我用python keras训练了我的模型。我正试图将其加载到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)
代码：
public static void jk()
throws IOException, InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
String simpleMlp = new ClassPathResource(
"randomjk.h5").getFile().getPath();
MultiLayerNetwork model = KerasModelImport.
importKerasSequentialModelAndWeights(simpleMlp);
}
依赖项：
<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>
我的蟒蛇-3.6进口：
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'])
我是如何在python中创建模型的：
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()
您使用的是顺序模型导入的功能，但使用函数API创建模型。
要导入使用功能性API创建的模型，您需要使用不同的导入器。https://deeplearning4j.konduit.ai/keras-import/model-functional展示了如何做到这一点。
TL；DR是您必须使用KerasModelImport.importKerasModelAndWeights(simpleMlp);而不是KerasModelImport.importKerasSequentialModelAndWeights(simpleMlp);