未知正则化器：tensorflowjs 中的 L2

Question

我在 python 中使用模型

训练了一个模型

reg = 0.000001
model = Sequential()
model.add(Dense(24, activation='tanh', name='input_dense', input_shape=input_shape))
model.add(GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg), reset_after=False))
model.add(Flatten())
model.add(Dense(2, activation='softmax'))

但是当我使用“tensorflowjs_converter --input_format keras”转换这个模型并在浏览器中加载时出现错误

Unhandled Rejection (Error): Unknown regularizer: L2. This may be due to one of the following reasons:

The regularizer is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.

The custom regularizer is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().

model.json文件内容为

{
 "format": "layers-model",
 "generatedBy": "keras v2.4.0",
 "convertedBy": "TensorFlow.js Converter v2.3.0",
 "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, 22, 13],
       "dtype": "float32",
       "sparse": false,
       "ragged": false,
       "name": "input_dense_input"
      }
     },
     {
      "class_name": "Dense",
      "config": {
       "name": "input_dense",
       "trainable": true,
       "batch_input_shape": [null, 22, 13],
       "dtype": "float32",
       "units": 24,
       "activation": "tanh",
       "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": "GRU",
      "config": {
       "name": "gru",
       "trainable": true,
       "dtype": "float32",
       "return_sequences": true,
       "return_state": false,
       "go_backwards": false,
       "stateful": false,
       "unroll": false,
       "time_major": false,
       "units": 24,
       "activation": "tanh",
       "recurrent_activation": "sigmoid",
       "use_bias": true,
       "kernel_initializer": {
        "class_name": "GlorotUniform",
        "config": { "seed": null }
       },
       "recurrent_initializer": {
        "class_name": "Orthogonal",
        "config": { "gain": 1.0, "seed": null }
       },
       "bias_initializer": { "class_name": "Zeros", "config": {} },
       "kernel_regularizer": {
        "class_name": "L2",
        "config": { "l2": 9.999999974752427e-7 }
       },
       "recurrent_regularizer": {
        "class_name": "L2",
        "config": { "l2": 9.999999974752427e-7 }
       },
       "bias_regularizer": null,
       "activity_regularizer": null,
       "kernel_constraint": null,
       "recurrent_constraint": null,
       "bias_constraint": null,
       "dropout": 0.0,
       "recurrent_dropout": 0.0,
       "implementation": 2,
       "reset_after": false
      }
     },
     {
      "class_name": "Flatten",
      "config": {
       "name": "flatten",
       "trainable": true,
       "dtype": "float32",
       "data_format": "channels_last"
      }
     },
     {
      "class_name": "Dense",
      "config": {
       "name": "dense",
       "trainable": true,
       "dtype": "float32",
       "units": 2,
       "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": ["accuracy"],
   "weighted_metrics": null,
   "loss_weights": null,
   "optimizer_config": {
    "class_name": "Nadam",
    "config": {
     "name": "Nadam",
     "learning_rate": 0.0020000000949949026,
     "decay": 0.004000000189989805,
     "beta_1": 0.8999999761581421,
     "beta_2": 0.9990000128746033,
     "epsilon": 1e-7
    }
   }
  }
 },
 "weightsManifest": [
  {
   "paths": ["group1-shard1of1.bin"],
   "weights": [
    { "name": "dense/kernel", "shape": [528, 2], "dtype": "float32" },
    { "name": "dense/bias", "shape": [2], "dtype": "float32" },
    { "name": "gru/gru_cell/kernel", "shape": [24, 72], "dtype": "float32" },
    {
     "name": "gru/gru_cell/recurrent_kernel",
     "shape": [24, 72],
     "dtype": "float32"
    },
    { "name": "gru/gru_cell/bias", "shape": [72], "dtype": "float32" },
    { "name": "input_dense/kernel", "shape": [13, 24], "dtype": "float32" },
    { "name": "input_dense/bias", "shape": [24], "dtype": "float32" }
   ]
  }
 ]
}

Answer 1

选项 1

没有class和L1和L2；它们只是接口(更多here)

有一个 class L1L2 将采用配置和 return 正确的正则化器。您可以手动将所有出现的 L2 替换为 L1L2.

选项 2

注册一个class L2

class L2 {

    static className = 'L2';

    constructor(config) {
       return tf.regularizers.l1l2(config)
    }
}
tf.serialization.registerClass(L2);

// now load the model

未知正则化器：tensorflowjs 中的 L2

Unknown regularizer: L2 in tensorflowjs

javascript

machine-learning

tensorflow

tensorflow.js