Keras - data size for predict()

#
# Code to question on StackOverflow
# https://stackoverflow.com/questions/51421885/expected-dense-to-have-shape-but-got-array-with-shape/51432523#51432523
#

data = '''             query        tags
               hi       intro
      how are you       wellb
            hello       intro
        what's up       wellb
       how's life       wellb
              bye          gb
    see you later          gb
         good bye          gb
           thanks   gratitude
        thank you   gratitude
  that's helpful   gratitude
      I am great  revertfine
            fine  revertfine
       I am fine  revertfine
            good  revertfine'''

import pandas as pd
from io import StringIO
import numpy as np

data = pd.read_table(StringIO(data), sep='\s{2,}')

print(df.columns)
print(df)

#-------------------------------------------------------

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)

print('x_data     :', type(x_data), x_data.shape)
print('x_data[0]  :', type(x_data[0]), x_data[0].shape)
print('x_data[0:1]:', type(x_data[0:1]), x_data[0:1].shape)

predictions = model.predict(x_data[0:1]) # <-- need x_data[0:1] instead of x_data[0]

tag_labels = encoder.classes_
predicted_tags = tag_labels[np.argmax(predictions)]
print (predicted_tags)