VGG16 Keras实施模型。拟合错误

Question

我有一个输入大小为32X32X3的数据集。我想使用VGG16 Keras实现对数据集进行分类。但是在训练阶段，我得到以下错误。如果您能提供帮助，我将不胜感激。谢谢...

错误：

Traceback (most recent call last):
File "VGG16_3.py", line 211, in <module>
   model = DroneVgg()
File "VGG16_3.py", line 31, in __init__
   self.model = self.train(self.model)
File "VGG16_3.py", line 204, in train
   validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2)
File "/truba/home/atkorez/anaconda3/envs/CPU_Env/lib/python3.6/site- packages/keras/legacy/interfaces.py", line 91, in wrapper
   return func(*args, **kwargs)
File "/truba/home/atkorez/anaconda3/envs/CPU_Env/lib/python3.6/site-packages/keras/engine/training.py", line 1418, in fit_generator
   initial_epoch=initial_epoch)
File "/truba/home/atkorez/anaconda3/envs/CPU_Env/lib/python3.6/site-packages/keras/engine/training_generator.py", line 144, in fit_generator
  val_x, val_y, val_sample_weight)
File "/truba/home/atkorez/anaconda3/envs/CPU_Env/lib/python3.6/site-packages/keras/engine/training.py", line 789, in _standardize_user_data
  exception_prefix='target')
File "/truba/home/atkorez/anaconda3/envs/CPU_Env/lib/python3.6/site-packages/keras/engine/training_utils.py", line 128, in standardize_input_data
  'with shape ' + str(data_shape))
ValueError: Error when checking target: expected activation_15 to have 2 dimensions, but got array with shape (329038, 6, 6)

修改 我发送了2条消息，因为在一条消息中收到太多代码错误。

此处的Python代码：

def veri_yukle():

   x_train = np.load('/truba/home/atkorez/CapsNet/x_train.npy')
   x_test = np.load('/truba/home/atkorez/CapsNet/x_test.npy')
   y_train = np.load('/truba/home/atkorez/CapsNet/y_train.npy')
   y_test = np.load('/truba/home/atkorez/CapsNet/y_test.npy')

   return (x_train, y_train), (x_test, y_test)

class DroneVgg:
def __init__(self,train=True):
    self.num_classes = 6
    self.weight_decay = 0.0005
    self.x_shape = [32,32,1]

    self.model = self.build_model()
    if train:
        self.model = self.train(self.model)
    else:
        self.model.load_weights('DroneVgg.h5')


def build_model(self):

    model = Sequential()
    weight_decay = self.weight_decay

    model.add(Conv2D(64, (3, 3), padding='same',
                     input_shape=self.x_shape,kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.3))

    model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size=(2, 2)))


    model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size=(2, 2)))


    model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.5))

    model.add(Flatten())
    model.add(Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
    model.add(Activation('relu'))
    model.add(BatchNormalization())

    model.add(Dropout(0.5))
    model.add(Dense(self.num_classes))
    model.add(Activation('softmax'))
    return model


def normalize(self,X_train,X_test):
    mean = np.mean(X_train,axis=(0,1,2,3))
    std = np.std(X_train, axis=(0, 1, 2, 3))
    X_train = (X_train-mean)/(std+1e-7)
    X_test = (X_test-mean)/(std+1e-7)
    return X_train, X_test


def train(self,model):

    #training parameters
    batch_size = 128
    maxepoches = 250
    learning_rate = 0.1
    lr_decay = 1e-6
    lr_drop = 20
    # The data, shuffled and split between train and test sets:
    (x_train, y_train), (x_test, y_test) = veri_yukle()
    print('Veriler Yuklendi')

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train, x_test = self.normalize(x_train, x_test)

    y_train = keras.utils.to_categorical(y_train, self.num_classes)
    y_test = keras.utils.to_categorical(y_test, self.num_classes)

    def lr_scheduler(epoch):
        return learning_rate * (0.5 ** (epoch // lr_drop))
    reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)

    #data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=15,  # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images
    # (std, mean, and principal components if ZCA whitening is applied).
    datagen.fit(x_train)



    #optimization details
    sgd = optimizers.SGD(lr=learning_rate, decay=lr_decay, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd,metrics=['accuracy'])


    # training process in a for loop with learning rate drop every 25 epoches.

    historytemp = model.fit_generator(datagen.flow(x_train, y_train,
                                     batch_size=batch_size),
                        steps_per_epoch=x_train.shape[0] // batch_size,
                        epochs=maxepoches,
                        validation_data=(x_test, y_test),callbacks=[reduce_lr],verbose=2)
    model.save_weights('DroneVgg.h5')
    return model