

Keras初心者です。  
CNNを理解するためにKerasでモデルを作り、cifar10のデータを元にモデルの認識精度を出力しようとしています。  
以下のプログラムに対するエラーの原因がわからずに困っております。  
もしご存知の方いらしましたら、ご教授またはご指摘頂けると幸いです。

以下プログラム

[code]

    from keras.datasets import cifar10
    from keras.preprocessing.image import ImageDataGenerator
    from keras.models import Sequential
    from keras.models import load_model
    from keras.layers.core import Dense, Dropout, Activation, Flatten
    from keras.layers.convolutional import Convolution3D, MaxPooling2D
    import keras.optimizers
    from keras.utils import np_utils
    
    #highparameter
    batch_size = 32
    nb_classes = 10
    nb_epoch = 80
    data_augmentation = False
    
    
    # the data, shuffled and split between train and test sets
    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
    print('X_train shape:', X_train.shape)
    print(X_train.shape[0], 'train samples')
    print(X_test.shape[0], 'test samples')
    
    # convert class vectors to binary class matrices
    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)
    
    
    model = Sequential()
    #input: number of frames/depth:  3 , number of channels: 1 , width: 128, height: 128 (3, 1, 128, 128)
    # 1st layer group
    model.add(Convolution3D(nb_filter=64,kernel_dim1=3, 
                            kernel_dim2=3,kernel_dim3=3,
                            activation='relu', 
                            input_shape = X_train.shape[1:])) 
    model.add(Convolution3D(nb_filter=64,kernel_dim1=3, 
                            kernel_dim2=3,kernel_dim3=3,
                            activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    # 2st layer group
    model.add(Convolution3D(nb_filter=128,kernel_dim1=3, 
                            kernel_dim2=3,kernel_dim3=3,
                            activation='relu')) 
    model.add(Convolution3D(nb_filter=64,kernel_dim1=3, 
                            kernel_dim2=3,kernel_dim3=3,
                            activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    # 3st layer group
    model.add(Convolution3D(nb_filter=256,kernel_dim1=3, 
                            kernel_dim2=3,kernel_dim3=3,
                            activation='relu')) 
    model.add(Convolution3D(nb_filter=256,kernel_dim1=3, 
                            kernel_dim2=3,kernel_dim3=3,
                            activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    
    model.add(Flatten())
    model.add(Dense(1024),activation('relu'))
    model.add(Dense(512),activation('relu'))
    model.add(Dense(num_classes))
    
    model.summary()
    
    model.compile(loss='categorical_crossentropy',
                  optimizer=RMSprop(),
                  metrics=['accuracy'])
    
    history = model.fit(x_train, y_train,
                        batch_size=batch_size,
                        epochs=nb_epochs,
                        verbose=1,
                        validation_data=(x_test, y_test))
    
    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])
    
[/code]

以下エラー

[code]

    > ValueError                                Traceback (most recent call
    > last) <ipython-input-6-787c050ba750> in <module>()
    >      33                         kernel_dim2=3,kernel_dim3=3,
    >      34                         activation='relu',
    > ---> 35                         input_shape = X_train.shape[1:])) 
    >      36 model.add(Convolution3D(nb_filter=64,kernel_dim1=3, 
    >      37                         kernel_dim2=3,kernel_dim3=3,
    > 
    > /usr/local/lib/python2.7/dist-packages/keras/models.pyc in add(self,
    > layer)
    >     420                 # and create the node connecting the current layer
    >     421                 # to the input layer we just created.
    > --> 422                 layer(x)
    >     423 
    >     424             if len(layer.inbound_nodes) != 1:
    > 
    > /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in
    > __call__(self, inputs, **kwargs)
    >     509                 # Raise exceptions in case the input is not compatible
    >     510                 # with the input_spec specified in the layer constructor.
    > --> 511                 self.assert_input_compatibility(inputs)
    >     512 
    >     513                 # Collect input shapes to build layer.
    > 
    > /usr/local/lib/python2.7/dist-packages/keras/engine/topology.pyc in
    > assert_input_compatibility(self, inputs)
    >     411                                      self.name + ': expected ndim=' +
    >     412                                      str(spec.ndim) + ', found ndim=' +
    > --> 413                                      str(K.ndim(x)))
    >     414             if spec.max_ndim is not None:
    >     415                 ndim = K.ndim(x)
    > 
    > ValueError: Input 0 is incompatible with layer conv3d_6: expected
    > ndim=5, found ndim=4
    
[/code]


CC BY-SA 3.0
2017-04-26T17:23:37.437
34301
2017-07-20T09:46:13.927
20523
question
深層学習 keras
Kerasを用いたCNN3によるcifar10の画像認識


X_train.shape[1:]とdepthを削って渡しているようですが、depthも渡す必要があるのではないでしょうか。

<https://keras.io/ja/layers/convolutional/#conv3d>

> このレイヤーをモデルの第一層に使うときはキーワード引数input_shape （整数のタプル，サンプル軸を含まない）を指定してください．
> 例えば10フレームの128x128 RGB画像ではinput_shape=(3, 10, 128, 128)．

※そもそも論で3dではなく2dだと思いますが


CC BY-SA 3.0
2017-07-03T12:53:37.570
36073
2017-07-20T09:46:13.927
2017-07-20T09:46:13.927
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answer