我正在尝试使用以下代码导入模型：
% Number of classes
classnames={'0','1','2','3','4','5','6','7','8'};
% Load model into Matlab
% net = importKerasNetwork(netfile);
netxx = importKerasNetwork('model.json','WeightFile','model.h5', 'classnames', classnames,'OutputLayerType','classification');
我收到以下错误：
>> load_keras_network_from_py
Error using importKerasNetwork (line 86)
Reference to non-existent field 'class_name'.
Error in load_keras_network_from_py (line 20)
netxx = importKerasNetwork('model.json','WeightFile','model.h5', 'classnames',
classnames,'OutputLayerType','classification');
这是我要在MATLAB中导入的JSON模型的结构：
{
"class_name":"Sequential",
"config":{
"name":"sequential_1",
"layers":[
{
"class_name":"Conv2D",
"config":{
"name":"conv2d_1",
"trainable":true,
"batch_input_shape":[
null,
128,
128,
3
],
"dtype":"float32",
"filters":32,
"kernel_size":[
3,
3
],
"strides":[
1,
1
],
"padding":"valid",
"data_format":"channels_last",
"dilation_rate":[
1,
1
],
"activation":"relu",
"use_bias":true,
"kernel_initializer":{
"class_name":"VarianceScaling",
"config":{
"scale":1.0,
"mode":"fan_avg",
"distribution":"uniform",
"seed":null
}
},
"bias_initializer":{
"class_name":"Zeros",
"config":{
}
},
"kernel_regularizer":null,
"bias_regularizer":null,
"activity_regularizer":null,
"kernel_constraint":null,
"bias_constraint":null
}
},
{
"class_name":"MaxPooling2D",
"config":{
"name":"max_pooling2d_1",
"trainable":true,
"pool_size":[
2,
2
],
"padding":"valid",
"strides":[
2,
2
],
"data_format":"channels_last"
}
},
{
"class_name":"Conv2D",
"config":{
"name":"conv2d_2",
"trainable":true,
"filters":32,
"kernel_size":[
3,
3
],
"strides":[
1,
1
],
"padding":"valid",
"data_format":"channels_last",
"dilation_rate":[
1,
1
],
"activation":"relu",
"use_bias":true,
"kernel_initializer":{
"class_name":"VarianceScaling",
"config":{
"scale":1.0,
"mode":"fan_avg",
"distribution":"uniform",
"seed":null
}
},
"bias_initializer":{
"class_name":"Zeros",
"config":{
}
},
"kernel_regularizer":null,
"bias_regularizer":null,
"activity_regularizer":null,
"kernel_constraint":null,
"bias_constraint":null
}
},
{
"class_name":"MaxPooling2D",
"config":{
"name":"max_pooling2d_2",
"trainable":true,
"pool_size":[
2,
2
],
"padding":"valid",
"strides":[
2,
2
],
"data_format":"channels_last"
}
},
{
"class_name":"Conv2D",
"config":{
"name":"conv2d_3",
"trainable":true,
"filters":64,
"kernel_size":[
3,
3
],
"strides":[
1,
1
],
"padding":"valid",
"data_format":"channels_last",
"dilation_rate":[
1,
1
],
"activation":"relu",
"use_bias":true,
"kernel_initializer":{
"class_name":"VarianceScaling",
"config":{
"scale":1.0,
"mode":"fan_avg",
"distribution":"uniform",
"seed":null
}
},
"bias_initializer":{
"class_name":"Zeros",
"config":{
}
},
"kernel_regularizer":null,
"bias_regularizer":null,
"activity_regularizer":null,
"kernel_constraint":null,
"bias_constraint":null
}
},
{
"class_name":"MaxPooling2D",
"config":{
"name":"max_pooling2d_3",
"trainable":true,
"pool_size":[
2,
2
],
"padding":"valid",
"strides":[
2,
2
],
"data_format":"channels_last"
}
},
{
"class_name":"Flatten",
"config":{
"name":"flatten_1",
"trainable":true,
"data_format":"channels_last"
}
},
{
"class_name":"Dense",
"config":{
"name":"dense_1",
"trainable":true,
"units":128,
"activation":"relu",
"use_bias":true,
"kernel_initializer":{
"class_name":"VarianceScaling",
"config":{
"scale":1.0,
"mode":"fan_avg",
"distribution":"uniform",
"seed":null
}
},
"bias_initializer":{
"class_name":"Zeros",
"config":{
}
},
"kernel_regularizer":null,
"bias_regularizer":null,
"activity_regularizer":null,
"kernel_constraint":null,
"bias_constraint":null
}
},
{
"class_name":"Dense",
"config":{
"name":"dense_2",
"trainable":true,
"units":1,
"activation":"softmax",
"use_bias":true,
"kernel_initializer":{
"class_name":"VarianceScaling",
"config":{
"scale":1.0,
"mode":"fan_avg",
"distribution":"uniform",
"seed":null
}
},
"bias_initializer":{
"class_name":"Zeros",
"config":{
}
},
"kernel_regularizer":null,
"bias_regularizer":null,
"activity_regularizer":null,
"kernel_constraint":null,
"bias_constraint":null
}
}
]
},
"keras_version":"2.2.4",
"backend":"tensorflow"
}
我已经尝试了几种方法来解决此问题（包括导入h5文件而不是JSON），但我真的不知道为什么会这样...在使用Python保存keras模型时，是否还有其他限制？它在matlab上运行？
我将不胜感激。
答案 0 :(得分：1)
您的“ keras_version”：“ 2.2.4”。 将其更改为2.1.2可以解决此问题。