问tf.data.Dataset.from_generator :值错误“没有足够的值来解压(预期的2，得到1)”

EN

import tensorflow.keras as keras 
from tensorflow.keras.models import Model 
from transformers import TFBertModel,BertConfig

def _input_fn():
    x  = (train_data.iloc[:,0:512]).to_numpy()
    y = (train_data.iloc[:,512:516]).to_numpy()
    attn = np.asarray(np.tile(attn_mask,x.shape[0]).reshape(-1,512))
    def generator():
       for s1, s2, l in zip(x, attn, y):
              yield {"input_id": s1, "attn_mask": s2}, l 
   
    dataset = tf.data.Dataset.from_generator(generator, output_types=({"input_id": tf.int32, "attn_mask": tf.int32}, tf.int32))
    #dataset = dataset.batch(2)
    #dataset = dataset.shuffle
    return dataset

bert = 'bert-base-uncased'

config = BertConfig(dropout=0.2, attention_dropout=0.2)
config.output_hidden_states = False
transformer_model = TFBertModel.from_pretrained(bert, config = config)

input_ids_in = tf.keras.layers.Input(shape=(512), name='input_id', dtype='int32')
input_masks_in = tf.keras.layers.Input(shape=(512), name='attn_mask', dtype='int32') 

embedding_layer = transformer_model(input_ids_in, attention_mask=input_masks_in)[0]
#cls_token = embedding_layer[:,0,:]
#X = tf.keras.layers.BatchNormalization()(cls_token)
X = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(50, return_sequences=True, dropout=0.1, recurrent_dropout=0.1))(embedding_layer)
X = tf.keras.layers.GlobalMaxPool1D()(X)
#X = tf.keras.layers.BatchNormalization()(X)
X = tf.keras.layers.Dense(50, activation='relu')(X)
X = tf.keras.layers.Dropout(0.2)(X)
X = tf.keras.layers.Dense(4, activation='softmax')(X)
model = tf.keras.Model(inputs=[input_ids_in, input_masks_in], outputs = X)


for layer in model.layers[:3]:
  layer.trainable = False

optimizer = tf.keras.optimizers.Adam(0.001, beta_1=0.9, beta_2=0.98,
                                     epsilon=1e-9)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['categorical_accuracy'])

epochs = 1
batch_size =2
history = model.fit(_input_fn(), epochs= epochs, batch_size= batch_size, verbose=2)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_16908/300834086.py in <module>
      2 batch_size =2
      3 #history = model.fit(trainDataGenerator(batch_size), epochs= epochs, validation_data=valDataGenerator(batch_size), verbose=2) #
----> 4 history = model.fit(_input_fn(), epochs= epochs, batch_size= batch_size, verbose=2) #validation_data=val_ds,

~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

~/.local/lib/python3.8/site-packages/transformers/models/bert/modeling_tf_bert.py in call(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
   1124             kwargs_call=kwargs,
   1125         )
-> 1126         outputs = self.bert(
   1127             input_ids=inputs["input_ids"],
   1128             attention_mask=inputs["attention_mask"],

~/.local/lib/python3.8/site-packages/transformers/models/bert/modeling_tf_bert.py in call(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
    771             raise ValueError("You have to specify either input_ids or inputs_embeds")
    772 
--> 773         batch_size, seq_length = input_shape
    774 
    775         if inputs["past_key_values"] is None:

ValueError: Exception encountered when calling layer "bert" (type TFBertMainLayer).

not enough values to unpack (expected 2, got 1)

Call arguments received:
  • input_ids=tf.Tensor(shape=(512,), dtype=int32)
  • attention_mask=tf.Tensor(shape=(512,), dtype=int32)
  • token_type_ids=None
  • position_ids=None
  • head_mask=None
  • inputs_embeds=None
  • encoder_hidden_states=None
  • encoder_attention_mask=None
  • past_key_values=None
  • use_cache=True
  • output_attentions=False
  • output_hidden_states=False
  • return_dict=True
  • training=True
  • kwargs=<class 'inspect._empty'>

<FlatMapDataset shapes: ({input_id: <unknown>, attn_mask: <unknown>}, <unknown>), types: ({input_id: tf.int32, attn_mask: tf.int32}, tf.int32)>