我在使用Keras运行文本分类模型时，调用model.predict函数时遇到以下错误。我已经搜索了所有地方，但对我没有帮助。
当运行一个文本分类模型时，在调用model.predict函数时，我遇到了如下的错误。我已经到处搜索过，但是还是没有解决问题。
ValueError: Error when checking input: expected dense_1_input to have shape (100,) but got array with shape (1,)
我的数据有5个类别，仅有15个样本。下面是数据集：
query tags
0 hi intro
1 how are you wellb
2 hello intro
3 what's up wellb
4 how's life wellb
5 bye gb
6 see you later gb
7 good bye gb
8 thanks gratitude
9 thank you gratitude
10 that's helpful gratitude
11 I am great revertfine
12 fine revertfine
13 I am fine revertfine
14 good revertfine
这是我的模型代码
from keras.preprocessing.text import Tokenizer
from sklearn.preprocessing import LabelBinarizer
from keras.models import Sequential
import pandas as pd
from keras.layers import Dense, Activation
data = pd.read_csv('text_class.csv')
train_text = data['query']
train_labels = data['tags']
tokenize = Tokenizer(num_words=100)
tokenize.fit_on_texts(train_text)
x_data = tokenize.texts_to_matrix(train_text)
encoder = LabelBinarizer()
encoder.fit(train_labels)
y_data = encoder.transform(train_labels)
model = Sequential()
model.add(Dense(512, input_shape=(100,)))
model.add(Activation('relu'))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
model.fit(x_data, y_data, batch_size=8, epochs=10)
predictions = model.predict(x_data[0])
tag_labels = encoder.classes_
predicted_tags = tag_labels[np.argmax(predictions)]
print (predicted_tags)
我无法确定问题出在哪里以及如何修复它。