javascript - 未处理的拒绝（RangeError）：使用 tf.models.modelFromJSON 时，Float32Array 的字节长度应为 4 的倍数
问题描述
我尝试训练情绪分析模型并将其加载到 tensorflow.js 但是，当我尝试这样做时，我收到了以下错误消息：
Unhandled Rejection (RangeError): byte length of Float32Array should be a multiple of 4 when using tf.models.modelFromJSON
以下是我开发过程中的一些重要部分：
Python：
model = tf.keras.models.Sequential([
tf.keras.layers.Embedding(num_words, 1000, input_length=65-1),
tf.keras.layers.Conv1D(filters=64, kernel_size=5,
strides=1, padding="causal",
activation="relu",
input_shape=[None, 1]),
tf.keras.layers.MaxPool1D(),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(24, activation='relu'),
tf.keras.layers.Dense(5, activation="softmax"),
])
! tensorflowjs_converter --input_format keras \
/content/covid_td_1.h5 \
/content/covid_tc_02
然后，我将我的 model.json 文件转换为一个 javascript 文件并将其分配给一个const变量。
模型.json
export const modelJSON = {
"format": "layers-model",
"generatedBy": "keras v2.4.0",
"convertedBy": "TensorFlow.js Converter v3.3.0",
"modelTopology": {
"keras_version": "2.4.0",
"backend": "tensorflow",
"model_config": {
"class_name": "Sequential",
"config": {
"name": "sequential",
"layers": [{
"class_name": "InputLayer",
"config": {
"batch_input_shape": [null, 64],
"dtype": "float32",
"sparse": false,
"ragged": false,
"name": "embedding_input"
}
}, {
"class_name": "Embedding",
"config": {
"name": "embedding",
"trainable": true,
"batch_input_shape": [null, 64],
"dtype": "float32",
"input_dim": 10000,
"output_dim": 1000,
"embeddings_initializer": {
"class_name": "RandomUniform",
"config": {
"minval": -0.05,
"maxval": 0.05,
"seed": null
}
},
"embeddings_regularizer": null,
"activity_regularizer": null,
"embeddings_constraint": null,
"mask_zero": false,
"input_length": 64
}
}, {
"class_name": "Conv1D",
"config": {
"name": "conv1d",
"trainable": true,
"batch_input_shape": [null, null, 1],
"dtype": "float32",
"filters": 64,
"kernel_size": [5],
"strides": [1],
"padding": "causal",
"data_format": "channels_last",
"dilation_rate": [1],
"groups": 1,
"activation": "relu",
"use_bias": true,
"kernel_initializer": {
"class_name": "GlorotUniform",
"config": {
"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": "MaxPooling1D",
"config": {
"name": "max_pooling1d",
"trainable": true,
"dtype": "float32",
"strides": [2],
"pool_size": [2],
"padding": "valid",
"data_format": "channels_last"
}
}, {
"class_name": "GlobalAveragePooling1D",
"config": {
"name": "global_average_pooling1d",
"trainable": true,
"dtype": "float32",
"data_format": "channels_last"
}
}, {
"class_name": "Dense",
"config": {
"name": "dense",
"trainable": true,
"dtype": "float32",
"units": 24,
"activation": "relu",
"use_bias": true,
"kernel_initializer": {
"class_name": "GlorotUniform",
"config": {
"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_1",
"trainable": true,
"dtype": "float32",
"units": 5,
"activation": "softmax",
"use_bias": true,
"kernel_initializer": {
"class_name": "GlorotUniform",
"config": {
"seed": null
}
},
"bias_initializer": {
"class_name": "Zeros",
"config": {}
},
"kernel_regularizer": null,
"bias_regularizer": null,
"activity_regularizer": null,
"kernel_constraint": null,
"bias_constraint": null
}
}]
}
},
"training_config": {
"loss": "categorical_crossentropy",
"metrics": [
[{
"class_name": "MeanMetricWrapper",
"config": {
"name": "accuracy",
"dtype": "float32",
"fn": "categorical_accuracy"
}
}]
],
"weighted_metrics": null,
"loss_weights": null,
"optimizer_config": {
"class_name": "Adam",
"config": {
"name": "Adam",
"learning_rate": 0.0010000000474974513,
"decay": 0.0,
"beta_1": 0.8999999761581421,
"beta_2": 0.9990000128746033,
"epsilon": 1e-07,
"amsgrad": false
}
}
}
},
"weightsManifest": [{
"paths": ["group1-shard1of10.bin", "group1-shard2of10.bin", "group1-shard3of10.bin", "group1-shard4of10.bin", "group1-shard5of10.bin", "group1-shard6of10.bin", "group1-shard7of10.bin", "group1-shard8of10.bin", "group1-shard9of10.bin", "group1-shard10of10.bin"],
"weights": [{
"name": "conv1d/kernel",
"shape": [5, 1000, 64],
"dtype": "float32"
}, {
"name": "conv1d/bias",
"shape": [64],
"dtype": "float32"
}, {
"name": "dense/kernel",
"shape": [64, 24],
"dtype": "float32"
}, {
"name": "dense/bias",
"shape": [24],
"dtype": "float32"
}, {
"name": "dense_1/kernel",
"shape": [24, 5],
"dtype": "float32"
}, {
"name": "dense_1/bias",
"shape": [5],
"dtype": "float32"
}, {
"name": "embedding/embeddings",
"shape": [10000, 1000],
"dtype": "float32"
}]
}]
}
这是我初始化模型的地方。
import * as tf from '@tensorflow/tfjs';
import {modelJSON} from './file (1)/content/covid_tc_02/model';
const model = tf.models.modelFromJSON(modelJSON).then((result) => {
console.log(result.summary)
});
如果有帮助，我正在使用带有反应的 tensorflow.js。
我尝试清除后端并重新启动运行时，但这不起作用。
到底是怎么回事？为什么我的模型不能正常工作？
如有需要，请留言以获取更多信息。谢谢！
解决方案
使用 tensorflow.js（至少对我而言）在本地加载模型确实是不可能的。相反，我将模型放入 github 存储库并使用该链接加载模型
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