类型错误：流（）缺少 1 个必需的位置参数：'x'

Question

我试图运行这段代码，但我仍然卡住了。

在这段代码中，我使用了预训练的神经 resnet50，并尝试提取深度特征并预测我的类。

请，如果有人遇到此错误，请告诉我如何解决？

谢谢

NUM_CLASSES = 2
CHANNELS = 3
IMAGE_RESIZE = 224
RESNET50_POOLING_AVERAGE = 'avg'
DENSE_LAYER_ACTIVATION = 'softmax'
OBJECTIVE_FUNCTION = 'binary_crossentropy'
LOSS_METRICS = ['accuracy']
NUM_EPOCHS = 10
EARLY_STOP_PATIENCE = 3
STEPS_PER_EPOCH_TRAINING = 10
STEPS_PER_EPOCH_VALIDATION = 10
batch_size = 32
from keras.models import load_model
BATCH_SIZE_TRAINING = 100
BATCH_SIZE_VALIDATION = 100
image_size = IMAGE_RESIZE
WEIGHTS_PATH = "C:\\Users\\Desktop\\RESNET  \\resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
model = Sequential()
train_data_dir = "C:\\Users\\Desktop\\RESNET"
model = ResNet50(include_top=True, weights='imagenet')
model.layers.pop()
model = Model(input=model.input,output=model.layers[-1].output)
model.summary()
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9), metrics=['binary_accuracy'])

data_dir = "C:\\Users\\Desktop\\RESNET"
data_generator = ImageDataGenerator(preprocessing_function=preprocess_input)

train_datagenerator = ImageDataGenerator(rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    validation_split=0.2)
train_generator = train_datagenerator.flow_from_directory(
    train_data_dir,
    target_size=(image_size, image_size), 
    batch_size=BATCH_SIZE_TRAINING,
    class_mode='categorical', shuffle=False, subset='training') # set as training data


validation_generator = train_datagenerator.flow_from_directory(
    train_data_dir, # same directory as training data kifkif
    target_size=(image_size, image_size), 
    batch_size=BATCH_SIZE_TRAINING,
    class_mode='categorical', shuffle=False, subset='validation') # set as validation data

generator = data_generator.flow(batch_size=batch_size)
batch_size = 32
X_train = np.zeros((len(train_generator.images_ids_in_subset),2048))
Y_train = np.zeros((len(train_generator.images_ids_in_subset),2))
nb_batches = int(len(train_generator.images_ids_in_subset) / batch_size) + 1

如果您对此问题有任何疑问，请告诉我

谢谢你的帮助

Answer 1

原始代码使用 pascal 数据库，如下所示：

from data_gen import PascalVOCDataGenerator
data_dir = '/data/VOCdevkit/VOC2007/' 
data_generator_train = PascalVOCDataGenerator('trainval', data_dir)

我正在尝试在我的个人数据集上调整此代码。

谢谢

Answer 2

删除此行

generator = data_generator.flow(batch_size=batch_size)

如果您的代码在那里结束，它什么也不做。

flow方法用于转换 ram 数据中的已有数据，但您的代码没有。