如何在 Tensorflow 2 中的嵌入层之后放置 Conv1D 层？

Question

对于评估，我需要能够将卷积层应用于文本数据。 所以我正在尝试对亚马逊评论进行情绪分析。 然而，在Embedding层之后， Conv1D层将无法获得所需的形状。

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
print(f'Tensorflow version {tf.__version__}')
from tensorflow import keras
from tensorflow.keras.layers import Dense, Conv1D, GlobalAveragePooling1D, Embedding
import tensorflow_datasets as tfds
from tensorflow.keras.models import Model

(train_data, test_data), info = tfds.load('imdb_reviews/subwords8k',
                                          split=[tfds.Split.TRAIN, tfds.Split.TEST],
                                          as_supervised=True, with_info=True)

padded_shapes = ([None], ())

train_dataset = train_data.shuffle(25000).padded_batch(padded_shapes=padded_shapes, batch_size=16)
test_dataset = test_data.shuffle(25000).padded_batch(padded_shapes=padded_shapes, batch_size=16)

n_words = info.features['text'].encoder.vocab_size


class ConvModel(Model):
    def __init__(self):
        super(ConvModel, self).__init__()
        self.embe = Embedding(n_words, output_dim=16)
        self.conv = Conv1D(32, kernel_size=6, activation='elu')
        self.glob = GlobalAveragePooling1D()
        self.dens = Dense(2)

    def call(self, x, training=None, mask=None):
        x = self.embe(x)
        x = self.conv(x)
        x = self.glob(x)
        x = self.dens(x)
        return x

conv = ConvModel()

conv(next(iter(train_data))[0])

ValueError: 层 conv1d_25 的输入 0 与层不兼容：预期 ndim=3，发现 ndim=2。 收到的完整形状：[163, 16]

怎么可能实现这一点，如果我错了，将Conv1D层用于文本序列的正确方法是什么？

Answer 1

它是conv(next(iter(train_dataset))[0])而不是conv(next(iter(train_data))[0])

网络结构没问题

Answer 2

到目前为止，你做得很好。 应更改代码的最后一行。 就这样。 参数应该是 train_data 而不是 train_dataset。

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
print(f'Tensorflow version {tf.__version__}')
from tensorflow import keras
from tensorflow.keras.layers import Dense, Conv1D, GlobalAveragePooling1D, Embedding
import tensorflow_datasets as tfds
from tensorflow.keras.models import Model

(train_data, test_data), info = tfds.load('imdb_reviews/subwords8k',
                                          split=[tfds.Split.TRAIN, tfds.Split.TEST],
                                          as_supervised=True, with_info=True)

padded_shapes = ([None], ())

train_dataset = train_data.shuffle(25000).padded_batch(padded_shapes=padded_shapes, batch_size=16)
test_dataset = test_data.shuffle(25000).padded_batch(padded_shapes=padded_shapes, batch_size=16)

n_words = info.features['text'].encoder.vocab_size


class ConvModel(Model):
    def __init__(self):
        super(ConvModel, self).__init__()
        self.embe = Embedding(n_words, output_dim=16)
        self.conv = Conv1D(32, kernel_size=6, activation='elu')
        self.glob = GlobalAveragePooling1D()
        self.dens = Dense(2)

    def call(self, x, training=None, mask=None):
        x = self.embe(x)
        x = self.conv(x)
        x = self.glob(x)
        x = self.dens(x)
        return x

conv = ConvModel()

conv(next(iter(train_data))[0])

希望你修复错误。

Answer 3

词嵌入层的 out_dim 应与 conv1D 输入过滤器大小匹配。 尝试将 out_dim 更改为 32。正确方法： https://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/