自定义损失函数如何除了y_true, y_pred,如何添加自己需要的参数呢？
来源：3-5 实战自定义损失函数与DenseLayer回顾
BloodHound_swh
2019-06-18
例如
def rmse(matrix_true, matrix_pre, BI_train=tf_UIBI_train):
loss = tf.reduce_sum(tf.square(tf.multiply(matrix_pre, BI_train) - tf.multiply(matrix_true, BI_train)))
return loss
我希望BI_train的值是我之前定义好的变量tf_UIBI_train。但是这样写报错
AssertionError Traceback (most recent call last)
in ()
23
24 model.summary()
---> 25 model.compile(loss=rmse, optimizer=op)
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
454 self._setattr_tracking = False # pylint: disable=protected-access
455 try:
--> 456 result = method(self, *args, **kwargs)
457 finally:
458 self._setattr_tracking = previous_value # pylint: disable=protected-access
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
428 # loss_weight_2 * output_2_loss_fn(...) +
429 # layer losses.
--> 430 self.total_loss = self._prepare_total_loss(skip_target_indices, masks)
431
432 # Functions for train, test and predict will
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\engine\training.py in _prepare_total_loss(self, skip_target_indices, masks)
1684 loss_fn.reduction = losses_utils.ReductionV2.NONE
1685 weighted_losses = loss_fn(
-> 1686 y_true, y_pred, sample_weight=sample_weight)
1687 loss_fn.reduction = current_loss_reduction
1688
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\losses.py in __call__(self, y_true, y_pred, sample_weight)
94 with ops.name_scope(scope_name, format(self.__class__.__name__),
95 (y_pred, y_true, sample_weight)):
---> 96 losses = self.call(y_true, y_pred)
97 return losses_utils.compute_weighted_loss(
98 losses, sample_weight, reduction=self.reduction)
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\keras\losses.py in call(self, y_true, y_pred)
156 Loss values per sample.
157 """
--> 158 return self.fn(y_true, y_pred, **self._fn_kwargs)
159
160 def get_config(self):
in rmse(matrix_true, matrix_pre, BI_train)
2
3 def rmse(matrix_true, matrix_pre, BI_train=tf_UIBI_train):
----> 4 loss = tf.reduce_sum(tf.square(tf.multiply(matrix_pre, BI_train) - tf.multiply(matrix_true, BI_train)))
5 return loss
6
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(*args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\math_ops.py in multiply(x, y, name)
297 @dispatch.add_dispatch_support
298 def multiply(x, y, name=None):
--> 299 return gen_math_ops.mul(x, y, name)
300
301
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in mul(x, y, name)
6646 # Add nodes to the TensorFlow graph.
6647 _, _, _op = _op_def_lib._apply_op_helper(
-> 6648 "Mul", x=x, y=y, name=name)
6649 _result = _op.outputs[:]
6650 _inputs_flat = _op.inputs
~\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
619 if input_arg.type_attr in attrs:
620 if attrs[input_arg.type_attr] != attr_value:
--> 621 assert False, "Unreachable"
622 else:
623 for base_type in base_types:
AssertionError: Unreachable
[5]
写回答
1回答
-
正十七
2019-08-01
同学你好，我这样写是可以的，不知到是不是版本的问题？我用的是2.0.0-beta1
matrix_pre = tf.constant([[1]], dtype=tf.float32) def customized_mse(y_true, y_pred, a = matrix_pre): return tf.reduce_mean(tf.square(tf.multiply(a, y_pred) - tf.multiply(a, y_true))) model = keras.models.Sequential([ keras.layers.Dense(30, activation="relu", input_shape=x_train_scaled.shape[1:]), keras.layers.Dense(1), ]) model.compile(loss=customized_mse, optimizer="sgd", metrics=["mean_squared_error"]) model.fit(x_train_scaled, y_train, epochs=2, validation_data = (x_valid_scaled, y_valid)) model.evaluate(x_test_scaled, y_test)
00
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