F1 metrics.py

from keras import backend as K

def recall_m(y_true, y_pred):   # '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 precision_m(y_true, y_pred):   # 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 f1_m(y_true, y_pred):    # F1 score
    precision = precision_m(y_true, y_pred)
    recall = recall_m(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))


############### Predict and Run results #####################

predicted_label = Watteffnet36.predict(np.asarray(new_X_test))

print(recall_m(onehot_encodedtest.astype('float32'),predicted_label.astype('float32')))
print(precision_m(onehot_encodedtest.astype('float32'),predicted_label.astype('float32')))
print(f1_m(onehot_encodedtest.astype('float32'),predicted_label.astype('float32')))