我已经为tensorflow.keras定义了自定义指标，以在每个时期之后计算宏f1分数，如下所示：
from tensorflow import argmax as tf_argmax
from sklearn.metric import f1_score
def macro_f1(y_true, y_pred):
# labels are one-hot encoded. so, need to convert
# [1,0,0] to 0 and
# [0,1,0] to 1 and
# [0,0,1] to 2. Then pass these arrays to sklearn f1_score.
y_true = tf_argmax(y_true, axis=1)
y_pred = tf_argmax(y_pred, axis=1)
return f1_score(y_true, y_pred, average='macro')
并在模型编译期间使用它
model_4.compile(loss = 'categorical_crossentropy',
optimizer = Adam(lr=init_lr, decay=init_lr / num_epochs),
metrics = [Recall(name='recall') #, weighted_f1
macro_f1])
当我尝试像这样适应时：
history_model_4 = model_4.fit(train_image_generator.flow(x=train_imgs, y=train_targets, batch_size=batch_size),
validation_data = (val_imgs, val_targets),
epochs=num_epochs,
class_weight=mask_weights_train,
callbacks=[model_save_cb, early_stop_cb, epoch_times_cb],
verbose=2)
这是错误：
OperatorNotAllowedInGraphError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
<ipython-input-57-a890ea61878e>:6 macro_f1 *
return f1_score(y_true, y_pred, average='macro')
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1095 f1_score *
return fbeta_score(y_true, y_pred, 1, labels=labels,
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1217 fbeta_score *
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1478 precision_recall_fscore_support *
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1301 _check_set_wise_labels *
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:80 _check_targets *
check_consistent_length(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:209 check_consistent_length *
uniques = np.unique(lengths)
<__array_function__ internals>:6 unique **
/usr/local/lib/python3.6/dist-packages/numpy/lib/arraysetops.py:263 unique
ret = _unique1d(ar, return_index, return_inverse, return_counts)
/usr/local/lib/python3.6/dist-packages/numpy/lib/arraysetops.py:311 _unique1d
ar.sort()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:877 __bool__
self._disallow_bool_casting()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:487 _disallow_bool_casting
"using a `tf.Tensor` as a Python `bool`")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:474 _disallow_when_autograph_enabled
" indicate you are trying to use an unsupported feature.".format(task))
OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
是什么导致了此类错误，如何修复它并将其用作每个 y 纪元结束时的评估指标之一？
EDIT 1:
注意：所有这些都是在 jupyter 笔记本中完成的，我在单独的行中添加了“>>>”
# getting a batch to pass to model
>>> a_batch = train_image_generator.flow(x=train_imgs, y=train_targets, batch_size=batch_size).next()
# checking its' type to ensure that it's what i though it is
>>> type(a_batch)
# passing the batch to the model
>>> logits = model_4(a_batch)
# checking the type of output
>>> type(logits)
tensorflow.python.framework.ops.EagerTensor
# extracting only the passed targets to calculate f1-score
>>> _, dummy_targets = a_batch
# checking it's type
>>> type(dummy_targets)
numpy.ndarray
>>> macro_f1(y_true=dummy_targets, y_pred=logits)
0.0811965811965812
