def custom_metric_mean_absolute_error(y_true, y_pred):
    return K.abs(y_pred - y_true)

class LossHistory(keras.callbacks.Callback):
    def __init__(self, n_labels):
        super(LossHistory, self).__init__()
        self.n_labels = n_labels

    def on_batch_end(self, logs={}):
         for i in xrange(self.n_labels):
            loss_name = 'custom_loss_neuron_', i
            cm_mean_absolute_error = logs.get('custom_metric_mean_absolute_error')
            logs[loss_name] = cm_mean_absolute_error[i]

model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_squared_error', custom_metric_mean_absolute_error])

callbacks = []
history_callback = LossHistory(n_labels)
callbacks.append(history_callback)

history = model.fit_generator(generate_batches_from_hdf5_file(...), ..., callbacks=callbacks)

#### result:
#logs[loss_name] = cm_mean_absolute_error[i]
#IndexError: invalid index to scalar variable.