def variance_metric(y_true, y_pred):
truth_v_tensor = K.expand_dims(y_true[:, :, 6], 2)
pred_v_tensor = K.expand_dims(y_pred[:, :, 6], 2)
return rmse_metric(truth_v_tensor, pred_v_tensor)
def mean_metric(y_true, y_pred):
truth_m_tensor = K.expand_dims(y_true[:, :, 5], 2)
pred_m_tensor = K.expand_dims(y_pred[:, :, 5], 2)
return rmse_metric(truth_m_tensor, pred_m_tensor)
def variance_metric(y_true, y_pred):
truth_v_tensor = K.expand_dims(y_true[:, :, 6], 2)
pred_v_tensor = K.expand_dims(y_pred[:, :, 6], 2)
return rmse_metric(truth_v_tensor, pred_v_tensor)
def mean_metric_multiclass_2(y_true, y_pred):
truth_m_tensor = K.expand_dims(y_true[:, :, 6], 2)
pred_m_tensor = K.expand_dims(y_pred[:, :, 6], 2)
return rmse_metric(truth_m_tensor, pred_m_tensor)
def mean_metric(y_true, y_pred):
truth_m_tensor = K.expand_dims(y_true[:, :, 5], 2)
pred_m_tensor = K.expand_dims(y_pred[:, :, 5], 2)
return rmse_metric(truth_m_tensor,pred_m_tensor)
