我使用 keras 编写了一个程序，用于检测假冒的真实文本（我使用了 5000 个训练数据和 10,000 个测试数据），我使用了 Transformer 和“distilbert-base-uncased”模型进行检测。现在我决定使用网格搜索来调整超参数，但遇到了以下错误：
TuneError Traceback (most recent call last)
<ipython-input-15-c4a44a2180d8> in <module>()
156 tune_iris,
157 verbose=1,
--> 158 config=hyperparameter_space,
159 )
160
/usr/local/lib/python3.6/dist-packages/ray/tune/tune.py in run(run_or_experiment, name, stop, config, resources_per_trial, num_samples, local_dir, upload_dir, trial_name_creator, loggers, sync_to_cloud, sync_to_driver, checkpoint_freq, checkpoint_at_end, sync_on_checkpoint, keep_checkpoints_num, checkpoint_score_attr, global_checkpoint_period, export_formats, max_failures, fail_fast, restore, search_alg, scheduler, with_server, server_port, verbose, progress_reporter, resume, queue_trials, reuse_actors, trial_executor, raise_on_failed_trial, return_trials, ray_auto_init)
354 if incomplete_trials:
355 if raise_on_failed_trial:
--> 356 raise TuneError("Trials did not complete", incomplete_trials)
357 else:
358 logger.error("Trials did not complete: %s", incomplete_trials)
TuneError: ('Trials did not complete', [tune_iris_83131_00000, tune_iris_83131_00001, tune_iris_83131_00002, tune_iris_83131_00003, tune_iris_83131_00004, tune_iris_83131_00005, tune_iris_83131_00006, tune_iris_83131_00007, tune_iris_83131_00008, tune_iris_83131_00009, tune_iris_83131_00010, tune_iris_83131_00011, tune_iris_83131_00012, tune_iris_83131_00013, tune_iris_83131_00014, tune_iris_83131_00015, tune_iris_83131_00016, tune_iris_83131_00017])
我写的程序如下：
data = pd.concat([train_webtext,train_gen,valid_webtext,valid_gen])
sentences=data['text']
labels=labels1+labels2
len(sentences),len(labels)
DistilBertTokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-cased",do_lower_case=False)
input_ids=[]
attention_masks=[]
for sent in sentences:
bert_inp=DistilBertTokenizer.encode_plus(sent,add_special_tokens = True,max_length =64,pad_to_max_length = True,return_attention_mask = True)
input_ids.append(bert_inp['input_ids'])
attention_masks.append(bert_inp['attention_mask'])
input_ids=np.asarray(input_ids)
attention_masks=np.array(attention_masks)
labels=np.array(labels)
class TuneReporterCallback(keras.callbacks.Callback):
"""Tune Callback for Keras.
The callback is invoked every epoch.
"""
def __init__(self, logs={}):
self.iteration = 0
super(TuneReporterCallback, self).__init__()
def on_epoch_end(self, batch, logs={}):
self.iteration += 1
tune.report(keras_info=logs, mean_accuracy=logs.get("accuracy"), mean_loss=logs.get("loss"))
def tune_gpt(config):
train_inp,val_inp,train_label,val_label,train_mask,val_mask=train_test_split(input_ids,labels,attention_masks,test_size=0.6666666666666666)
DistilBert_model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased',num_labels=2)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
optimizer = tf.keras.optimizers.Adam(learning_rate=config["learning_rate"],epsilon=1e-08)
DistilBert_model.compile(loss=loss,optimizer=optimizer,metrics=[metric])
checkpoint_callback = [tf.keras.callbacks.ModelCheckpoint( "DistilBert_model.h5",monitor='val_loss',mode='min',save_best_only=True)]
callbacks = [checkpoint_callback, TuneReporterCallback()]
history=DistilBert_model.fit([train_inp,train_mask],train_label,batch_size=config["batch_size"],epochs=config["epochs"],validation_data=([val_inp,val_mask],val_label),callbacks=callbacks)
assert len(inspect.getargspec(tune_gpt).args) == 1, "The `tune_gpt` function needs to take in the arg `config`."
hyperparameter_space ={
"batch_size": tune.grid_search([16, 32]),
"learning_rate": tune.grid_search([2e-5, 3e-5, 5e-5]),
"epochs": tune.grid_search([2, 3, 4])
}
analysis = tune.run(
tune_gpt,
verbose=1,
config=hyperparameter_space,
)