Importerror: Cannot Import Name 'Batchnormalization' from 'Keras. Layers. Normalization'
I Have an Import Problem When Executing My Code: from Keras. Models Import Sequential from Keras. Layers. Normalization Import Batchnormalization 2021-10-06...
i have an import problem when executing my code:
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)
- 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?
3 Answers
You should import BatchNormalization in following way:
from tensorflow.keras.layers import BatchNormalization
You're using outdated imports for tf.keras.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()
this worked for me:
from tensorflow.python.layers.normalization import BatchNormalization
Elena Rostova holds a Master's degree in Public Health Journalism. She covers groundbreaking medical research, holistic wellness trends, mental health awareness, and nutritional science.
