F1 Score Metrics removed from Keras in 2.0. Get True Positive, False Positive, True Negative, False Negative, Precision, Recall, Accuracy

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())




# use it as per below mentioned code...
# model.compile(loss='categorical_crossentropy',
#                               optimizer='adam',
#                               metrics=['accuracy', f1_score])

tpfptnfn.py

def tpfptnfn(cls, y_true, y_predicted):
    tp = 0
    fp = 0
    tn = 0
    fn = 0
    for i in range(len(y_predicted)):
        if y_true[i] == y_predicted[i] == 1:
            tp += 1
        if y_predicted[i] == 1 and y_true[i] != y_predicted[i]:
            fp += 1
        if y_true[i] == y_predicted[i] == 0:
            tn += 1
        if y_predicted[i] == 0 and y_true[i] != y_predicted[i]:
            fn += 1
    return tp, fp, tn, fn