Tensorflow：如何将预训练模型已经嵌入的数据输入到 LSTM 模型中？

Posted 2023-02-16

【中文标题】Tensorflow：如何将预训练模型已经嵌入的数据输入到 LSTM 模型中？

【英文标题】：Tensorflow: How to input data already embedded by pre-train model into a LSTM model?

【发布时间】：2022-01-20 03:06:37

【问题描述】：

我是 TensorFlow 的新手。我正在构建一个简单的 LSTM 来进行情感分析（二进制分类）。我在名为traindata 的python 列表中有15391 个句子。所有句子都已经嵌入了预训练模型。现在句子的形状是(591, 200)：每个句子有591个单词，每个单词嵌入为一个(200,)向量。因此，数据是(591, 200) 张量的python 列表。下面是我的模型：

BATCH_SIZE = 32 #128
def lstm_model(input_shape, units):
    input_data = Input(shape=input_shape, dtype="float32", name="input_layer")
    x = LSTM(units, input_shape=(591, 200), name="lstm_layer_1", activation="tanh", return_sequences=False)(input_data)
    x = Dense(units=1, name="full_connection_layer_1")(x)
    x = Activation("sigmoid", name="activation_layer")(x)
    model = Model(inputs=input_data, outputs=x)
    return model
    
model = lstm_model(traindata[0].shape, 32)
model.summary()

tf.compat.v1.random.set_random_seed(111)
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2), metrics=['binary_accuracy', 'Precision', "Recall", "AUC"])

以上模型总结：

Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_layer (InputLayer)     [(None, 591, 200)]        0         
_________________________________________________________________
lstm_layer_1 (LSTM)          (None, 32)                29824     
_________________________________________________________________
full_connection_layer_1 (Den (None, 1)                 33        
_________________________________________________________________
activation_layer (Activation (None, 1)                 0         
=================================================================
Total params: 29,857
Trainable params: 29,857
Non-trainable params: 0

我指定了BATCH_SIZE = 32：

history_weighted_lstm_model = model.fit(traindata, train_labels, epochs = 2, batch_size = BATCH_SIZE, shuffle=True, class_weight=class_weight)

不幸的是，它引发了错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_5152/3209127219.py in <module>
----> 1 history_weighted_lstm_model = model.fit(traindata, train_labels, epochs = 2, batch_size = BATCH_SIZE, shuffle=True, class_weight=class_weight)

E:\anaconda\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    707         steps=steps_per_epoch,
    708         validation_split=validation_split,
--> 709         shuffle=shuffle)
    710 
    711     # Prepare validation data.

E:\anaconda\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2649           feed_input_shapes,
   2650           check_batch_axis=False,  # Don't enforce the batch size.
-> 2651           exception_prefix='input')
   2652 
   2653     if y is not None:

E:\anaconda\envs\tensorflow-cpu\lib\site-packages\tensorflow\python\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    374                            ': expected ' + names[i] + ' to have ' +
    375                            str(len(shape)) + ' dimensions, but got array '
--> 376                            'with shape ' + str(data_shape))
    377         if not check_batch_axis:
    378           data_shape = data_shape[1:]

ValueError: Error when checking input: expected input_layer to have 3 dimensions, but got array with shape (591, 200)

我认为该模型每次需要 32 (591, 200) 个张量。看来我错了。你能帮我弄清楚如何解决它吗？

【参考方案1】：

您可能混淆了输入形状。试试这样的：

import tensorflow as tf

BATCH_SIZE = 32 
def lstm_model(input_shape, units):
    input_data = tf.keras.layers.Input(shape=input_shape, dtype="float32", name="input_layer")
    x = tf.keras.layers.LSTM(units, name="lstm_layer_1", activation="tanh", return_sequences=False)(input_data)
    x = tf.keras.layers.Dense(units=1, name="full_connection_layer_1")(x)
    x = tf.keras.layers.Activation("sigmoid", name="activation_layer")(x)
    model = tf.keras.Model(inputs=input_data, outputs=x)
    return model
    
model = lstm_model(input_shape=(591, 200), units=32)
model.summary()

samples, words, embedding_representation = 64, 591, 200
traindata = tf.random.normal((samples, words, embedding_representation))
train_labels = tf.random.uniform((64, 1), maxval=2, dtype=tf.int32)
class_weight = 0: 1.,
                1: 50.
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2), metrics=['binary_accuracy', 'Precision', "Recall", "AUC"])
history_weighted_lstm_model = model.fit(traindata, train_labels, epochs = 2, batch_size = BATCH_SIZE, shuffle=True, class_weight=class_weight)

Model: "model_2"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_layer (InputLayer)    [(None, 591, 200)]        0         
                                                                 
 lstm_layer_1 (LSTM)         (None, 32)                29824     
                                                                 
 full_connection_layer_1 (De  (None, 1)                33        
 nse)                                                            
                                                                 
 activation_layer (Activatio  (None, 1)                0         
 n)                                                              
                                                                 
=================================================================
Total params: 29,857
Trainable params: 29,857
Non-trainable params: 0
_________________________________________________________________
Epoch 1/2
2/2 [==============================] - 5s 104ms/step - loss: 20.8765 - binary_accuracy: 0.5000 - precision: 0.5758 - recall: 0.5135 - auc: 0.5010
Epoch 2/2
2/2 [==============================] - 0s 100ms/step - loss: 6.4090 - binary_accuracy: 0.7188 - precision: 0.6727 - recall: 1.0000 - auc: 0.9780

【讨论】：

那么，我应该将输入数据重塑为 (15391, 591, 200)，一个 3D 张量？

是的，所以你有 15391 个句子 591 个单词和每个单词的 200 个向量表示......那么它应该可以工作......让我知道

谢谢。我尝试使用tf.stack 来重塑我的列表。但我发现它会耗尽内存。稍后我将在 AWS 上再次尝试。我会告诉你的。

可能对您的小批量数据执行堆栈。

是的，小批量堆栈有效。但是准确率很低。我需要改进它。无论如何，谢谢。