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Keras Unet + VGG16 predictions are all the same


By : sk055
Date : October 18 2020, 06:10 PM
fixed the issue. Will look into that further The problem is with the VGG frozen layers. Maybe you should train the entire model end-to-end if your dataset is quite different from imagenet. Also, apparently, if you freeze BatchNormalization layers, they might act weirdly. For reference, see this discussion.
code :


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Using Keras with Tensorflow as backend to train cifar10 using vgg16.py from keras


By : Ken_g6
Date : March 29 2020, 07:55 AM
To fix the issue you can do What I found after trying different configurations is that VGG16 architecture is too big for an image of size 32x32.I tried to use VGG16 until block3_pool and then added a dense 512 fully_connected followed by softmax classifier for10 classes. Below is modified code:
code :
base_model = VGG16(weights=None, include_top=False, 
             input_shape=X_train.shape[1:], classes=10)
x = base_model.get_layer('block3_pool').output
x = Flatten(name='Flatten')(x)
x = Dense(512, activation='relu', name='fc1')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(input=base_model.input, output=predictions)

Keras CIFAR10 finetuning on VGG16: How can I preprocess the input data to fit the VGG16 network?


By : L.Athan
Date : March 29 2020, 07:55 AM
help you fix your problem You can simply set the input_shape to a dimension of your choosing.
Be aware that you very likely will get inferior results since VGG16 expects at least 48x48px. Quoting from Keras documentation:

Still downloading even Keras has the VGG16 pretrained model in ./keras/models


By : Ilya Putilin
Date : March 29 2020, 07:55 AM
wish help you to fix your issue The default value of include_top parameter in VGG16 function is True. This means if you want to use a full layer pre-trained VGG network (with fully connected parts) you need to download vgg16_weights_tf_dim_ordering_tf_kernels.h5 file, not vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5.

You must feed value for placeholder *_sample_weights while training UNET from VGG16


By : user1638674
Date : March 29 2020, 07:55 AM
Hope that helps The problem was with DataGenerator.getitem(): resize does not return a new numpy array. It changes the original array and returns nothing. Therefore the getitem method returned None, None. The keras error messages is misleading.

Keras VGG16 preprocess_input modes


By : Vijay Saravate
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further The mode here is not about the backend, but rather about on what framework the model was trained on and ported from. In the keras link to VGG16, it is stated that:
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