我正在尝试将在Python中用tf.keras制作的模型转换为tensorflow.js格式，以便在Node.js中使用。这是我的软件包版本：
tensorflowjs: 1.0.1
Keras: 2.2.4
tf-nightly-2.0-preview: 2.0.0.dev20190321 (from pip install tensorflowjs)
这是我的顺序模型，也使用功能性API进行了重新构建：
# Sequential API
model = tf.keras.Sequential()
model.add(layers.Dense(128, activation='relu', input_shape=(22050,))
model.add(layers.Dense(9, activation='softmax'))
# Functional API
inputs = tf.keras.Input(shape=(22050,))
x = layers.Dense(128, activation='relu')(inputs)
logits = layers.Dense(9, activation='softmax')(x)
当我使用tfjs_layers_model将顺序模型转换为tensorflowjs_converter时，使用tensorflowjs可以很好地加载。当我对功能模型执行相同的操作时，会出现格式错误的模型配置错误：
Error: Improperly formatted model config for layer {"_callHook":null,"_addedWeightNames":[],"_stateful":false,"id":1,"activityRegularizer":null,"inputSpec":[{"minNDim":2}],"supportsMasking":true,"_trainableWeights":[],"_nonTrainableWeights":[],"_losses":[],"_updates":[],"_built":false,"inboundNodes":[],"outboundNodes":[],"name":"dense_38","trainable_":true,"updatable":true,"initialWeights":null,"_refCount":null,"fastWeightInitDuringBuild":true,"activation":{},"useBias":true,"kernel":null,"bias":null,"DEFAULT_KERNEL_INITIALIZER":"glorotNormal","DEFAULT_BIAS_INITIALIZER":"zeros","units":128,"kernelInitializer":{"scale":1,"mode":"fanAvg","distribution":"uniform","seed":null},"biasInitializer":{},"kernelConstraint":null,"biasConstraint":null,"kernelRegularizer":null,"biasRegularizer":null}: "input_26"
我也尝试导出为tfjs_graph_model，但是tensorflowjs_converter不允许这样做。我希望模型最终具有多个输出，这就是为什么我想使用功能性API而不是顺序性API的原因。