CODEWITHSUNDEEP
python-3.xdeep-learningconv-neural-networkefficientnetdensenet
CaRoule
CaRoule

Reputation: 11

Following Pyhthon CNN architectures Libraries : EfficientNet & DenseNet 169 EfficientNet import Problems

I'm trying to use the following Deep Learning CNN architecutres : DenseNet169 & EfficientNet with transfer learning. I've installed the following libraries bu PyCharm and call the following import libraries : 

from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD, RMSprop
from keras.callbacks import ModelCheckpoint
from keras.callbacks import History

from keras import applications
import keras_applications
#Transfer Learning Networks Models

# 5 - DensNet family
import densenet
from keras.applications.densenet.DenseNet121 import DenseNet121
from keras.applications.densenet.DenseNet169 import DenseNet169
from keras.applications.densenet.DenseNet201 import DenseNet201
from keras_applications.densenet.DenseNet121 import DenseNet121
from keras_applications.densenet.DenseNet169 import DenseNet169
from keras_applications.densenet.DenseNet201 import DenseNet201
# 6 - EfficientNet Alone
import efficientnet.keras as efn
# 6 - EfficientNet family
from efficientnet import EfficientNetB0
from efficientnet import EfficientNetB1
from efficientnet import EfficientNetB2
from efficientnet import EfficientNetB3
from efficientnet import EfficientNetB4
from efficientnet import EfficientNetB5
from efficientnet import EfficientNetB6
from efficientnet import EfficientNetB7

And I call the following architectures:

Downloading of pretrained model and weight

elif model_tl_name == 'DenseNet169':
    print("base_model = DenseNet169")
    base_model = densenet.DenseNetImageNet169(include_top=True, input_shape=(224, 224, 3), input_tensor=None, pooling=None, classes=1000)
    #base_model = DenseNet169(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)
elif model_tl_name == 'EfficientNetB5':
    print("base_model = EfficientNetB5")
    #base_model = EfficientNetB5(include_top=False, weights='imagenet')
    base_model = efn.EfficientNetB5(include_top=False, weights='imagenet')
    # model = EfficientNetB3(weights='imagenet', include_top=False, input_shape=(img_size, img_size, 3))
# Changing last layer to adapt to two classes
model = add_new_last_layer(base_model, nb_classes)

But I always get the following error messages :

For DenseNet169 : mask = node.output_masks[tensor_index] AttributeError: 'Node' object has no attribute 'output_masks'

For EfficientNetB5 from keras.applications import EfficientNetB5 File "C:\Users\QTR7701\AppData\Local\Programs\Python\Python37\lib\site-packages\efficientnet\initializers.py", line 44, in call return tf.random_normal( AttributeError: module 'tensorflow' has no attribute 'random_normal'

If someone can help me.

Upvotes: 1

Views: 743

Answers (1)

bitbang
bitbang

Reputation: 2202

In PyPharm go to settings->project interpreter, and try to load tensorflow lib. After that try->

from tensorflow.keras.applications.efficientnet import EfficientNetB0, EfficientNetB5
mm = EfficientNetB0(include_top=True, weights=None, input_tensor=None, input_shape=None, pooling=None, classes=2, classifier_activation="softmax")
mm.summary()

Upvotes: 0

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