Kerasを用いたCNN3によるcifar10の画像認識

Question

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

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])

以下エラー

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

Answer 1

いろいろ直しました。まだ調整前だと思いますので、パラメータはそのままでは動かない部分以外はそのままです。

わからない部分がありましたら、適宜回答します

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 Convolution2D, MaxPooling2D
import keras.optimizers
from keras.utils import np_utils

#highparameter
batch_size = 32
nb_classes = 10
nb_epochs = 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(Convolution2D(64, (3, 3),
                        activation='relu',
                        input_shape = X_train.shape[1:]))
model.add(Convolution2D(64, (3,3),
                        activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# 2st layer group
model.add(Convolution2D(128, (3, 3),
                        activation='relu'))
model.add(Convolution2D(64,3,
                        activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# 3st layer group
model.add(Convolution2D(256, (3, 3),
                        activation='relu'))
model.add(Convolution2D(256, (3,3),
                        activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 1)))

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(512 ,activation='relu'))
model.add(Dense(nb_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])