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我正在尝试使用 Keras 中的 CNN 执行多类多标签分类。我试图从一个类似的问题中基于这个函数创建一个单独的标签准确度函数

我尝试过的相关代码是：

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

然后它被称为度量：

model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
              loss='binary_crossentropy', metrics=[metrics.binary_accuracy, 
              single_class_accuracy(interesting_id)])

但我得到的错误是：

> 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'.

我尝试更改类型无济于事。

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1 回答 1

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K.equal具有不同数据类型的输入。我认为您应该转换class_id_preds为float32或。如果后者是整数（否则转换其他张量），这应该可以解决错误：interesting_class_idint64

interesting_class_id = K.cast(interesting_class_id, 'int64')

于 2017-12-11T21:38:50.883 回答