python - TypeError:在 Tensorflow 中使用自定义指标时,“Tensor”类型的对象没有 len()
问题描述
我正在使用带有 Tensorflow 后端的 Keras 开发多类分类问题(4 类)的模型。的值y_test
具有 2D 格式:
0 1 0 0
0 0 1 0
0 0 1 0
这是我用来计算平衡精度的函数:
def my_metric(targ, predict):
val_predict = predict
val_targ = tf.math.argmax(targ, axis=1)
return metrics.balanced_accuracy_score(val_targ, val_predict)
这是模型:
hidden_neurons = 50
timestamps = 20
nb_features = 18
model = Sequential()
model.add(LSTM(
units=hidden_neurons,
return_sequences=True,
input_shape=(timestamps,nb_features),
dropout=0.15
#recurrent_dropout=0.2
)
)
model.add(TimeDistributed(Dense(units=round(timestamps/2),activation='sigmoid')))
model.add(Dense(units=hidden_neurons,
activation='sigmoid'))
model.add(Flatten())
model.add(Dense(units=nb_classes,
activation='softmax'))
model.compile(loss="categorical_crossentropy",
metrics = [my_metric],
optimizer='adadelta')
当我运行此代码时,我收到此错误:
-------------------------------------------------- ------------------------- TypeError Traceback (最近一次调用最后一次) in () 30 model.compile(loss="categorical_crossentropy", 31 metrics = [my_metric], #'accuracy', ---> 32 优化器='adadelta')
~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py 在编译(自我,优化器,损失,指标,loss_weights,sample_weight_mode,weighted_metrics,target_tensors,**kwargs)449 output_metrics = nested_metrics [i ] 450 output_weighted_metrics = nested_weighted_metrics [i] --> 451 handle_metrics(output_metrics) 452 handle_metrics(output_weighted_metrics, weights=weights) 453
~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in handle_metrics(metrics, weights) 418 metric_result = weighted_metric_fn(y_true, y_pred, 419 weights=weights, --> 420 mask=masks[ i]) 421 422 # 附加到 self.metrics_names, self.metric_tensors,
~/anaconda3/lib/python3.6/site-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask) 402 """ 403 # score_array has ndim >= 2 --> 404 score_array = fn(y_true, y_pred) 405 if mask is not None: 406 # 将掩码转换为 floatX 以避免 Theano 中的 float64 向上转换
在 my_metric(targ, predict) 22 val_predict = predict 23 val_targ = tf.math.argmax(targ, axis=1) ---> 24 return metrics.balanced_accuracy_score(val_targ, val_predict) 25 #return 5 26
~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in balance_accuracy_score(y_true, y_pred, sample_weight,adjusted)
1431 1432 """ -> 1433 C = chaos_matrix(y_true, y_pred, sample_weight= sample_weight) 1434 with np.errstate(divide='ignore', invalid='ignore'): 1435
per_class = np.diag(C) / C.sum(axis=1)~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py inconfusion_matrix(y_true, y_pred, labels, sample_weight) 251 252 """ --> 253 y_type, y_true, y_pred = _check_targets(y_true , y_pred) 254 如果 y_type 不在 ("binary", "multiclass"): 255 raise ValueError("%s is not supported" % y_type)
~/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py in _check_targets(y_true, y_pred) 69 y_pred : 数组或指标矩阵 70 """ ---> 71 check_consistent_length(y_true, y_pred) 72 type_true = type_of_target(y_true) 73 type_pred = type_of_target(y_pred)
~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays) 229 """ 230 --> 231 lengths = [_num_samples(X) for X in arrays if X is不是无] 232 唯一 = np.unique(lengths) 233 如果 len(uniques) > 1:
~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in (.0) 229 """ 230 --> 231 lengths = [_num_samples(X) for X in arrays if X is not无] 232 uniques = np.unique(lengths) 233 如果 len(uniques) > 1:
~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in _num_samples(x) 146 return x.shape[0] 147 else: --> 148 return len(x) 149 else: 150返回长度(x)
TypeError:“张量”类型的对象没有 len()
解决方案
您不能在 Keras 张量上调用 sklearn 函数。如果您使用的是 TF 后端,您需要使用 Keras 的后端函数或 TensorFlow 函数自己实现该功能。
balanced_accuracy_score
定义为在每列中获得的召回率的平均值。检查此链接以了解精确度和召回率的实现。至于balanced_accuracy_score
,您可以按如下方式实现:
import keras.backend as K
def balanced_recall(y_true, y_pred):
"""
Computes the average per-column recall metric
for a multi-class classification problem
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)), axis=0)
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)), axis=0)
recall = true_positives / (possible_positives + K.epsilon())
balanced_recall = K.mean(recall)
return balanced_recall
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