我正在进行一个卷积神经网络（CNN）项目，用于对一系列音高进行分类。音高类别总共有51类，意味着我希望对数据集中可用的51个音高进行分类。
对于评估指标，我计划使用精确率、召回率和F1分数。我参考了这个帖子来创建如下函数：
我创建的函数如下：
from keras import backend as Kdef recall_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) recall = true_positives / (possible_positives + K.epsilon()) return recalldef precision_m(y_true, y_pred): true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) precision = true_positives / (predicted_positives + K.epsilon()) return precisiondef f1_m(y_true, y_pred): precision = precision_m(y_true, y_pred) recall = recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon()))我使用metrics=['accuracy', f1_m, precision_m, recall_m]创建的模型操作如下：
epochs = 200batch_size = 50weight_optimizer = keras.optimizers.Adam(lr=0.0001)with tf.device('/device:GPU:0'): model.compile(optimizer = weight_optimizer , loss = "categorical_crossentropy", metrics=['accuracy', f1_m, precision_m, recall_m]]) history = model.fit(X_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_split=0.1)然后我得到了这个错误：
ValueError: in user code: /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function * return step_function(self, iterator) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica return fn(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step ** outputs = model.train_step(data) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:758 train_step self.compiled_metrics.update_state(y, y_pred, sample_weight) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:408 update_state metric_obj.update_state(y_t, y_p, sample_weight=mask) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated update_op = update_state_fn(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:177 update_state_fn return ag_update_state(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:620 update_state ** matches, sample_weight=sample_weight) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:355 update_state values = math_ops.cast(values, self._dtype) /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper return target(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:964 cast x = ops.convert_to_tensor(x, name="x") /usr/local/lib/python3.6/dist-packages/tensorflow/python/profiler/trace.py:163 wrapped return func(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1540 convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:339 _constant_tensor_conversion_function return constant(v, dtype=dtype, name=name) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:265 constant allow_broadcast=True) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py:283 _constant_impl allow_broadcast=allow_broadcast)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_util.py:445 make_tensor_proto raise ValueError("None values not supported.") ValueError: None values not supported.如果我从metrics中删除f1_m, precision_m, recall_m，就不会出现任何错误。是否有任何线索可以让我在不出现None values错误的情况下将这些f1_m, precision_m, recall_m包含在metrics中？或者是因为我的分类不是二元分类？谢谢你。
回答：
根据这个线程，你应该将优化器改为如下：
optimizer = "adam"另外，你的函数f1_m是不完整的，应该是这样的。
def f1_m(y_true, y_pred): precision = precision_m(y_true, y_pred) recall = recall_m(y_true, y_pred) return 2*((precision*recall)/(precision+recall+K.epsilon()))