image classifier 'Node' object has no attribute 'output_masks'

Question

hiya so ive been doing this image classifier project for university and ive been having trouble with how to use the models and what codes to use and if im doing everything right ive been reading this but i still dont know why i keep getting errors https://keras.io/applications/#vgg16

I use these codes

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from PIL import ImageFile, Image
from tensorflow import keras

print(Image.__file__)
import numpy
import matplotlib.pyplot as plt

# dimensions of our images.
img_width, img_height = 200, 200

train_data_dir = r'C:\Users\Acer\imagerec\Brain\TRAIN'
validation_data_dir = r'C:\Users\Acer\imagerec\Brain\VAL'
nb_train_samples = 140
nb_validation_samples = 40
epochs = 20
batch_size = 5

if K.image_data_format() == 'channels_first':
    input_shape = (1, img_height, img_width)
else:
    input_shape = (img_height, img_width, 1)

from keras.applications.densenet import DenseNet121
from keras.models import Model
from keras.layers import Dense

MN = keras.applications.densenet.DenseNet121(include_top=False,
                                            weights='imagenet', input_tensor=None, input_shape=None,
                                            pooling='avg', classes=1000)
x = MN.output
x = Dense(1, activation='sigmoid')(x)
model = Model(MN.input, x)
model.summary()

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns

test_steps_per_epoch = numpy.math.ceil(validation_generator.samples / validation_generator.batch_size)

predictions = model.predict_generator(validation_generator, steps=test_steps_per_epoch)
# Get most likely class
predicted_classes = numpy.argmax(predictions, axis=1)
true_classes = validation_generator.classes
class_labels = list(validation_generator.class_indices.keys())
report = classification_report(true_classes, predicted_classes, target_names=class_labels)
print(report)

cm=confusion_matrix(true_classes,predicted_classes)

sns.heatmap(cm, annot=True)

print(cm)

plt.show()

and got this error

2019-12-09 12:55:02.163825: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Traceback (most recent call last):
  File "C:/Users/Acer/PycharmProjects/condas/VGG16.py", line 36, in <module>
    x = Dense(1, activation='sigmoid')(x)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\backend\tensorflow_backend.py", line 75, in symbolic_fn_wrapper
    return func(*args, **kwargs)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\engine\base_layer.py", line 475, in __call__
    previous_mask = _collect_previous_mask(inputs)
  File "C:\Users\Acer\Anaconda3\envs\condas\lib\site-packages\keras\engine\base_layer.py", line 1441, in _collect_previous_mask
    mask = node.output_masks[tensor_index]
AttributeError: 'Node' object has no attribute 'output_masks'

Process finished with exit code 1

im using python 3.6

Answer 1

There is an issue on GitHub regarding this subject GitHub Keras 10907

In the posts there is something about tensorflow and keras relation:

I had a similar issue, but with different architecture. As people suggested, it's important not to mix keras with tensorflow.keras, so try swapping

import image from keras.models
import Model from keras.layers 
import Dense, GlobalAveragePooling2D
from keras import backend as K 

to:

from tensorflow.keras.preprocessing import image 
from tensorflow.keras.models import Model from tensorflow.keras.layers
import Dense, GlobalAveragePooling2D 
from tensorflow.keras import backend as K 

Also make sure, you don't use keras.something inside your code (not only imports) as well, hope it helps : ) Also, I used Keras 2.2.4 with tensorflow 1.10.0