Question

我正在构建具有多个输入（实际上是3个）的Keras分类模型，以预测单个输出。具体来说，我的3个输入是：

演员
图摘要
相关电影功能

输出：

流派标签

以上所有输入和单个输出都与10,000个IMDB电影有关。

即使模型创建成功，当我尝试在三个不同的X_train上拟合模型时，也会出现Attribute错误。对于演员，我有一个X_train和X_test，对于情节摘要，我有一个不同的X_train和X_test；对于电影功能，我有一个不同的X_train和X_test。所有输入的y_train和y_test都相同。

Python代码（创建多个输入keras）

def kera_multy_classification_model():

    sentenceLength_actors = 15
    vocab_size_frequent_words_actors = 20001

    sentenceLength_plot = 23
    vocab_size_frequent_words_plot = 17501

    sentenceLength_features = 69
    vocab_size_frequent_words_features = 20001

    model = keras.Sequential(name='Multy-Input Keras Classification model')

    actors = keras.Input(shape=(sentenceLength_actors,), name='actors_input')
    plot = keras.Input(shape=(sentenceLength_plot,), name='plot_input')
    features = keras.Input(shape=(sentenceLength_features,), name='features_input')

    emb1 = layers.Embedding(input_dim = vocab_size_frequent_words_actors + 1,
                            # based on keras documentation input_dim: int > 0. Size of the vocabulary, i.e. maximum integer index + 1.
                            output_dim = Keras_Configurations_model1.EMB_DIMENSIONS,
                            # int >= 0. Dimension of the dense embedding
                            embeddings_initializer = 'uniform', 
                            # Initializer for the embeddings matrix.
                            mask_zero = False,
                            input_length = sentenceLength_actors,
                            name="actors_embedding_layer")(actors)
    encoded_layer1 = layers.LSTM(100)(emb1)

    emb2 = layers.Embedding(input_dim = vocab_size_frequent_words_plot + 1,
                            output_dim = Keras_Configurations_model2.EMB_DIMENSIONS,
                            embeddings_initializer = 'uniform',
                            mask_zero = False,
                            input_length = sentenceLength_plot,
                            name="plot_embedding_layer")(plot)
    encoded_layer2 = layers.LSTM(100)(emb2)

    emb3 = layers.Embedding(input_dim = vocab_size_frequent_words_features + 1,
                            output_dim = Keras_Configurations_model3.EMB_DIMENSIONS,
                            embeddings_initializer = 'uniform',
                            mask_zero = False,
                            input_length = sentenceLength_features,
                            name="features_embedding_layer")(features)
    encoded_layer3 = layers.LSTM(100)(emb3)

    merged = layers.concatenate([encoded_layer1, encoded_layer2, encoded_layer3])

    layer_1 = layers.Dense(Keras_Configurations_model1.BATCH_SIZE, activation='relu')(merged)

    output_layer = layers.Dense(Keras_Configurations_model1.TARGET_LABELS, activation='softmax')(layer_1)

    model = keras.Model(inputs=[actors, plot, features], outputs=output_layer)

    print(model.output_shape)

    print(model.summary())

    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['sparse_categorical_accuracy'])

Python代码（适合我的输入的多个输入keras）

def fit_keras_multy_input(model, x_train_seq_actors, x_train_seq_plot, x_train_seq_features, x_test_seq_actors, x_test_seq_plot, x_test_seq_features, y_train, y_test):

    s = time()

    fit_model = model.fit([x_train_seq_actors, x_train_seq_plot, x_train_seq_features], y_train, 
                          epochs=Keras_Configurations_model1.NB_EPOCHS,
                          verbose = Keras_Configurations_model1.VERBOSE,
                          batch_size=Keras_Configurations_model1.BATCH_SIZE,
                          validation_data=([x_test_seq_actors, x_test_seq_plot, x_test_seq_features], y_test),
                          callbacks=callbacks)

    duration = time() - s
    print("\nTraining time finished. Duration {} secs".format(duration))

模型的结构

产生错误

注意：请不要X_train和X_test都是数字序列。（已标记化的文本）

进行了一些研究后，问题开始于model.compile（）函数。虽然，我不确定应该在模型的编译功能中进行哪些更改以解决此问题。

在此问题上，谢谢您的任何建议或帮助。请随时在评论中询问我可能错过的任何其他信息，以使此问题更完整。

Answer 1

您的函数kera_multy_classification_model()不返回任何内容，因此在model = kera_multy_classification_model()之后，您将得到model == None，因为函数不返回任何内容。 None的类型为NoneType，它实际上没有名为fit()的方法。

只需在kera_multy_classification_model()的末尾添加返回模型。

model.fit（）Keras分类多输入-单输出给出错误：AttributeError：'NoneType'对象没有属性'fit'

1 个答案: