Question

我真的需要这里的帮助，不知道我在做什么错。我有一个CNN可以对图像进行分类，效果很好。现在，我想在最后一个密集层之前集成一个年龄变量，该模型仍在运行，但不再愿意保存。确实，我有这个错误：“ TypeError ：（‘不是JSON可序列化的：’，Dimension（1001））”。从网上找到的示例中，我看不到我的错误：

https://github.com/keras-team/keras/issues/2818

https://github.com/keras-team/keras/issues/9342

正常工作时的代码：

base_model = applications.resnet50.ResNet50(include_top=True, 
                                                   weights='imagenet', 
                                                   pooling='avg', 
                                                   input_shape=(params['n_rows'], params['n_cols'], 
                                                                params['n_channels']))
#Adding custom Layers
x = Dense(1024, activation='relu',input_shape=base_model.output_shape[1:],
          kernel_regularizer=regularizers.l2(0.001))(base_model.output)
x = Dropout(0.60)(x)
out = Dense(params['n_classes'], activation='sigmoid')(x)

#creating the final model
model = Model(inputs=base_model.input, outputs=out)

，当我添加元数据（与每个图像相关的简单数字）时：

base_model = applications.resnet50.ResNet50(include_top=True,  #inception_v3.InceptionV3
                                                   weights='imagenet', #imagenet
                                                   pooling='avg', 
                                                   input_shape=(params['n_rows'], params['n_cols'], 
                                                                params['n_channels']))


#adding age (it should be connected to all local features hence not just adding it at the end)
metadata_input = Input(shape=(1,))

#Adding custom Layers
merge_info = concatenate([metadata_input, base_model.output], axis=1)
x = Dense(1024, activation='relu', input_shape=merge_info.shape[1:], 
          kernel_regularizer=regularizers.l2(0.001))(merge_info)
x = Dropout(0.60)(x)
main_output = Dense(params['n_classes'], activation='sigmoid')(x)

#creating the final model
model = Model(inputs=[base_model.input, metadata_input], outputs=main_output)

此外，我的错误可能来自数据生成器：

class DataGenerator(keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self, list_IDs, labels, image_path, mask_path, batch_size, n_rows, n_cols, n_channels, n_classes, 
                 augmentation=None, preprocessing=None, shuffle=True, normalize=False, age=None):
        self.n1 = n_rows
        self.n2 = n_cols
        self.batch_size = batch_size
        self.image_path = image_path
        self.mask_path = mask_path
        self.labels = labels
        self.age = age
        self.list_IDs = list_IDs
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.shuffle = shuffle
        self.normalize = normalize
        self.augmentation = augmentation
        self.preprocessing = preprocessing
        self.on_epoch_end()

    def __len__(self):
        'number of step per epoch'
        return int(np.floor(len(self.list_IDs) / self.batch_size))

    def __getitem__(self, index):
        'Generate one batch of data'
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
        # Find list of IDs
        list_IDs_temp = [self.list_IDs[k] for k in indexes]
        # Generate data
        return(self.__data_generation(list_IDs_temp))

    def on_epoch_end(self):
        'Updates indexes after each epoch'
        self.indexes = np.arange(len(self.list_IDs))
        if self.shuffle == True:
            np.random.shuffle(self.indexes)

    def __data_generation(self, list_IDs_temp):
        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels) where n_sampled=batch_size
        # Initialization
        X = np.empty((self.batch_size, self.n1, self.n2, self.n_channels))
        a = np.empty((self.batch_size), dtype=int)
        y = np.empty((self.batch_size), dtype=int)

        # Generate data
        for i, ID in enumerate(list_IDs_temp):     
            #handle image
            image = image_augmentation_with_maskrcnn(ID=ID, n1=self.n1,n2=self.n2, 
                                                     image_path=self.image_path, mask_path=self.mask_path,
                                                     augmentation=self.augmentation,
                                                     normalize=self.normalize,
                                                     preprocessing=self.preprocessing)
            X[i,] = image
            #handle class
            y[i] = self.labels[ID]
            #handle age
            if self.age is not None:
                #handle age
                a[i] = self.age[ID]

        #handle age
        if self.age is not None:
            return [X,a], keras.utils.to_categorical(y, num_classes=self.n_classes)
        else:
            return X, keras.utils.to_categorical(y, num_classes=self.n_classes)

任何见解都会有很大帮助。谢谢！

CNN中的元数据不起作用（不再可序列化JSON）

0 个答案: