ImportError：无法从“keras.layers.normalization”导入名称“BatchNormalization”

Question

我在执行代码时遇到导入问题：

from keras.models import Sequential
from keras.layers.normalization import BatchNormalization

2021-10-06 22:27:14.064885: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-10-06 22:27:14.064974: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "C:\Data\breast-cancer-classification\train_model.py", line 10, in <module>
    from cancernet.cancernet import CancerNet
  File "C:\Data\breast-cancer-classification\cancernet\cancernet.py", line 2, in <module>
    from keras.layers.normalization import BatchNormalization
ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\Users\Catalin\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\layers\normalization\__init__.py)

• 喀拉拉邦版本：2.6.0
• 张量流：2.6.0
• Python版本：3.9.7

它也安装了库

pip install numpy opencv-python pillow tensorflow keras imutils scikit-learn matplotlib

你有什么想法？

库路径

Answer 1

您应该通过以下方式导入BatchNormalization ：

from tensorflow.keras.layers import BatchNormalization

Answer 2

您正在使用过时的导入tf.keras。现在可以直接从以下位置导入图层tensorflow.keras.layers：

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
)
from tensorflow.keras import backend as K


class CancerNet:
    @staticmethod
    def build(width, height, depth, classes):
        model = Sequential()
        shape = (height, width, depth)
        channelDim = -1

        if K.image_data_format() == "channels_first":
            shape = (depth, height, width)
            channelDim = 1

        model.add(SeparableConv2D(32, (3, 3), padding="same", input_shape=shape))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(256))
        model.add(Activation("relu"))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))

        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

model = CancerNet()