多输出分类的类权重

Question

我在创建这样的模型时遇到了问题：

from keras.models import Sequential, Model
from keras.layers import Dense,  Dropout, Flatten
from keras.layers import LSTM, Conv1D, Input, MaxPooling1D, GlobalMaxPooling1D
from keras.layers.embeddings import Embedding

posts_input = Input(shape=(None,), dtype='int32', name='posts')
embedded_posts = Embedding(max_nb_words, embedding_vector_length, input_length=max_post_len)(posts_input)

x = Conv1D(128, 5, activation='relu')(embedded_posts)
x = Dropout(0.25)(x)
x = MaxPooling1D(5)(x)
x = Conv1D(256, 5, activation='relu')(x)
x = Conv1D(256, 5, activation='relu')(x)
x = Dropout(0.25)(x)
x = MaxPooling1D(5)(x)
x = Conv1D(256, 5, activation='relu')(x)
x = Conv1D(256, 5, activation='relu')(x)
x = Dropout(0.25)(x)
x = GlobalMaxPooling1D()(x)
x = Dense(128, activation='relu')(x)

Axe1_prediction = Dense(1, activation='sigmoid', name='axe1')(x)
Axe2_prediction = Dense(1, activation='sigmoid', name='axe2')(x)
Axe3_prediction = Dense(1, activation='sigmoid', name='axe3')(x)
Axe4_prediction = Dense(1, activation='sigmoid', name='axe4')(x)

model = Model(posts_input, [Axe1_prediction, Axe2_prediction, Axe3_prediction, Axe4_prediction])

如您所见，此模型有 4 个输出。

然后我像这样编译这个模型：

model.compile(optimizer='rmsprop', 
              loss=['binary_crossentropy', 
                    'binary_crossentropy', 
                    'binary_crossentropy', 
                    'binary_crossentropy'],
              metrics=['accuracy'])

为了拟合这个模型，我想我需要设置类权重，所以我创建了这些：

from sklearn.preprocessing import LabelEncoder
from sklearn.utils import class_weight

le = LabelEncoder()
y1 = le.fit_transform(df2["Axe1"])
y2 = le.fit_transform(df2["Axe2"])
y3 = le.fit_transform(df2["Axe3"])
y4 = le.fit_transform(df2["Axe4"])

cw1 = class_weight.compute_class_weight('balanced', np.unique(y1), y1)
cw2 = class_weight.compute_class_weight('balanced', np.unique(y2), y2)
cw3 = class_weight.compute_class_weight('balanced', np.unique(y3), y3)
cw4 = class_weight.compute_class_weight('balanced', np.unique(y4), y4)

但最后我不知道如何在配件中设置此参数：

history = model.fit(X_train, 
          [y1_train, y2_train, y3_train, y4_train], 
          epochs=10,
          validation_data=(X_val, [y1_val, y2_val, y3_val, y4_val]));

您能帮我看看如何添加“class_weights =”参数吗？

Answer 1

您必须使用 tensorflow 2.1 或更早版本。多输出模型在 TF2.1 之后移除了类权重功能