マルチクラス分類にKerasのマルチレイヤパーセプトロンを使用する方法

Question

hereの指示に従いました。 はReuter datasetを使用しています。

from keras.datasets import reuters 

(X_train, y_train), (X_test, y_test) = reuters.load_data(path="reuters.pkl", 
                 nb_words=None, 
                 skip_top=0, 
                 maxlen=None, 
                 test_split=0.1) 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Activation 
from keras.optimizers import SGD 

model = Sequential() 
# Dense(64) is a fully-connected layer with 64 hidden units. 
# in the first layer, you must specify the expected input data shape: 
# here, 20-dimensional vectors. 
model.add(Dense(64, input_dim=20, init='uniform')) 
model.add(Activation('tanh')) 
model.add(Dropout(0.5)) 
model.add(Dense(64, init='uniform')) 
model.add(Activation('tanh')) 
model.add(Dropout(0.5)) 
model.add(Dense(10, init='uniform')) 
model.add(Activation('softmax')) 

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) 
model.compile(loss='categorical_crossentropy', 
       optimizer=sgd, 
       metrics=['accuracy']) 

#breaks here 
model.fit(X_train, y_train, 
      nb_epoch=20, 
      batch_size=16) 

score = model.evaluate(X_test, y_test, batch_size=16) 

しかし、コードはモデルフィッティングで破損します。どうすれば問題を解決できますか？

更新：これは私が持っているエラーです。

In [21]: model.fit(X_train, y_train, 
    ....:   nb_epoch=20, 
    ....:   batch_size=16) 
--------------------------------------------------------------------------- 
Exception         Traceback (most recent call last) 
<ipython-input-21-4b227e56e5a9> in <module>() 
     1 model.fit(X_train, y_train, 
     2   nb_epoch=20, 
----> 3   batch_size=16) 

//anaconda/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs) 
    400        shuffle=shuffle, 
    401        class_weight=class_weight, 
--> 402        sample_weight=sample_weight) 
    403 
    404  def evaluate(self, x, y, batch_size=32, verbose=1, 

//anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight) 
    969               class_weight=class_weight, 
    970               check_batch_dim=False, 
--> 971               batch_size=batch_size) 
    972   # prepare validation data 
    973   if validation_data: 

//anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size) 
    909       in zip(y, sample_weights, class_weights, self.sample_weight_modes)] 
    910   check_array_lengths(x, y, sample_weights) 
--> 911   check_loss_and_target_compatibility(y, self.loss_functions, self.internal_output_shapes) 
    912   if self.stateful and batch_size: 
    913    if x[0].shape[0] % batch_size != 0: 

//anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in check_loss_and_target_compatibility(targets, losses, output_shapes) 
    182    if y.shape[1] == 1: 
    183     raise Exception('You are passing a target array of shape ' + str(y.shape) + 
--> 184         ' while using as loss `categorical_crossentropy`. ' 
    185         '`categorical_crossentropy` expects ' 
    186         'targets to be binary matrices (1s and 0s) ' 

Exception: You are passing a target array of shape (10105, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via: 
``` 
from keras.utils.np_utils import to_categorical 
y_binary = to_categorical(y_int) 
``` 

Alternatively, you can use the loss function `sparse_categorical_crossentropy` instead, which does expect integer targets. 

Answer 1

これはKerasでの初心者の間違いです。他のディープ・ラーニング・フレームワークとは異なり、Kerasは通常のクロスエントロピー損失に整数ラベルを使用しません。バイナリ・ベクトル（「ワン・ホット」と呼ばれます）を期待します。ベクトルは正しいクラスのインデックスに対して0と1になります。

あなたは簡単に次のコードで、この形式にラベルを変換することができます：model.fit前

from keras.utils.np_utils import to_categorical 
y_train = to_categorical(y_train) 
y_test = to_categorical(y_test) 

。別の方法として、損失を "sparse_categorical_crossentropy"に変更する方法があります。これには整数ラベルが必要です。