Keras custom metric iteration

Question

I\'m pretty new to Keras and I\'m trying to define my own metric. It calculates concordance index which is a measure for regression problems.

def cindex_scor         


        

Answer 1

I used @Pedia code for 3D-tensors to compute Rank loss for multi label classification:

def rloss(y_true, y_pred):
    g = tf.subtract(tf.expand_dims(y_pred[1], -1), y_pred[1])
    g = tf.cast(g == 0.0, tf.float32) * 0.5 + tf.cast(g > 0.0, tf.float32)
    f = tf.subtract(tf.expand_dims(y_true[1], -1), y_true[1]) > 0.0
    f = tf.matrix_band_part(tf.cast(f, tf.float32), -1, 0)
    g = tf.reduce_sum(tf.multiply(g, f))
    f = tf.reduce_sum(f)
return tf.where(tf.equal(g, 0), 0.0, g/f)


model = Sequential()
model.add(Dense(label_length, activation='relu'))
model.add(Dense(label_length, activation='relu'))
model.add(Dense(label_length, activation='sigmoid'))
model.summary()


adgard = optimizers.Adagrad(lr=0.01, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy',
          optimizer=adgard, metrics=[rloss])
model.fit(X_train, y_train,
      batch_size=batch_size,
      epochs=n_epoch,
      validation_data=(X_test, y_test),
      shuffle=True)