我在Keras中为函数定义了一个自定义损失函数:
(y - yhat)^2 + (y * yhat).
def customLoss(y_true, y_pred, sample_weight=None): y_true = K.cast(y_true, 'float32') y_pred = K.cast(y_pred, 'float32') loss = K.square(y_true - y_pred) + K.prod(y_true, y_pred) loss = loss * K.cast(sample_weights, 'float32') return loss当我运行model.fit时，它在TypeError上失败:
earlystopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)history = model.fit(Xtrain, ytrain_raw, validation_data=(Xval, yval_raw), batch_size=128, epochs=500, verbose=1, callbacks=[earlystopping], sample_weight=sample_weights)错误:
TypeError: in user code: /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function * outputs = self.distribute_strategy.run( /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run ** return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica return fn(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step ** y, y_pred, sample_weight, regularization_losses=self.losses) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__ losses = self.call(y_true, y_pred) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call return self.fn(y_true, y_pred, **self._fn_kwargs) <ipython-input-477-99f75f332877>:4 customLoss loss = K.square(y_true - y_pred) + K.prod(y_true, y_pred) /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1716 prod return tf.reduce_prod(x, axis, keepdims) /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:180 wrapper return target(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:2196 reduce_prod name=name)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:6642 prod name=name) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:578 _apply_op_helper param_name=input_name) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:61 _SatisfiesTypeConstraint ", ".join(dtypes.as_dtype(x).name for x in allowed_list))) TypeError: Value passed to parameter 'reduction_indices' has DataType float32 not in list of allowed values: int32, int64然而，如果我删除K.prod(y_true, y_pred)部分，代码可以无障碍运行。
def customLoss(y_true, y_pred, sample_weight=None): y_true = K.cast(y_true, 'float32') y_pred = K.cast(y_pred, 'float32') loss = K.square(y_true - y_pred) #+ K.prod(y_true, y_pred) loss = loss * K.cast(sample_weights, 'float32') return loss哪里出了问题？
回答：
我认为错误来自于你对K.prod()的第二个参数的调用。这个函数接受一个张量x，但你指定了两个张量y_true和y_pred。
错误本身是因为K.prod()的第二个参数指的是轴，必须是整数，而不能是浮点数。
听起来你可能想要使用tf.keras.layers.multiply()或tf.keras.layers.dot()。
