fmeasure_metric.py

import keras.backend as K

def binary_accuracy(y_true, y_pred, threshold=0.5):
    if threshold != 0.5:
        threshold = K.cast(threshold, y_pred.dtype)
        y_pred = K.cast(y_pred > threshold, y_pred.dtype)
    return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)

def precision(y_true, y_pred):
    # Calculates the precision
    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):
    # Calculates the recall
    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 fbeta_score(y_true, y_pred, beta=1):
    # Calculates the F score, the weighted harmonic mean of precision and recall.
    if beta < 0:
        raise ValueError('The lowest choosable beta is zero (only precision).')
    
    # If there are no true positives, fix the F score at 0 like sklearn.
    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
        return 0
    p = precision(y_true, y_pred)
    r = recall(y_true, y_pred)
    bb = beta ** 2
    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
    return fbeta_score

def fmeasure(y_true, y_pred):
    # Calculates the f-measure, the harmonic mean of precision and recall.
    return fbeta_score(y_true, y_pred, beta=1)
    
earlystop = EarlyStopping(monitor='val_fmeasure', patience=4, verbose=0, mode='max')   
    
model.compile(optimizer = 'adam',
              loss='binary_crossentropy',
              metrics=['accuracy',fmeasure,recall,precision])