Metrics removed from Keras in 2.0.

keras_metrics.py

"""Keras 1.0 metrics.

This file contains the precision, recall, and f1_score metrics which were
removed from Keras by commit: a56b1a55182acf061b1eb2e2c86b48193a0e88f7
"""
from keras import backend as K


def precision(y_true, y_pred):	
    """Precision metric.	

    Only computes a batch-wise average of precision. Computes the precision, a
    metric for multi-label classification of how many selected items are
    relevant.
    """	
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))	
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))	
    precision = true_positives / (predicted_positives + K.epsilon())	
    return precision

def recall(y_true, y_pred):	
    """Recall metric.	

    Only computes a batch-wise average of recall. Computes the recall, a metric
    for multi-label classification of how many relevant items are selected.	
    """	
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))	
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))	
    recall = true_positives / (possible_positives + K.epsilon())	
    return recall

def f1_score(y_true, y_pred):
    """Computes the F1 Score

    Only computes a batch-wise average of recall. Computes the recall, a metric
    for multi-label classification of how many relevant items are selected.	
    """
    p = precision(y_true, y_pred)
    r = recall(y_true, y_pred)
    return (2 * p * r) / (p + r + K.epsilon())

`from sklearn.metrics import auc, precision_recall_curve
import keras.backend as K

def precision(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true[:,:,1] * y_pred[:,:,1], 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred[:,:,1], 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision
def auc_pcr_1(y_true, y_pred):

    precision, recall, _ = precision_recall_curve(y_true[:,:,1] ,y_pred[:,:,1])
    area_under_curve_p_r = auc(recall, precision)
    return auc_1 `

@dgrahn no, they are sklearn functions

@FrancescaAlf Ah -- that's what I meant!. So those methods accept numpy matrices, not tensors. If you are using TensorFlow as the backend, you could use tf.keras.metrics.AUC and tf.keras.metrics.PrecisionAtRecall. If not, you might have to implement those functions with tensors.

dgrahn Oh, ok. Thanks for your help