, 1].  
  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.