ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization-CodePudding

i have an import problem when executing my code:

from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import SeparableConv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dropout
from keras.layers.core import Dense
from 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

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)

Keras version: 2.6.0 Tensorflow: 2.6.0 Python version: 3.9.7

The library it is installed also with "pip install numpy opencv-python pillow tensorflow keras imutils scikit-learn matplotlib"

Do you have any ideas?

library path

CodePudding user response：

You're using outdated imports for tf.keras. Layers can now be imported directly from 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()