回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p>这是为了在Keras中定义一个自定义损失函数。代码如下:</p>
<pre><code>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)
</code></pre>
<p>上述代码返回以下错误:</p>
<pre><code>---------------------------------------------------------------------------
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"]()]]
</code></pre>
<p>所以任何人都知道我们如何将<code>y_true</code>和<code>y_pred</code>转换成numpy数组</p>
<p><strong>编辑:</strong>
<strong>————————————————————————————————————————————————————————————</p>
<p>基本上,我希望在loss函数中写入如下内容:</p>
<pre><code>def custom_loss_function(y_true, y_pred):
classifieds = []
for actual, predicted in zip(y_true, y_pred):
if predicted == 1:
classifieds.<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>(actual)
classification_score = abs(classifieds.count(0) - classifieds.count(1))
return SOME_MAGIC_FUNCTION_TO_CONVERT_INT_TO_TENSOR(classification_score)
</code></pre>