小编Kha*_*rad的帖子

我知道问题不可能出在数据集上，因为我见过其他项目使用相同的数据集。这是我的数据预处理代码：

import pandas as pd
dataset = pd.read_csv('political_tweets.csv')
dataset.head()
dataset = pd.read_csv('political_tweets.csv')["tweet"].values
y_train = pd.read_csv('political_tweets.csv')["dem_or_rep"].values

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(dataset, y_train, test_size=0.1)

max_words = 10000
print(max_words)
max_len = 25

tokenizer = Tokenizer(num_words = max_words, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n1234567890', lower=False,oov_token="<OOV>")

tokenizer.fit_on_texts(x_train)
x_train = tokenizer.texts_to_sequences(x_train)
x_train = pad_sequences(x_train, max_len, padding='post', truncating='post')

tokenizer.fit_on_texts(x_test)
x_test = tokenizer.texts_to_sequences(x_test)
x_test = pad_sequences(x_test, max_len, padding='post', truncating='post')

还有我的模型：

model = Sequential([
    Embedding(max_words+1,64,input_length=max_len),
    Bidirectional(GRU(64, return_sequences = True), merge_mode='concat'),
    GlobalMaxPooling1D(),
    Dense(64,kernel_regularizer=regularizers.l2(0.02)),
    Dropout(0.5),
    Dense(1, activation='sigmoid'),

])
model.summary()

model.compile(loss='binary_crossentropy', optimizer=RMSprop(learning_rate=0.0001), …