MLPの勉強、教科書通りのコードのはずがエラー。

Question

MLPの練習で教科書通りにコーディングをしているのですが、エラーが出てしまいます。
どなたか、間違いの方をご指摘していただけませんでしょうか。
よろしくお願い致します。

入力したコード↓

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
import json

max_words = 67395 #入力単語数
nb_classes = 9 #9カテゴリを分類

batch_size = 64
nb_epoch = 20

#MLPモデルの生成
def build_model():
    model = Sequential()
    model.add(Dense(512, input_shape=(max_words,)))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model

#データを読み込み
data = json.load(open("./newstext/data-mini.json"))
#data = json.load(open("./newstext/data.json"))
X = data["X"] # テキストを表すデータ
Y = data["Y"] # カテゴリデータ

#学習
X_train, X_test, Y_train, Y_test = train_test_split(X, Y)
Y_train = np_utils.to_categorical(Y_train, nb_classes)
print(len(X_train), len(Y_train))
model = KerasClassifier(
        build_fn=build_model, 
        nb_epoch=nb_epoch,
        batch_size=batch_size)
model.fit(X_train, Y_train)

エラー内容↓

148 148
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-214-d3a06600dbca> in <module>()
      7         nb_epoch=nb_epoch,
      8         batch_size=batch_size)
----> 9 model.fit(X_train, Y_train)

~/anaconda3/lib/python3.6/site-packages/keras/wrappers/scikit_learn.py in fit(self, x, y, sample_weight, **kwargs)
    207         if sample_weight is not None:
    208             kwargs['sample_weight'] = sample_weight
--> 209         return super(KerasClassifier, self).fit(x, y, **kwargs)
    210 
    211     def predict(self, x, **kwargs):

~/anaconda3/lib/python3.6/site-packages/keras/wrappers/scikit_learn.py in fit(self, x, y, **kwargs)
    149         fit_args.update(kwargs)
    150 
--> 151         history = self.model.fit(x, y, **fit_args)
    152 
    153         return history

~/anaconda3/lib/python3.6/site-packages/keras/models.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, **kwargs)
    961                               initial_epoch=initial_epoch,
    962                               steps_per_epoch=steps_per_epoch,
--> 963                               validation_steps=validation_steps)
    964 
    965     def evaluate(self, x=None, y=None,

~/anaconda3/lib/python3.6/site-packages/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, **kwargs)
   1628             sample_weight=sample_weight,
   1629             class_weight=class_weight,
-> 1630             batch_size=batch_size)
   1631         # Prepare validation data.
   1632         do_validation = False

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
   1474                                     self._feed_input_shapes,
   1475                                     check_batch_axis=False,
-> 1476                                     exception_prefix='input')
   1477         y = _standardize_input_data(y, self._feed_output_names,
   1478                                     output_shapes,

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
     74         data = data.values if data.__class__.__name__ == 'DataFrame' else data
     75         data = [data]
---> 76     data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
     77 
     78     if len(data) != len(names):

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in <listcomp>(.0)
     74         data = data.values if data.__class__.__name__ == 'DataFrame' else data
     75         data = [data]
---> 76     data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
     77 
     78     if len(data) != len(names):

AttributeError: 'list' object has no attribute 'ndim'

Answer 1

X、Yのテストデータ、訓練データをnp.array()のなかにに入れてやることで上手くいきました。
また、教科書の方にも訂正箇所があったようなので、そこも参考にしながら解決しました。