我正在解决一个多类分类问题。数据集如下所示：

|---------------------|------------------|----------------------|------------------|
|      feature 1      |     feature 3    |   feature 4          |     feature 2    |
|---------------------|------------------|------------------------------------------
|          1.302      |       102.987    |      1.298           |     99.8         |
|---------------------|------------------|----------------------|------------------|
|---------------------|------------------|----------------------|------------------|
|          1.318      |       102.587    |      1.998           |     199.8        |
|---------------------|------------------|----------------------|------------------|

字符串
这4个特征是浮点数，我的目标变量类是1、2或3。当我构建跟随模型并训练时，需要很长时间才能收敛（24小时，仍在运行）
我使用了一个Keras模型，如下所示：

def create_model(optimizer='adam', init='uniform'):
    # create model
    if verbose: print("**Create model with optimizer: %s; init: %s" % (optimizer, init) )
    model = Sequential()
    model.add(Dense(16, input_dim=X.shape[1], kernel_initializer=init, activation='relu'))
    model.add(Dense(8, kernel_initializer=init, activation='relu'))
    model.add(Dense(4, kernel_initializer=init, activation='relu'))
    model.add(Dense(1, kernel_initializer=init, activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

型
拟合模型

best_epochs = 200
best_batch_size = 5
best_init = 'glorot_uniform'
best_optimizer = 'rmsprop'
verbose=0
model_pred = KerasClassifier(build_fn=create_model, optimizer=best_optimizer, init=best_init, epochs=best_epochs, batch_size=best_batch_size, verbose=verbose)
model_pred.fit(X_train,y_train)

型
我在这里遵循教程：https://www.kaggle.com/stefanbergstein/keras-deep-learning-on-titanic-data
还有一个快速的人工智能模型，如下所示：

cont_names = [ 'feature1', 'feature2', 'feature3', 'feature4']
procs = [FillMissing, Categorify, Normalize]
test = TabularList.from_df(test,cont_names=cont_names, procs=procs)
data = (TabularList.from_df(train, path='.', cont_names=cont_names, procs=procs)
                        .random_split_by_pct(valid_pct=0.2, seed=43)
                        .label_from_df(cols = dep_var)
                        .add_test(test, label=0)
                        .databunch())

learn = tabular_learner(data, layers=[1000, 200, 15], metrics=accuracy, emb_drop=0.1, callback_fns=ShowGraph)

型
我按照下面的教程
https://medium.com/@nikkisharma536/applying-deep-learning-on-tabular-data-for-regression-and-classification-problems-1e5f80743259的

print(X_train.shape,y_train.shape,X_test.shape,y_test.shape)
(138507, 4) (138507, 1) (34627, 4) (34627, 1)

型
不知道为什么这两个模型都需要这么长的时间来运行。我的输入中有任何错误吗？任何帮助都很感激。