我正在尝试使用KERAS中的CNN执行多类的多类分类。我试图根据此功能从类似的问题
创建单独的标签精度函数我尝试的相关代码是：
labels = ["dog", "mammal", "cat", "fish", "rock"] #I have more
interesting_id = [0]*len(labels)
interesting_id[labels.index("rock")] = 1 #we only care about rock's accuracy
interesting_label = K.variable(np.array(interesting_label), dtype='float32')
def single_class_accuracy(interesting_class_id):
def single(y_true, y_pred):
class_id_true = K.argmax(y_true, axis=-1)
class_id_preds = K.argmax(y_pred, axis=-1)
# Replace class_id_preds with class_id_true for recall here
accuracy_mask = K.cast(K.equal(class_id_preds, interesting_class_id), 'float32')
class_acc_tensor = K.cast(K.equal(class_id_true, class_id_preds), 'float32') * accuracy_mask
class_acc = K.sum(class_acc_tensor) / K.maximum(K.sum(accuracy_mask), 1)
return class_acc
return single
，然后将其称为度量：
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='binary_crossentropy', metrics=[metrics.binary_accuracy,
single_class_accuracy(interesting_id)])
但是我遇到的错误是：
> Traceback (most recent call last):
File "/share/pkg/tensorflow/r1.3/install/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 490, in apply_op
preferred_dtype=default_dtype)
File "/share/pkg/tensorflow/r1.3/install/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 676, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/share/pkg/tensorflow/r1.3/install/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 677, in _TensorConversionFunction
"of type '%s'" % (dtype.name, v.dtype.name))
ValueError: Incompatible type conversion requested to type 'int64' for variable of type 'float32_ref'
During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
File "bottleneck_model.py", line 190, in <module>
main()
File "bottleneck_model.py", line 171, in main
loss='binary_crossentropy', metrics=[metrics.binary_accuracy, binary_accuracy_with_threshold, single_class_accuracy(interesting_label)])
File "/share/pkg/keras/2.0.6/install/lib/python3.6/site-packages/keras/engine/training.py", line 898, in compile
metric_result = masked_metric_fn(y_true, y_pred, mask=masks[i])
File "/share/pkg/keras/2.0.6/install/lib/python3.6/site-packages/keras/engine/training.py", line 494, in masked
score_array = fn(y_true, y_pred)
File "bottleneck_model.py", line 81, in single
accuracy_mask = K.cast(K.equal(class_id_preds, interesting_class_id), 'float32')
File "/share/pkg/keras/2.0.6/install/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 1516, in equal
return tf.equal(x, y)
File "/share/pkg/tensorflow/r1.3/install/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 753, in equal
result = _op_def_lib.apply_op("Equal", x=x, y=y, name=name)
File "/share/pkg/tensorflow/r1.3/install/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 526, in apply_op
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Equal' Op has type float32 that does not match type int64 of argument 'x'.
我尝试将类型更改为无济于事。
K.equal的输入具有不同的数据类型。我认为您应该将class_id_preds施加到float32或interesting_class_id到int64。如果后者是整数（否则会施放其他张量），则应解决错误：
interesting_class_id = K.cast(interesting_class_id, 'int64')