0

我有这个使用 TF Hub 的 Elmo 层进行分类任务的网络。奇怪的是，它开始训练，但在过程中失败并出现错误：

不支持的对象类型 int

import tensorflow_hub as hub
import tensorflow as tf

elmo = hub.Module("https://tfhub.dev/google/elmo/3", trainable=True)

from tensorflow.keras.layers import Input, Lambda, Bidirectional, Dense, Dropout, Flatten, LSTM
from tensorflow.keras.models import Model

def ELMoEmbedding(input_text):
    return elmo(tf.reshape(tf.cast(input_text, tf.string), [-1]), signature="default", as_dict=True)["elmo"]

def build_model():
    input_layer = Input(shape=(1,), dtype="string", name="Input_layer")    
    embedding_layer = Lambda(ELMoEmbedding, output_shape=(1024, ), name="Elmo_Embedding")(input_layer)
    BiLSTM = Bidirectional(LSTM(128, return_sequences= False, recurrent_dropout=0.2, dropout=0.2), name="BiLSTM")(embedding_layer)
    Dense_layer_1 = Dense(64, activation='relu')(BiLSTM)
    Dropout_layer_1 = Dropout(0.5)(Dense_layer_1)
    Dense_layer_2 = Dense(32, activation='relu')(Dropout_layer_1)
    Dropout_layer_2 = Dropout(0.5)(Dense_layer_2)
    output_layer = Dense(1, activation='sigmoid')(Dropout_layer_2)
    model = Model(inputs=[input_layer], outputs=output_layer, name="BiLSTM with ELMo Embeddings")
    model.summary()
    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
    return model
elmo_BiDirectional_model = build_model()

import numpy as np
import io
import re
from tensorflow import keras 


i = 0
max_cells = 51 
x_data = np.zeros((max_cells, 1), dtype='object')
y_data = np.zeros((max_cells, 1), dtype='float32')

with io.open('./data/names-sample.txt', encoding='utf-8') as f:
    content = f.readlines()
    for line in content:        
        line = re.sub("[\n]", " ", line)    
        x_data[i] = line
        y_data[i] = .1 #testing!
        
        i = i+1


with tf.Session() as session:
    session.run(tf.global_variables_initializer()) 
    session.run(tf.tables_initializer())
    model_elmo = elmo_BiDirectional_model.fit(x_data, y_data, epochs=100, batch_size=5)
    train_prediction = elmo_BiDirectional_model.predict(x_data)

完整错误：

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
Model: "BiLSTM with ELMo Embeddings"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Input_layer (InputLayer)     [(None, 1)]               0         
_________________________________________________________________
Elmo_Embedding (Lambda)      (None, None, 1024)        0         
_________________________________________________________________
BiLSTM (Bidirectional)       (None, 256)               1180672   
_________________________________________________________________
dense_43 (Dense)             (None, 64)                16448     
_________________________________________________________________
dropout_28 (Dropout)         (None, 64)                0         
_________________________________________________________________
dense_44 (Dense)             (None, 32)                2080      
_________________________________________________________________
dropout_29 (Dropout)         (None, 32)                0         
_________________________________________________________________
dense_45 (Dense)             (None, 1)                 33        
=================================================================
Total params: 1,199,233
Trainable params: 1,199,233
Non-trainable params: 0
_________________________________________________________________
Train on 51 samples
Epoch 1/100
30/51 [================>.............] - ETA: 2s - loss: 0.5324 - acc: 0.0000e+00 Traceback (most recent call last):

  File "C:\temp\Simon\TestElmo2.py", line 52, in <module>
    model_elmo = elmo_BiDirectional_model.fit(x_data, y_data, epochs=100, batch_size=5)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 727, in fit
    use_multiprocessing=use_multiprocessing)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_arrays.py", line 675, in fit
    steps_name='steps_per_epoch')

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_arrays.py", line 394, in model_iteration
    batch_outs = f(ins_batch)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\backend.py", line 3476, in __call__
    run_metadata=self.run_metadata)

  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\client\session.py", line 1472, in __call__
    run_metadata_ptr)

InternalError: Unsupported object type int
4

1 回答 1

0

结果证明这是一个数据问题。我在数据集中有一个空行！

于 2021-10-25T00:37:16.897 回答