ValueError异常：分类指标无法处理多标记指示器和二元目标的混合

Question

我想申请KerasCLassifier解决多类分类问题。 y的值是独热编码，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)

当我运行的代码的最后一行，它抛出后10个时代出现以下错误：

/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py在accuracy_score（y_true，y_pred，正常化，sample_weight）174 175＃计算精度为每个可能的表示 - > 176 y_type，y_true， y_pred = _check_targets（y_true，y_pred）177 check_consistent_length（y_true，y_pred，sample_weight）178如果y_type.startswith（ '多标记'）：

/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py在_check_targets（y_true，y_pred）79如果len（y_type）> 1：80升ValueError异常（“分类指标不能处理{0}“---> 81 ”和{1}目标“ .format（type_true，type_pred））82 83＃我们不能有一个以上的值的组合y_type =>的集合是不再需要的

ValueError异常：分类指标无法处理多标记指示器和二元目标的混合

当我写categorical_accuracy或balanced_accuracy代替accuracy，我不能编译模型。

Answer 1

这是一个工作演示：

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)

PS请sparse_categorical_*丧失功能和指标的使用注意。