在Keras中使用K.eval（）将张量转换为np.array将返回InvalidArgumentE

Question

这是为了在Keras中定义一个自定义损失函数。代码如下：

from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam

def custom_loss_function(y_true, y_pred):
    a_numpy_y_true_array = K.eval(y_true)
    a_numpy_y_pred_array = K.eval(y_pred)

    # some million dollar worth custom loss that needs numpy arrays to be added here...

    return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)


def build_model():
    model= Sequential()
    model.add(Dense(16, input_shape=(701, ), activation='relu'))
    model.add(Dense(16, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss=custom_loss_function, optimizer=Adam(lr=0.005), metrics=['accuracy'])  
    return model

model = build_model()
early_stop = EarlyStopping(monitor="val_loss", patience=1) 
model.fit(kpca_X, y, epochs=50, validation_split=0.2, callbacks=[early_stop], verbose=False)

上述代码返回以下错误：

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
D:\milind.dalvi\personal\_python\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1326     try:
-> 1327       return fn(*args)
   1328     except errors.OpError as e:

D:\milind.dalvi\personal\_python\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1305                                    feed_dict, fetch_list, target_list,
-> 1306                                    status, run_metadata)
   1307 

D:\milind.dalvi\personal\_python\Anaconda3\lib\contextlib.py in __exit__(self, type, value, traceback)
     88             try:
---> 89                 next(self.gen)
     90             except StopIteration:

D:\milind.dalvi\personal\_python\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
    465           compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466           pywrap_tensorflow.TF_GetCode(status))
    467   finally:

InvalidArgumentError: You must feed a value for placeholder tensor 'dense_84_target' with dtype float and shape [?,?]
     [[Node: dense_84_target = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

所以任何人都知道我们如何将y_true和y_pred转换成numpy数组

编辑：————————————————————————————————————————————————————————————

基本上，我希望在loss函数中写入如下内容：

def custom_loss_function(y_true, y_pred):

    classifieds = []
    for actual, predicted in zip(y_true, y_pred):
        if predicted == 1:
            classifieds.append(actual)
    classification_score = abs(classifieds.count(0) - classifieds.count(1))

    return SOME_MAGIC_FUNCTION_TO_CONVERT_INT_TO_TENSOR(classification_score)

Answer 1

So anybody knows how we could convert y_true and y_pred which is Tensor("dense_84_target:0", shape=(?, ?), dtype=float32) into numpy array

the_tensor = K.arange(5)
// >>> Tensor("arange:0", shape=(5,), dtype=int32)
the_np = the_tensor.eval(session=K.get_session())
// >>> [0 1 2 3 4]

Answer 2

用该模型编制了损耗函数。在编译时，y_true和y_pred只是占位符张量，因此它们还没有值，因此无法计算。这就是为什么您会收到错误消息。

损失函数应该使用Keras张量，而不是它们计算的numpy数组。如果需要使用其他numpy数组，请通过keras.backend（Keras Backend Documentation）的variable方法将它们转换为张量。

编辑：

你仍然需要呆在路缘石的功能空间，使你的损失工作。如果这是您要实现的具体损失函数，并且假设您的值在{0,1}中，则可以尝试如下操作：

import keras.backend as K

def custom_loss_function(y_true, y_pred):

    y_true = y_true*2 - K.ones_like(y_true) # re-codes values of y_true from {0,1} to {-1,+1}
    y_true = y_true*y_pred # makes the values that you are not interested in equal to zero
    classification_score = K.abs(K.sum(y_true))