在Deeplearning4j中加载keras模型时出错-Java

我在python keras中训练了我的模型。我正在尝试将其加载到Java代码中，但出现以下错误如何解决此问题。

参考：

https://towardsdatascience.com/deploying-keras-deep-learning-models-with-java-62d80464f34a

https://deeplearning4j.konduit.ai/keras-import/overview

Exception in thread "main" org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException: Model class name must be Sequential (found Model). For more information, see http://deeplearning4j.org/docs/latest/keras-import-overview
    at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.<init>(KerasSequentialModel.java:90)
    at org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel.<init>(KerasSequentialModel.java:57)
    at org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder.buildSequential(KerasModelBuilder.java:322)
    at org.deeplearning4j.nn.modelimport.keras.KerasModelImport.importKerasSequentialModelAndWeights(KerasModelImport.java:223)
    at Jktes.jk(Jktes.java:24)
    at Jktes.main(Jktes.java:13)

码：

public static void jk()
    throws IOException, InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {

    String simpleMlp = new ClassPathResource(
        "randomjk.h5").getFile().getPath();
    MultiLayerNetwork model = KerasModelImport.
        importKerasSequentialModelAndWeights(simpleMlp);

}

依赖关系：

<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-native-platform</artifactId>
    <version>1.0.0-beta6</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-modelimport</artifactId>
    <version>1.0.0-beta6</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>0.9.1</version>
</dependency>

我的python-3.6导入：

import datetime
import keras.backend as K
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import statistics
import sys
import tensorflow as tf
import uuid

from IPython.display import display, FileLink

from keras.layers import Activation, BatchNormalization, Conv2D, Dense, Dropout, Flatten, Input, Lambda, MaxPooling2D
from keras.models import Model, Sequential, load_model
from keras.optimizers import Adam, SGD

我如何在python中保存：

model_name_jk = "model_name_jk"
hyper['uuid'] = model_name_jk
stamp('%.1f%% (%.1f%% training) %s' % (test_accuracy, train_accuracy, hyper))
model.save('saved_models/%s.h5' % hyper['uuid'])

我如何在python中创建模型：

hyper['dropout'] = 0.5
model_size = 'L'
if model_size == 'S':
    hyper['conv_filters'] = [32, 64]
    hyper['pool_size'] = (8, 8)
elif model_size == 'M':
    hyper['conv_filters'] = [32, 64, 128]
    hyper['pool_size'] = (4, 4)
else:
    hyper['conv_filters'] = [32, 64, 128, 256, 512]
    hyper['pool_size'] = (2, 2)
hyper['batch_normalization'] = True
hyper['dense_units'] = [6144]
hyper['share_per_character_weights'] = False
hyper['post_shared_dense'] = False
hyper['batch_normalization'] = True

def create_per_character_model(activation):
    inputs = Input(shape=(hyper['charset_len'],))
    x = Dense(hyper['charset_len'], activation='softmax')(inputs)
    return Model(inputs, x, name='char_model')

def create_model():
    x = Input(shape=(hyper['image_height'], hyper['image_width'], 1), name='input')
    image_input = x

    # Shared convolutional layers
    for layer, filters in enumerate(hyper['conv_filters']):
        if hyper['batch_normalization']:
            x = BatchNormalization()(x)
        x = Conv2D(filters, (3, 3), strides=(1, 1), padding='same', name=f'conv_{layer}', activation='relu')(x)
        x = MaxPooling2D(pool_size=hyper['pool_size'], padding='same', name=f'maxpool_{layer}')(x)
        x = Dropout(hyper['dropout'], name=f'conv_dropout_{layer}')(x)

    # Shared dense layers
    x = Flatten()(x)
    for layer, units in enumerate(hyper['dense_units']):
        x = Dense(units, activation='relu', name=f'dense_{layer}')(x)
        x = Dropout(hyper['dropout'], name=f'dense_dropout_{layer}')(x)

    x = Dense(hyper['max_len'] * hyper['charset_len'], name='wide_output', activation='linear')(x)

    # Per-character output layers
    split = Lambda(lambda whole: tf.split(whole, num_or_size_splits=hyper['max_len'], axis=1))(x)
    if hyper['share_per_character_weights']:
        per_character_model = create_per_character_model(activation='relu' if hyper['post_shared_dense'] else 'softmax')
        if hyper['post_shared_dense']:
            outputs = [Dense(hyper['charset_len'], name='output_char_%d' % ii, activation='softmax')(per_character_model(split[ii])) for ii in range(hyper['max_len'])]
        else:
            outputs = [per_character_model(split[ii]) for ii in range(hyper['max_len'])]
    else:
        outputs = [Dense(hyper['charset_len'], name='output_char_%d' % ii, activation='softmax')(split[ii]) for ii in range(hyper['max_len'])]

    model = Model(inputs=[image_input], outputs=outputs)
    model.summary()

    return model
model = create_model()