tensorflow_概率的密度变化层不使用ModelCheckpoint回调

Question

我在保存一个模型时遇到了一个问题，该模型包括张量流概率的密度变化层

尝试保存完整模型时出现错误：

Epoch 00001: saving model to files/model.h5
Traceback (most recent call last):
  File "train_lstm.py", line 60, in <module>
    main()
  File "train_lstm.py", line 43, in main
    history, model = model_handler.fit_model(
  File "/home/nilsflaschel/Projects/Autopilot/Forecastings/lstm/handlers/model_handler.py", line 114, in fit_model
    history = model.fit(
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1145, in fit
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 428, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 1344, in on_epoch_end
    self._save_model(epoch=epoch, logs=logs)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 1408, in _save_model
    self.model.save(filepath, overwrite=True, options=self._options)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 2001, in save
    save.save_model(self, filepath, overwrite, include_optimizer, save_format,
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/saving/save.py", line 153, in save_model
    hdf5_format.save_model_to_hdf5(
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 115, in save_model_to_hdf5
    model_metadata = saving_utils.model_metadata(model, include_optimizer)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py", line 158, in model_metadata
    raise e
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py", line 155, in model_metadata
    model_config['config'] = model.get_config()
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 650, in get_config
    return copy.deepcopy(get_network_config(self))
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 1349, in get_network_config
    layer_config = serialize_layer_fn(layer)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 250, in serialize_keras_object
    raise e
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 245, in serialize_keras_object
    config = instance.get_config()
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 699, in get_config
    raise NotImplementedError('Layer %s has arguments in `__init__` and '
NotImplementedError: Layer DenseVariational has arguments in `__init__` and therefore must override `get_config`.

如果我只想保存重量，这个：

Epoch 00001: saving model to files/model.h5
Traceback (most recent call last):
  File "train_lstm.py", line 60, in <module>
    main()
  File "train_lstm.py", line 43, in main
    history, model = model_handler.fit_model(
  File "/home/nilsflaschel/Projects/Autopilot/Forecastings/lstm/handlers/model_handler.py", line 114, in fit_model
    history = model.fit(
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1145, in fit
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 428, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 1344, in on_epoch_end
    self._save_model(epoch=epoch, logs=logs)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/callbacks.py", line 1405, in _save_model
    self.model.save_weights(
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 2108, in save_weights
    hdf5_format.save_weights_to_hdf5_group(f, self.layers)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 642, in save_weights_to_hdf5_group
    param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/h5py/_hl/group.py", line 139, in create_dataset
    self[name] = dset
  File "/home/nilsflaschel/miniconda3/envs/autopilot/lib/python3.8/site-packages/h5py/_hl/group.py", line 373, in __setitem__
    h5o.link(obj.id, self.id, name, lcpl=lcpl, lapl=self._lapl)
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py/h5o.pyx", line 202, in h5py.h5o.link
RuntimeError: Unable to create link (name already exists)

我的模型架构：

Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, 7, 73)]           0
_________________________________________________________________
lstm (LSTM)                  (None, 64)                35328
_________________________________________________________________
dense_variational (DenseVari (None, 2)                 390
_________________________________________________________________
distribution_lambda (Distrib multiple                  0
=================================================================
Total params: 35,718
Trainable params: 35,718
Non-trainable params: 0
_________________________________________________________________

编译模型后，我打印了weight.name：

Model Weights:
0 lstm/lstm_cell/kernel:0
1 lstm/lstm_cell/recurrent_kernel:0
2 lstm/lstm_cell/bias:0
3 dense_variational/constant:0
4 dense_variational/constant:0

我想问题源于重复的名字。这很奇怪，我对此没有解释，因为只有一个DenseVariational层

原则上，我只是使用了这里的代码： https://www.tensorflow.org/probability/examples/Probabilistic_Layers_Regression

但添加了一个回调以保存模型：

tf.keras.callbacks.ModelCheckpoint(
                filepath="files/model.h5",
                mode="min",
                monitor="val_loss",
                save_best_only=False,
                verbose=1,
                save_freq="epoch",
                save_weights_only=True
            )

以及完整的模型：

    negloglik = lambda y, rv_y: -rv_y.log_prob(y)
    input1 = tf.keras.layers.Input(shape=X_train.shape[1:])
    net = tf.keras.layers.LSTM(64, dropout=0.2)(input1)
    net = tfp.layers.DenseVariational(
        units=1 + 1,
        make_posterior_fn=posterior_mean_field,
        make_prior_fn=prior_trainable, 
        kl_weight=1/X_train.shape[0],
        )(net)
    net = tfp.layers.DistributionLambda(
        lambda t: tfd.Normal(loc=t[..., :1],
        scale=1e-3 + tf.math.softplus(0.01 * t[...,1:])),
        )(net)
    model = tf.keras.Model([input1], net)
    model.compile(
        loss=negloglik,
        optimizer=tfa.optimizers.AdamW(learning_rate=learning_rate, weight_decay=5e-5),
    )