小编Pra*_*ati的帖子

我正在使用Keras的Functional API(使用TensorFlow后端)培训具有多个输出层的文本情感分类模型.该模型将Keras预处理API的hashing_trick()函数生成的Numpy散列值数组作为输入,并使用Numpy二进制单热标签数组列表作为其目标,根据Keras规范训练具有多个输出的模型(请参阅fit()的文档:https://keras.io/models/model/).

这是模型,没有大多数预处理步骤:

    textual_features = hashing_utility(filtered_words) # Numpy array of hashed values(training data)

    label_list = [] # Will eventually contain a list of Numpy arrays of binary one-hot labels 

    for index in range(one_hot_labels.shape[0]):
        label_list.append(one_hot_labels[index])

     weighted_loss_value = (1/(len(filtered_words))) # Equal weight on each of the output layers' losses

     weighted_loss_values = []

     for index in range (one_hot_labels.shape[0]):
        weighted_loss_values.append(weighted_loss_value)


     text_input = Input(shape = (1,))


     intermediate_layer = Dense(64, activation = 'relu')(text_input)


     hidden_bottleneck_layer = Dense(32, activation = 'relu')(intermediate_layer)

     keras.regularizers.l2(0.1) …