我使用 Keras 构建并训练了一个模型，并使用 tensorflowjs 转换器（tfjs.converters.save_keras_model() 函数）保存了它。
后来，当试图在 tensorflowjs 中加载它时，我收到以下错误：
Error: The first layer in a Sequential model must get an 'inputShape' or 'batchInputShape' argument.
但是在签入包含模型结构的 JSON 文件后，指定了一个输入形状。知道为什么 tfjs 无法加载它吗？这可能是由不同的变量名称引起的（batch_input_shape在我的 JSON 文件和batchInputShape错误消息中）。
以下是我构建和训练模型的方法：
model.add(LSTM(128, dropout=0.2, input_shape=(time_steps, input_dim) ))
model.add(Dense(output_dim, activation='sigmoid'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
model.fit_generator(generator=train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=valid_generator,
validation_steps=valid_steps
)
这是 JSON 文件。我不知道第三个null变量是从哪里来的，但是如果我改变它，我会得到一个错误，说维度的数量是错误的。
"format": "layers-model",
"generatedBy": "keras v2.3.1",
"convertedBy": "TensorFlow.js Converter v1.4.0",
"modelTopology": {
"keras_version": "2.3.1",
"backend": "tensorflow",
"model_config": {
"class_name": "Sequential",
"config": {
"name": "sequential_1",
"layers": [
{
"class_name": "LSTM",
"config": {
"name": "lstm_1",
"trainable": true,
"batch_input_shape": [null, 10, 100],
"dtype": "float32",
"return_sequences": false,
"return_state": false,
"go_backwards": false,
"stateful": false,
"unroll": false,
"units": 128,
"activation": "tanh",
"recurrent_activation": "sigmoid",
"use_bias": true,
"kernel_initializer": {
"class_name": "VarianceScaling",
"config": {
"scale": 1.0,
"mode": "fan_avg",
"distribution": "uniform",
"seed": null
}
},
"recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}},
"bias_initializer": {"class_name": "Zeros", "config": {}},
"unit_forget_bias": true,
"kernel_regularizer": null,
"recurrent_regularizer": null,
"bias_regularizer": null,
"activity_regularizer": null,
"kernel_constraint": null,
"recurrent_constraint": null,
"bias_constraint": null,
"dropout": 0.2,
"recurrent_dropout": 0.0,
"implementation": 2
}
},
{"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 29, "activation": "sigmoid", "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}}
]
}
},
"training_config": {
"optimizer_config": {
"class_name": "Adam",
"config": {
"learning_rate": 0.0010000000474974513,
"beta_1": 0.8999999761581421,
"beta_2": 0.9990000128746033,
"decay": 0.0,
"epsilon": 1e-07,
"amsgrad": false
}
},
"loss": "mse",
"metrics": ["accuracy"],
"weighted_metrics": null,
"sample_weight_mode": null,
"loss_weights": null
}
},
"weightsManifest": [{
"paths": ["group1-shard1of1.bin"],
"weights": [
{"name": "dense_1/kernel", "shape": [128, 29], "dtype": "float32"},
{"name": "dense_1/bias", "shape": [29], "dtype": "float32"},
{"name": "lstm_1/kernel", "shape": [100, 512], "dtype": "float32"},
{"name": "lstm_1/recurrent_kernel", "shape": [128, 512], "dtype": "float32"},
{"name": "lstm_1/bias", "shape": [512], "dtype": "float32"}
]
}]
}