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ValueError:分类指标无法处理多标签指标和二进制目标的混合

scikit-learn keras machine-learning tensorflow python

ScalaBoy · 技术社区 · 6 年前

我想申请 KerasCLassifier y 是一个热编码的,例如:

0 1 0
1 0 0
1 0 0

这是我的代码:

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier

# Function to create model, required for KerasClassifier
def create_model(optimizer='rmsprop', init='glorot_uniform'):
    # create model
    model = Sequential()
    model.add(Dense(2048, input_dim=X_train.shape[1], kernel_initializer=init, activation='relu'))
    model.add(Dense(512, kernel_initializer=init, activation='relu'))
    model.add(Dense(y_train_onehot.shape[1], kernel_initializer=init, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

# create model
model = KerasClassifier(build_fn=create_model, class_weight="balanced", verbose=2)

# grid search epochs, batch size and optimizer
optimizers = ['rmsprop', 'adam']
epochs = [10, 50]
batches = [5, 10, 20]
init = ['glorot_uniform', 'normal', 'uniform']

param_grid = dict(optimizer=optimizers, epochs=epochs, batch_size=batches, init=init)
grid = model_selection.GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy')

grid_result = grid.fit(X_train], y_train_onehot)

当我运行最后一行代码时,它在10个纪元后抛出以下错误:

准确度评分(y_真、y_pred、标准化、样本重量) 175#计算每个可能表示的精度 --&燃气轮机;176 y_类型,y_真,y_pred=_检查_目标(y_真,y_pred) 178如果y_type.startswith('multilabel'):

/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py in_check_目标(y_true,y_pred) 80 raise VALUERROR(“分类度量无法处理{0}的混合” ---&燃气轮机;81“和{1}目标”。格式(type_true,type_pred)) 83#y#u type=>上不能有多个值;这台电视机不再需要了

ValueError:分类指标无法处理以下组合多标签指示器和二进制目标

categorical_accuracy 或 balanced_accuracy 而不是 accuracy

1 回复 | 直到 6 年前

MaxU - stand with Ukraine 6 年前

下面是一个工作演示:

import numpy as np
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier

N = 100
X_train = np.random.rand(N, 4)
Y_train = np.random.choice([0,1,2], N, p=[.5, .3, .2])

# Function to create model, required for KerasClassifier
def create_model(optimizer='rmsprop', init='glorot_uniform'):
    # create model
    model = Sequential()
    model.add(Dense(2048, input_dim=X_train.shape[1], kernel_initializer=init, activation='relu'))
    model.add(Dense(512, kernel_initializer=init, activation='relu'))
    model.add(Dense(len(np.unique(Y_train)), kernel_initializer=init, activation='softmax'))
    # Compile model
    model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['sparse_categorical_accuracy'])
    return model

# create model
model = KerasClassifier(build_fn=create_model, class_weight="balanced", verbose=2)

# grid search epochs, batch size and optimizer
optimizers = ['rmsprop', 'adam']
epochs = [10, 50]
batches = [5, 10, 20]
init = ['glorot_uniform', 'normal', 'uniform']

param_grid = dict(optimizer=optimizers, epochs=epochs, batch_size=batches, init=init)
grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy')

grid_result = grid.fit(X_train, Y_train)

sparse_categorical_* 损失函数和度量。