def linear_model_fn_with_model_fn_ops(features, labels, mode): """Same as linear_model_fn, but returns `ModelFnOps`.""" assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, model_fn.ModeKeys.INFER) prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1) return model_fn.ModelFnOps( mode=mode, predictions=prediction, loss=loss, train_op=train_op)
def linear_model_fn_with_model_fn_ops(features, labels, mode): """Same as linear_model_fn, but returns `ModelFnOps`.""" assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, model_fn.ModeKeys.INFER) prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1) return model_fn.ModelFnOps( mode=mode, predictions=prediction, loss=loss, train_op=train_op)
def linear_model_fn(features, labels, mode): features = extract(features, 'input') labels = extract(labels, 'labels') assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, model_fn.ModeKeys.INFER) if isinstance(features, dict): (_, features), = features.items() prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1) return prediction, loss, train_op
def linear_model_fn(features, labels, mode): features = extract(features, 'input') labels = extract(labels, 'labels') assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, model_fn.ModeKeys.INFER) if isinstance(features, dict): (_, features), = features.items() prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=0.1) return prediction, loss, train_op
def linear_model_params_fn(features, labels, mode, params): features = extract(features, 'input') labels = extract(labels, 'labels') assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, model_fn.ModeKeys.INFER) prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss(loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=params['learning_rate']) return prediction, loss, train_op
def linear_model_params_fn(features, labels, mode, params): features = extract(features, 'input') labels = extract(labels, 'labels') assert mode in (model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, model_fn.ModeKeys.INFER) prediction, loss = (models.linear_regression_zero_init(features, labels)) train_op = optimizers.optimize_loss( loss, variables.get_global_step(), optimizer='Adagrad', learning_rate=params['learning_rate']) return prediction, loss, train_op